The Zycus and Hackett Group research on Shadow Agent Estates doesn’t begin with a customer success story or a market opportunity framing. It begins with Zycus admitting that by early 2024, its own marketing team could not produce an accurate list of every AI agent running in the company’s name. Couldn’t tell you what each one did, who owned it, what it could access, or what it cost to keep running. A company whose entire value proposition is helping enterprises govern autonomous procurement had, in its own words, accumulated a Shadow Agent Estate inside its own walls. 

That admission is doing a lot of work. It’s saying: this doesn’t happen to careless organisations. It happens to organisations that move fast in an environment where the technology makes deployment easier than governance by design. 

Following Zycus Horizon events in Europe and ANZ last year, it was discovered that agent debt is real, it’s growing, and the procurement community hasn’t yet developed the vocabulary to name it clearly enough to fix it. 

What agent debt actually is 

Agent debt is the liability side of the Shadow Agent Estate ledger. 

The Shadow Agent Estate is the asset: the full population of AI agents running across your enterprise that nobody has inventoried, owned, or governed. Not because anyone was reckless. Because every route to building or acquiring agents defaults to isolation unless someone deliberately fights against it. Teams build agents without connecting them to outcome flows. Vendors ship agents natively inside tools you already pay for. Orchestration layers get assembled from scripts owned by one person. Agent builders put agent creation in the hands of anyone, at any time, with no governance attached. 

The Shadow Agent Estate is what accumulates. Agent debt is what that accumulation generates. 

The Zycus–Hackett research maps agent debt across four ledgers. Ownership debt is what you carry when no one can name the human accountable for what an agent does. Access debt is what you carry when agents have permissions broader than any task they currently perform. Entanglement debt is what you carry when agents depend silently on each other in ways that nobody has documented. Operational debt is what you carry when you cannot state what your agents cost last month, and have no audit trail to reconstruct what any specific agent did on a specific day. 

The numbers from Hackett make the case 

This isn’t an abstract risk. The Hackett Group’s 2026 research quantified where the exposure sits. 

It was found that 69% of companies expected additional material risk from agentic AI deployment versus traditional automation, according to the 2026 Future-Ready Purchase-to-Pay Quick Poll. That is not a fringe concern. It is the majority view. 

Amy Hillcox, Senior Research Director, Procurement Applied Intelligence at The Hackett Group®, outlined in the research the specific risks most weighing on procurement leaders. Internal adoption and trust risk was cited by 84% of respondents. Incorrect or inappropriate autonomous decisions by 74%. Data quality risk by 72%. These are not technology risks in the narrow sense. They are governance risks. 

The mechanism that produces these risks is captured in a data point from the 2026 North American Procurement Applied Intelligence Leadership Forum: only 17% of attendees reported having moderate or advanced experience building AI agents. (Note: this polling was conducted at a Zycus Horizon event, a self-selected audience of procurement professionals who attended a vendor conference, and should be read in that context rather than as a representative sample of the broader procurement market.) 83% are configuring agents with limited expertise, using tools that produce standalone task executors by default. 

And then there is the governance gap at the provider level. The Hackett Group’s 2026 AI Solution Providers Study found that 50% of procurement technology providers do not fully support the governance requirements needed for effective AI. The market for agent tooling outpaced the market for agent governance before either had a clear vocabulary. 

What was heard at Horizon Europe and Horizon ANZ 

The Zycus Horizon events in 2025 were where the practical dimension of this became concrete. 

At Horizon ANZ, Seqwater presented their experience implementing Zycus’s Merlin Agentic Platform. What came through clearly wasn’t a story about technology deployment. It was a story about what it meant to have agents operating inside governed flows rather than alongside processes. When agents are embedded in the flow, the outcome has a structure. Someone owns it. The cost is visible. There is a record of what happened. 

BOLT’s presentation at Horizon Europe told a similar story from a different starting point. The distinction they kept coming back to was between having AI capability and having AI that produces procurement outcomes. Point agents, in their experience, produced capability. Governed agentic flows produced outcomes that procurement could stand behind in front of the CFO. 

That last phrase matters. Standing behind an outcome in front of the CFO is the test Agent Debt fails structurally. You cannot make that case with isolated agents. You can only make it with governed flows that have auditable results, named owners, and measurable outputs tied to procurement’s core accountabilities. 

The four failure modes most likely to surface first 

The Accountability Gap is when an agent can send a communication, approve a transaction, or publish content with no named human answerable for the outcome. It tends to produce an incident: something gets sent or approved that shouldn’t have been, and when the question of who is responsible is asked, there is no answer. 

Credential Sprawl shows up in security reviews and offboarding processes. API keys and tokens scatter across scripts and automations, unrotated, uncounted. When someone with significant agent-building history leaves an organisation, the credential audit that follows is frequently the first time the access footprint of the agent population becomes visible. 

The Pipeline Jungle becomes apparent when something upstream breaks. An agent that silently depends on the output of another agent creates a fragile dependency that nobody has mapped. When the upstream agent changes, updates, or is retired, the downstream failure is often a surprise. 

Unmetered Spend surfaces in budget conversations. Finance asks what the AI programme cost last month. Procurement cannot produce a single number. This failure mode most directly undermines the board-level ROI case. 

The research is precise on why these failure modes accumulate structurally. Isolation is not a risk of the point agent model. It is the default output of the point agent model. Creation is easy. Governance never arrives. 

The urgency argument that changes the calculus 

The case for addressing agent debt now rather than later rests on a dynamic that Zycus and The Hackett Group describe as ‘the inversion’. 

Procurement’s day-to-day work is shifting. Today, agentic flows cover intake and tail spend. Zycus and The Hackett Group project that over the next 12 to 18 months, they expand into sourcing, contracts, supplier onboarding, and AP. The end state is end-to-end procurement running in flows, with the S2P suite as the system of record underneath. Organisations like BOLT and Seqwater are already in it. 

The reason this matters for agent debt is that shadow estates do not shrink as flows expand. They compound. Every new flow layered on top of an ungoverned point-agent population makes the estate harder and more expensive to unwind. Organisations that govern their estates before the inversion completes enter the flow era with an architecture. Those that don’t enter with an accumulation. 

Richard Gardner, Senior Director of Market Intelligence at The Hackett Group®  observed in the research that organisations have always struggled to manage IT complexity as they scale, through redundant software, acquired legacy systems, and customisations that outlive the problems they solved. The same complexity pattern, he argued, is highly likely to emerge for companies building agent populations without the data maturity, governance structures, and talent to manage what accumulates. 

We named this pattern when it was technical debt. We named it when it was data debt. The procurement community needs to name it now, while the estates are still small enough to inventory by hand. 

What closing the ledger requires 

The research identifies five things that need to be attached to every agent at the moment it is created. 

An owner: a named person who answers for what the agent does. A scope: least-privilege access granted for the task at hand, not extended by default. A meter: cost attribution visible to finance. A log: a record complete enough to reconstruct what the agent did on any given day. A kill switch: one place to stop it immediately when something goes wrong. 

These five disciplines are not technically complex. The question is whether the platform architecture enforces them at creation or leaves them to whoever happens to be building the agent. Most platforms leave it to the builder. That is the gap the Shadow Agent Estate grows through. 

Platforms that embed governance architecturally rather than optionally (Zycus’s Merlin Agentic Platform being the primary example cited in the research) operate on a different premise: every agent in every flow carries all five disciplines from the moment it exists. The governance is embedded in the architecture, not added after the fact. The presentations at Horizon Europe and Horizon ANZ demonstrated what that architectural difference produces in practice: procurement functions that can stand behind their AI investment in front of the CFO. 

The diagnostic question for any CPO reading this is direct. Can you produce, right now, a complete list of every agent running in your name, who owns it, what it costs, and whether it sits inside a governed outcome flow? If the answer is no, you have a Shadow Agent Estate. What you have accumulated in it is Agent Debt. And it is growing. 

The ledger is open. The question is when you look at it. 

Agent debt will not appear on any balance sheet until something goes wrong. An autonomous decision that nobody can attribute. A compliance event with no audit trail. A board question about AI ROI that procurement cannot answer. A security review that reveals an access footprint nobody mapped. 

By that point, the debt is visible because it has already been called in. 

The research ends with a line worth taking seriously: the move from a Shadow Agent Estate to a governed one is not the move from many agents to fewer. It is the move from isolated agents executing tasks to orchestrated agents delivering outcomes. That is the only move that closes the ledger. 

The organisations that make that move now, before the inversion forces the question, are the ones who will be presenting success stories at the next Horizon. The ones that wait will be presenting something else. 

The full Zycus–Hackett Group research paper on the Shadow Agent Estate is publicly available.  https://www.zycus.com/knowledge-hub/whitepapers/shadow-agent-estate-hackett-group

TL;DR 

• Agent Debt is the compounding liability generated when AI agents are deployed without governance, ownership, or connection to outcome flows. 
• It accumulates across four ledgers: Ownership (no accountable human), Access (unscoped permissions), Entanglement (undocumented dependencies), and Operational (no cost tracking or audit trail). 
• 69% of companies already expect material risk from agentic AI deployment; 50% of technology providers cannot fully support the governance requirements to manage it. 
• Shadow Agent Estates compound as agentic flows expand: ungoverned populations become harder to unwind, not easier, with every new flow layered on top. 
• The fix requires five disciplines attached to every agent at creation: owner, scope, meter, log, and kill switch. 

Editorial note: The reporting here draws on The Shadow Agent Estate (2026), a point of view paper published by Zycus in collaboration with The Hackett Group’s research. The research draws on the 2026 North American Procurement Applied Intelligence Leadership Forum, the 2026 Future-Ready Purchase-to-Pay Quick Poll, and the 2026 AI Solution Providers Study. Case material comes from presentations at Zycus Horizon Europe and Horizon ANZ 2025. 

Most procurement AI projects do not fail at deployment. They fail six months earlier, when someone decides to run a proof of concept on top of inconsistent data, with intelligence that nobody can rely on.  

It often starts with a compelling GenAI demo. Real enthusiasm from the business. A pilot that produces output that looks useful. Then a slow stall, as the team realises they cannot access clean, connected data.  

The key challenge with procurement automation  

Procurement is one of the hardest functions to automate well, and I think the industry underestimates this. Supplier negotiation is a high-stakes, trust-based process. The outcome depends not just on price, but on relationship history, risk exposure, and commercial intent built up over years. The moment you cannot explain how a recommendation was reached, or who is accountable for acting on it, you have a commercial legal and compliance problem.  

The data problem sits underneath all of it. Most large organisations use multiple enterprise resource planning tools (ERPs), with supplier, contract, and performance data scattered across different systems and owned by different teams that use different definitions and terms. Often data from one system gets ‘processed’ in another system also. Making traceability to its source, or defining the ‘authoritative datasource’ for each datapoint a cumbersome exercise. Yet AI cannot make reliable recommendations from inconsistent sources. And connecting the signals across supplier risk, contract terms, performance history, and commercial intent into something coherent is not a simple task either. It requires continuous investment in a single version of the truth. Without it, even the most sophisticated AI model won’t be able to deliver tangible improvements.  

Intelligence nobody can act on  

If the underlying data is inconsistent, procurement contracts will have gaps, and AI recommendations and automated actions will be unreliable. The result is wasted investment in intelligence no one can use.  

The fix is not more AI. It is better data. This requires one version of the truth (the authoritative source) for supplier master data, contracts, performance, and risk as well as clear decision-making roles and responsibilities. It is also essential to establish strong governance policies that empower teams to act with confidence, rather than constantly going back and forth for approval. None of this is glamorous work. It does not demo well. But without it, even a well-built AI model is guessing without ‘understanding’ the context of the organisation and its suppliers.  

Start small and move quickly  

In 2026, Excel and email cannot be the backbone of procurement. This does not mean procurement should wait for a perfect technology program to materialise. It means being clear-eyed about what you are actually trying to do.  

The pilots I have seen work well are not the most ambitious ones. They pick one specific, high-impact use case. They invest in the data foundations first. They define who owns each decision and what the guardrails are. And they run the ideate, test, and iterate cycles in days, not weeks or months. Traditional enterprise delivery, where you define requirements for six months and then run a long proof of concept, does not work in this environment. The conditions keep changing, so a program that arrives twelve months later, fully formed, is usually solving last year’s problem.  

Autonomous negotiation is advancing, but only for the right use cases  

Another area where we are seeing a lot of experimentation in is autonomous contract negotiation. AI could add value here, but only for the right use cases. Real automation is within reach only for transactional, commoditised spend. High-volume, rules-based negotiations can run end-to-end within clear guardrails, with humans stepping in for managing exceptions. We are testing this and it is delivering results. But the further you move toward complex, strategic relationships, the more decisions depend on judgment that no AI model handles well yet. Treating both categories as the same problem could result in misplaced investment and poor outcomes.  

The bigger prize is speed, not autonomy  

There is a more immediate role for AI that gets less attention than the autonomous negotiation conversation and it is where more of the value sits right now.  

Teams are no longer just asking whether a product is provided at the optimal cost. They are asking whether a supplier is resilient, and what plan B looks like if they are no longer able to fulfil their obligations due to disruption. That due diligence takes time that procurement does not always have. When you can connect signals across suppliers, contracts, inventory, and logistics in real time, and turn them into clear recommended actions, you make decisions faster and with more confidence.  

AI that identifies leverage you would not have spotted manually, or flags supplier stress early enough to act on it, is delivering the biggest value right now. It helps create more resilient supply chains.

Specialist AI tools can also play a key role in accelerating supply chain decarbonisation. AI, combined with digitisation, allows organisations to better track and manage carbon footprint across their supply chains, including scope 3 emissions.  The ability to view the carbon footprint across your supplier network and make informed decisions about strategic partnership or how to support suppliers in advancing their own net zero strategies is key for enabling greener supply chains. 

Getting the foundations right

But none of these benefits can be realised without getting the foundations of right. Gartner says only 29% of supply chain organisations have built the capabilities needed for future AI performance. It is a data, governance, and operating model gap. Procurement leaders who deploy AI onto fragmented foundations will keep running impressive pilots that go nowhere.  

Readiness looks different from the outside than it does from the inside. From the outside, it looks like a technology programme. From the inside, it is mostly organisational work: getting teams to agree on what a supplier record should contain, who signs off on a recommended action, and how you know whether the tool changed anything.  

Data quality. Process standardisation. Governance. None of it is optional. If procurement leaders want AI to move beyond the pilot phase, they need foundations strong enough for teams to trust the outputs. Get that right, and AI stops being an interesting experiment and becomes a practical advantage driving faster decisions, stronger resilience, and measurable impact at scale.

It is the precursor to the live Agentic AI Procurement Masterclass, which Zycus will host this July with ISM (Americas), CIPS (EMEA) and PASA (Australia/APAC), grounded in research from The Hackett Group.

As vendors race to advertise ever-larger libraries of AI agents, most procurement teams still cannot separate genuine agentic capability from marketing claims, or move pilots into governed, enterprise-scale use. Both programs tackle that gap with education first – not a product pitch.

Part 1 – Watch Now: The On-Demand Field Guide (Complimentary)

The Field Guide is a seven-module, self-paced video series, watchable on its core path in about 35-40 minutes. It moves from the vocabulary of AI to the architecture decisions that determine whether AI delivers value in procurement – covering how orchestrated agentic workflows differ from isolated point agents, why autonomy needs governance, and the concept of Agent Debt: the compounding cost of deploying agents without structure. A practical example runs throughout, such as turning a vague intake request into a sourcing-ready event, then into a governed supplier negotiation. Interactive knowledge checks and downloadable worksheets let leaders apply it to their own workflows. No cost, no barrier to entry.

Part 2 – Register Now: The Live Masterclass (Complimentary, This July)

The live Masterclass takes leaders from understanding to application across three sessions, run region by region alongside each region’s authoritative procurement body and local experts:

• Session 1 – What agentic AI really is, and what it does in procurement (Chris Sawchuk, Principle Analyst, The Hackett Group)
• Session 2 – How point agents differ from agentic AI (Chris Sawchuk, Principle Analyst, The Hackett Group)
• Session 3 – Why DIY and studio-based solutions create Agent Debt (Omid Ghamami, Procurement Educator and Thought Leader)

Hosted with ISM (Americas), CIPS (EMEA) and PASA (Australia/APAC). Americas attendees can earn 2 CEH credits accredited by ISM, Australia/APAC attendees can earn 1 CPD credits accredited by PASA – and benchmark their own AI maturity against global peers.

Agentic AI will not reward organisations that adopt it blindly. It rewards those who understand it deeply enough to govern it. We built the Field Guide to raise the floor of understanding across the profession, so every procurement leader walks in informed.

– Aatish Dedhia, Founder and CEO, Zycus

Our community wants clarity on what agentic AI means for procurement. Working with experts, like Zycus, provides a research-grounded, education-first perspective that adds value. 

– Melanie Larimer, VP, Member Experience & Learning, Institute of Supply Management (ISM)

How to Take Part

• Watch the Field Guide now – complimentary and on-demand. Build your foundation today: Zycus Agentic AI Field Guide For Procurement.
• Register for the live Masterclass – complimentary, this July. Choose your region, reserve a seat, and earn accreditation credits while benchmarking your AI maturity. Seats per regional session are limited: Zycus Agentic AI Procurement Masterclass.

Frequently Asked Questions

What is the Agentic AI Field Guide for Procurement?

A free, on-demand video series from Zycus that gives procurement professionals a vendor-neutral education in agentic AI, including interactive knowledge checks and downloadable worksheets. It is the precursor to the live Masterclass.

Who is hosting the Masterclass, and when?

Zycus hosts the live Masterclass this July, region by region, with ISM (Americas), CIPS (EMEA) and PASA (Australia/APAC), and research from The Hackett Group. Dates are confirmed regionally.

How much does it cost, and is there accreditation?

Both programs are free. Registration is required only for the live sessions. Americas attendees can earn 2 CEH credits accredited by ISM; Australia/APAC attendees can earn 1 CPD credits accredited by PASA.

What does “Intake-to-Outcomes” mean?

Intake-to-Outcomes (I2O) is the category framing Zycus uses to redefine procurement beyond Source-to-Pay (S2P): a unified platform combining AI-guided intake, an agentic execution layer, and an integrated S2P core to deliver measurable outcomes, not just transactions.

Rather than managing many AI agents atop fragmented data and disjointed systems, IVA Studio enables procurement to perform any activity across the Source-to-Pay (S2P) process through a single agent, IVA, that knows your data, follows your rules, and gets work done on day one. IVA Studio is the AI control tower managing IVA’s skills, permissions, tools, and MCP integrations.

Procurement can operate with greater speed and intelligence with IVA. A category manager preparing to re-source an expiring contract can ask IVA to pull the contract, benchmark against agreements, identify stronger suppliers, set up an RFx, and launch the event, all in a single conversation. An Accounts Payable team can have IVA validate incoming invoices against not just price and PO, but also against the harder-to-enforce contract terms buried in documents. 

“At Ivalua, we believe the future of procurement is not AI, it’s people using AI to deliver better and faster results. IVA, powered by IVA Studio, is a decisive step forward into a new era for procurement, one full of disruptive challenges and exciting opportunities. With IVA, procurement teams can focus on what they do best: building strategies and relationships, internally and externally. We are thrilled to lead this journey with our customers and partners,” said Franck Lheureux, CEO of Ivalua.

IVA also works autonomously, in workflows, and in the background. A supplier risk event can trigger IVA to surface every affected contract, open a PO, create a mitigation plan, and propose qualified alternative suppliers. IVA can do anything a user can and more, because the entire Ivalua Source-to-Pay application is its knowledge source and toolset.

A complete agentic operating system for procurement 

Procurement teams gain a truly intelligent, intuitive experience – they simply tell IVA what to do, and IVA assembles the right skills, tools, and knowledge for the task. For complex work, IVA creates and orchestrates temporary sub-agents.

This is the first complete S2P agent to follow the skills-based architecture frontier AI labs are converging on. Users do not need to build, orchestrate, or select from a growing catalog of agents. Instead, IVA understands all the data in the Ivalua S2P platform, can take any action as a tool, and follows organization-specific best practices through skills, with human-in-the-loop controls maintained.

Ivalua’s AI technology and services combine to ensure successful implementation, adoption and continuous optimization. IVA delivers value immediately because it can execute any action a user can from day one. Unlike competing solutions, there is nothing to integrate or configure. Over time, IVA gets smarter as organizations use it, encoding their expertise in skills and memory. And as teams grow to trust it, IVA can take on more autonomous work — embedded in workflows or running in the background — turning Source-to-Pay into an automation engine. Additionally, Ivalua and its implementation partners provide guidance, expertise and support services.

“Procurement teams have typically had to spend time building and configuring AI agents before seeing any results. Ivalua’s approach is entirely different: IVA accesses the Ivalua platform as its toolset and source of knowledge, so procurement can start getting value from day one, within a framework of governance that’s enforced by design,” said David Khuat-Duy, Founder & Chief AI Officer at Ivalua.

Governed by design

Governance is enforced at the platform level, not by the agent. IVA inherits the permissions of the user who invokes it and cannot exceed them. When IVA is operating autonomously in a workflow, it still has an accountable user whose boundary it follows and who it falls back on for human-in-the-loop controls.  Every action is logged with a continuous audit trail. AI works only where it is allowed to work, and procurement leaders can prove it.

Expertise that compounds

IVA learns employee best practices: sourcing approaches, negotiation strategies, and compliance rules as skills that IVA follows. Skills turn individual expertise into team capabilities, building institutional knowledge that compounds with every interaction. A new hire working with IVA operates with the benefit of the whole organization’s experience. IVA also maintains memory of interactions, self-improving automatically. 

Fits your AI ecosystem

IVA Studio is the AI control tower to manage the skills IVA uses, the tools it can call, the external systems it connects to through MCP, and the LLMs it runs on. IVA is LLM-agnostic, customers can use the models Ivalua provides out of the box or bring their own. With IVA Studio, IT teams, partners, and customers can build or tailor what IVA can do and how it connects.

IVA Studio is now available in Beta and will be generally available in summer. To learn more about IVA, powered by IVA Studio, watch this webinar.

Launched from the main stage at DPW NYC, MAX is the first product to close the gap between intelligence and decisive action, providing a unified view across cost, risk, and ESG.

Why procurement needs a new operating system

The Chief Procurement Officer mandate has expanded faster than the function’s operating model. Procurement leaders are now accountable for resilience, ESG exposure, tariff response, margin protection, and more, often with the same or fewer resources than they had five years ago. This environment introduces three critical challenges:

• Velocity: Supply markets move in hours, not quarters, yet category strategies update infrequently. 
• Fragmentation: The profusion of procuretech in recent years has armed procurement teams with tools that provide insights, and others that help execute, without a connection.
• The Missing Link: What has been missing is an intelligent system that bridges the two. 

Continuous intelligence, applied in context

MAX closes that gap. Built on a neurosymbolic framework that leverages best-of-breed agentic AI, MAX sits as a connecting layer between data and execution systems. It continuously combines Beroe’s global market intelligence and specialist third-party data via a data marketplace, with Kearney’s proprietary consulting methodology, benchmarks, and decision frameworks, applied against a user’s own spend, contracts, and supplier base. The result is recommendations surfaced in the organization’s specific context, as soon as opportunities arise. When a tariff is imposed, a commodity price spikes, or a supplier risk rating shifts, MAX reassesses affected categories and flags the decisions that need attention before teams have to go looking for them.

Built with procurement, for procurement

MAX results from a multi-year partnership between Kearney and Beroe which set out to enable previously impossible decisions and outcomes in procurement. The product was designed with a Strategic Advisory Council of 13 global procurement organizations, representing some of the world’s most complex supply chains, who shaped the product design, validated its application across categories, and continue to steer its roadmap today. Their involvement ensures that MAX reflects the real decisions procurement teams face, not a theoretical model of what those decisions should look like.

Vel Dhinagaravel, Founder and CEO, Beroe, said: “The processes procurement relies on today were built for a world that no longer exists. Teams have always had access to market data, but what they have lacked is a system that connects that data to their specific spend, applies procurement logic, and tells them where to act, continuously. MAX puts that capability into the hands of every category manager, not just the ones in the room with a consultant, enabling decisions and outcomes that were previously impossible.”

Suketu Gandhi, Co-leader of Global Strategic Operations, Kearney, commented: “CPOs are being asked to deliver competitive advantage in markets that move in hours, not quarters. MAX gives procurement leaders the real-time visibility and prioritized recommendations they need to anticipate shifts, focus where it matters, and move decisively. This is not a faster version of the function procurement has always been. It is a different function, one that stops waiting to be asked. It moves from responding to anticipating, from episodic to continuous. That is what it means to compete.”

A CPO member of the Strategic Advisory Council added: “Today, category managers are forced to be selective; focusing on the top 20 percent of spend, the most strategic suppliers, and the categories with the largest potential savings. Smaller opportunities go unrecognized because the analytical cost outweighs the return. MAX changes the economics of that decision. Category managers can now cover every supplier, every category, every day.”

MAX is being unveiled today at Digital Procurement World (DPW) NYC – one of the largest procurement conferences in the US – and will be available to select customers over the coming months.

Earlier this month, Uber admitted it had exhausted its entire 2026 AI budget by April, after usage among its 5,000-strong engineering team climbed from 32% to 84% in just one month. Meanwhile. Microsoft is urgently cancelling most of its non-GitHub AI licences by 30 June 2026, after token-based billing made its costs unsustainable.  

Accessible through Vertice’s award-winning intelligent procurement platform, AI Cost Optimization creates a unified view of spend, usage and commitments across all AI providers, with automated projections, pre-emptive alerts and intelligent cost control recommendations.

Vertice’s analysis of $75bn+ spend across 250k+ negotiated contracts – the largest global dataset of software pricing – has revealed that in just the last 12 months, the proportion of SaaS vendors offering seat-based pricing plans has plummeted from approximately half to a third – with the remaining two-thirds of software vendors’ plans now either consumption-based or hybrid.  

Vertice’s analysis has also revealed that consumption-based pricing: 

• Offers less negotiation leverage, resulting in 42% lower discounts versus seat-based models. 
• Costs on average 37% more per user than seat-based pricing.
• Is 3X more likely to incur overage charges than seat-based models.
• Typically exceeds budgets by nearly 40%, versus just 5% for seat-based.

“Consumption-based pricing is a perfect storm for finance, procurement and IT teams,” said Eldar Tuvey, Founder and CEO, Vertice. “AI usage has driven demand to unprecedented levels, creating an AI arms race in every SaaS vertical. The economics of AI infrastructure have meant that pricing models have radically changed too. Which means as companies’ boards demand AI adoption and encourage experimentation, usage is skyrocketing – and so are costs.”

Vertice’s AI Cost Optimization tool has been designed to solve this exact problem for finance, procurement and IT teams. 

• All AI usage and spend in a single dashboard, broken down by department, vendor and even individual AI models, showing the internal teams and applications consuming the most tokens and driving the highest spend.
• Granular usage views, showing token usage, requests and completed prompts.
• Budget utilization rates and 90-day usage and cost forecasts based on recent usage patterns.
• Usage versus commitment trackers for every vendor, with commitment thresholds extracted automatically from contracts.
• Cost optimization recommendations, based on the individual business’s usage patterns and needs, the contracts’ terms, and Vertice’s real-time dataset of 32,000+ SaaS vendors’ pricing data, commercial terms and negotiation intelligence.  
• Customizable alerts to flag risk of upcoming threshold breaches, such as usage surges or commitment breaches. 

“Traditional cost optimization tools were designed to manage seat-based spend,” added Tuvey. “But this doesn’t hold up when you are paying by the task or token, and your usage shifts every day. Our AI Cost Optimization not only helps teams control and predict their spend, but also creates an accurate usage precedent to inform future purchases, internal token allocations and usage policies.”

Vertice’s AI Cost Optimization tool is fully integrated within the Vertice Intake-to-Procure platform, from which renewals, contracts and approval workflows are also managed. This allows finance, procurement and IT teams to act immediately when usage is approaching a threshold, ranging from accessing the contracts’ terms and triggering a renegotiation workflow to reallocating usage. 

“We are giving finance, procurement and IT teams the control they need,” continued Tuvey. “For years, buyers have lacked the pricing benchmarks and vendor intelligence to negotiate the best price and terms with vendors. To now add unpredictable AI pricing models on top just compounds the imbalance. Our AI Cost Optimization is the next step in Vertice’s mission to level the playing field.”

Built as the first global, research-anchored gathering dedicated to agentic AI in procurement, the summit drew more than 1,500 senior procurement leaders from over 50 countries across the Americas, EMEA, and APAC – and gave the function its sharpest frame yet for what to own, what to delegate, and what to design with intent.

Every session, including the founder’s keynote, analyst premieres, partner conversations, and live Merlin platform walkthroughs, is now available on demand via the Zycus summit hub, the Zycus LinkedIn page, and the Zycus YouTube channel.

Two research premieres that reframe the conversation

The summit unveiled two major studies from two of the most cited names in the analyst community. Both are now in the hands of practitioners worldwide:

Forrester – Don’t Delegate AI. Presented by Jeffrey Rajamani, Senior Analyst, Forrester, the report’s argument is uncompromising: procurement’s AI strategy is not an IT project, and it is not a vendor’s job. The CPO has to own it – set the direction, define the guardrails, and own the outcomes.

The Hackett Group – Agentic AI in Procurement Adoption Index 2026.  Presented by Chris Sawchuk, Principal Analyst, The Hackett Group, drawing on more than 250 global CPO conversations, this is the cleanest read yet on where procurement actually sits on the agentic AI curve – what’s working, what’s stuck, and how far the typical organisation still has to travel from pilot to enterprise scale.

One data point cut through the rest of the agenda: 58% of procurement leaders expect agentic AI to make a material dent on their organisations within twelve months — yet the bulk of work remains stranded in pilot mode, never quite breaking through to enterprise-wide deployment. That single tension shaped much of the day’s discussion, with CPOs openly contesting where ownership of the AI agenda must live — and what gets lost when it lives elsewhere.

A keynote on designed autonomy

The summit’s anchor was a keynote from Aatish Dedhia, Founder and CEO of Zycus, who reframed agentic AI as something larger than the next wave of automation.

His message – that agentic AI represents a structural change in how procurement work is orchestrated, governed, and trusted, rather than a faster version of yesterday’s automation – set the tone for the analyst and practitioner sessions that followed.

Strategy met execution on the same stage

The summit didn’t stop at the whiteboard. Attendees were taken inside live, production-grade demonstrations of the Zycus Merlin Agentic AI Platform, showcasing goal-driven agents already operating inside enterprise procurement workflows:

· Autonomous Negotiation Agent (ANA) – running supplier negotiations end to end with minimal human touch.

· Merlin Intake – orchestrating intake-to-pay across the full procurement lifecycle, guided by AI.

· Merlin Analytics – surfacing spend intelligence at enterprise scale.

The agenda was deliberately built to mirror the full stack required to move from idea to operating reality:

· Analyst sessions from Forrester and The Hackett Group, grounding strategy in evidence.

· Consulting perspectives from EY and IBM on governance, readiness, and operating-model design.

· Practitioner-led conversations on the unglamorous work of moving from pilot to production without losing control or accountability.

Attendees scored the event 4.5 out of 5 overall, calling out the practical orientation of the sessions and the relevance of the live demonstrations to real procurement problems.

Why it mattered

This wasn’t another AI conference. The Agentic AI Procurement Summit 2026 was structured as a decision forum for procurement leaders working through three questions:

· How does our agentic AI maturity compare to global peers?

· Which AI decisions cannot leave the CPO’s desk?

· How do we close the gap between experimentation and enterprise impact?

With procurement caught between assisted automation and genuine autonomy, the summit offered clarity on three fronts — what to own, what to delegate, and what to design deliberately. The conversations it sparked are expected to shape boardroom AI agendas across procurement functions in the year ahead.

Access the research and on-demand sessions

The Forrester Don’t Delegate AI report, The Hackett Group’s Agentic AI in Procurement Adoption Index 2026, and the full library of session recordings are available now:

· Zycus Agentic AI Procurement Summit on-demand page: https://www.zycus.com/campaigns/agentic-ai-procurement-summit-2026-on-demand/?utm_source=cpostrategy&utm_medium=pr&utm_campaign=aps_2026

· Zycus on LinkedIn: https://www.linkedin.com/company/zycus

· Zycus on YouTube: https://www.youtube.com/@Zycus

Frequently asked questions

What was the Agentic AI Procurement Summit 2026? A virtual global forum hosted by Zycus on May 13, 2026, focused exclusively on how autonomous AI should be designed, governed, and scaled by procurement leaders. It brought together more than 1,500 procurement leaders from over 50 countries across the Americas, EMEA, and APAC.

Who hosted it? Zycus – the global pioneer of Agentic AI in procurement and creator of the Merlin Agentic AI Platform.

Which research reports were released? Forrester’s Don’t Delegate AI, which argues that procurement’s AI agenda must sit with the CPO; and The Hackett Group’s Agentic AI in Procurement Adoption Index 2026, drawn from interviews with more than 250 global CPOs.

What is the Merlin Agentic AI Platform? Zycus’s unified agentic AI platform for procurement, featuring goal-driven autonomous agents including Merlin Intake (intake-to-pay orchestration), the Autonomous Negotiation Agent (ANA) for supplier negotiations, and Merlin Analytics for spend intelligence.

Where can I watch the replays? Recordings, keynotes, and live demonstrations are available via Zycus Agentic AI Procurement Summit on-demand page, the official Zycus LinkedIn page, and the Zycus YouTube channel.

What does Intake-to-Outcomes mean? Intake-to-Outcomes (I2O) is Zycus’s category framing – a step beyond traditional Source-to-Pay (S2P). It describes a unified platform that combines AI-guided intake, an agentic execution layer, and an integrated S2P core, focused on measurable business outcomes rather than transactions.

About Zycus

Zycus is the global pioneer of agentic AI in procurement, redefining the category from Source-to-Pay (S2P) to Intake-to-Outcomes (I2O). Its unified platform combines AI-guided intake, an agentic execution layer, and a deeply integrated S2P core to deliver measurable business outcomes – not just transactions.

At the heart of the platform is the Merlin Agentic AI Platform, featuring autonomous, goal-driven agents such as Merlin Intake, the Autonomous Negotiation Agent (ANA), and Merlin Analytics.

Zycus is consistently recognised as a market leader across Gartner®, Forrester™, IDC, and Gartner Peer Insights, and is trusted by enterprise procurement leaders worldwide.

With this acquisition, Coupa now stands alone in the ability to offer fully integrated Agentic-as-a-Service capabilities powering agentic workflow automation and execution to its global network of more than 3,500 buyers and 10m suppliers. The move accelerates Coupa’s ambition to power proprietary, two-sided, autonomous workflow automation for global buyers and suppliers.

“The acquisition of Tonkean is game-changing for Coupa and the market. For 20 years, we’ve built a global trade network that brings together buyers and sellers for the purpose of optimising trade and spend,” said Leagh Turner, Coupa CEO. “To date, we’ve delivered more than $300 billion of value to that network and amassed a dataset of $10 trillion. To deliver the promise of increasingly autonomous execution of transactions between buyers and suppliers, you need three main things, industrial-grade and unified orchestration, best-in-class buyer and supplier workflows, and clean, trusted data. With the acquisitions of Rossum and Tonkean, in short order, we have now amassed all the assets to make this promise possible for both buyers and suppliers at a time when buying and selling is getting more complex and costly. We couldn’t be more excited to welcome Sagi and the Tonkean team to Coupa.”

Tonkean is the fourth strategic acquisition in Coupa’s vision to build the world’s foremost agentic trade network, following Cirtuo, Scoutbee, and Rossum. Together with Coupa’s leading platform capabilities, they create a unified capability to deliver agentic trade and autonomous spend management that Coupa is uniquely positioned in the market to deliver. 

“This is a deliberate, strategic step toward building the number one agentic trade network and delivering autonomous spend management at speed and scale like no one else in the market,” said Salvatore Lombardo, Chief Product and Technology Officer of Coupa. “We’ve assembled the ultimate procurement toolkit: a $10 trillion data foundation, eyes to read any document, intelligence to source smarter, and now the ultimate engine to orchestrate modern, agentic work. Tonkean brings a 100% no-code process builder and 250+ native connectors that wrap seamlessly around customers’ existing architecture. This delivers advanced orchestration and unified integrations on day one with zero rip-and-replace. With Tonkean natively embedded in Coupa, customers get best-in-class orchestration and the best spend platform they already trust, in a single, unified agentic architecture.”

Delivering AI-driven workflow automation and execution with an advanced framework for multi-agent orchestration

Tonkean brings a natural language interface that allows users to submit and track requests – for an increase of 2.2x in user adoption – and an advanced framework for multi-agent orchestration and Agent-to-Agent (A2A) coordination. Together they execute complex workflows and replace manual handoffs, reducing cycle times by 50% and saving operations teams over 30 hours per week. Additionally, suppliers benefit from the unified integration with APIs that facilitate one-to-many connections for suppliers to grow their businesses faster.

“We couldn’t have created a more powerful combination in the market than joining forces with Coupa, unlocking tremendous value to customers far and wide,” said Sagi Eliyahu, Tonkean Co-Founder and CEO. “With our industrial-grade intake and orchestration now serving as the backbone and foundation for their industry-leading agentic trade network and data, we will seamlessly connect buyers, sellers, and agents in a unified way to deliver autonomous trade in a way that no other company can match, and in turn drive exponential business value for customers. This massive opportunity greatly excites my team and me, and we are thrilled to join Coupa.”

Join on June 4th for a live walkthrough of the unified Coupa and Tonkean experience. 

Check out our interview with Tonkean at DPW Amsterdam here and our Process Orchestration Playbook, sponsored by Tonkean.

On Wednesday, June 10th, AI Game Changers Club is hosting its first executive exchange in London, UK.

As AI becomes increasingly embedded in business operations and decision-making, the real competitive advantage will not come from technology alone. It will come from the quality of leadership, the ability to turn AI into measurable business performance, the strength of governance, and the judgement to know where human oversight remains essential.

The AI Game Changers Club is built differently. Founded by Emi Olausson Fourounjieva, CEO, AI strategist, and host of the Digital Transformation and AI for Humans podcast, the organisation brings together senior leaders for closed-room conversations about what it actually takes to turn AI into a strategic advantage. Not tools. Not use cases. Strategy.

What will the forum cover?

The session is structured around five strategic questions that procurement and business leaders consistently raise – but rarely explore with sufficient depth or cross-industry perspective:

• How do we turn AI investments into measurable business ROI?
• Where does AI truly drive performance, and where does it break at scale?
• What hidden inefficiencies, trade-offs, and risks are emerging as AI embeds into operations?
• How do we make sound decisions under increasing uncertainty?
• How do we preserve human accountability as intelligence scales?

These are not questions with clean answers. They are questions that benefit enormously from honest dialogue between leaders who are navigating them in real time, across different sectors and organisational contexts.

Who should attend?

The forum is designed for senior executives, board members, investors, and business leaders who are past the ‘exploring AI’ stage and are now working through what strategic deployment actually requires – in terms of governance, culture, capability, and accountability.

If your organisation is still focused primarily on tools and isolated use cases, this is the room to be in before the gap between early movers and late adopters becomes structural.

Featured Speakers:

• Emi Olausson Fourounjieva, CEO & Founder, AI Game Changers Club; Host, Digital Transformation & AI for Humans Podcast
• Steve Hyde, CEO & Co-Founder, Push Group
• Paul Smith, TEDx Speaker, Founder of the SOSTAC® Planning System, Marketing Author
• Sze Wong, Mid-Atlantic Chapter Chair, Founder, Zenith Venture Studio (Virtual)
• Dr. Alex Heublein, Chief Innovation & AI Officer, Netsurit; Adjunct Professor, Clayton State University
• Rob Provenzano, 7 to 9 Figures Growth Strategist; Founder, Pioneers of the Digital Economy (Canada)
• Geoffrey Klein, TEDx Speaker, Founder & CEO, nine dots; Lecturer at The Wharton School

AI Game Changers Club: A closer look

The AI Game Changers Club has previously convened executive exchanges in Stockholm, Washington DC, Luxembourg, and Zurich. The format is deliberately selective: participation by confirmation only, depth over breadth, and a speaker line-up that spans C-suite practitioners, investors, and academics — including voices from Wharton, TEDx, and the front line of AI deployment in enterprise environments.

The London chapter is chaired by Bernhard Raschke, who brings a perspective to his community that is rare in AI leadership conversations which is 30 years on both sides of the table.

Raschke spent his career inside some of the world’s most complex organisations – as a management consultant at A.T. Kearney and PwC, an executive search and talent advisory partner at Korn Ferry, and Chief Transformation Officer at RS Group (FTSE 100) – across procurement, supply chain, logistics, manufacturing, and technology sectors. That career produced one consistent observation: genuinely high-performing, aligned leadership teams are far more the exception than the rule, and no structural redesign or operating model compensates for the absence of sound judgment at the top. He now works as an executive mentor, coach, and advisor, helping leaders navigate transformation in an era where AI is fundamentally reshaping how organisations operate. He is an accredited coach, certified Hogan assessor, and a co-author in AI research since 1991.

Seats are limited and confirmed by application: https://luma.com/l4h7cj7z

AI Game Changers Club London | 10 June 2026, 17:00–21:30 BST 

Supporting more than 78,000 students across 150 schools throughout Western Australia, CEWA is modernising procurement through a digitally enabled operating model designed to balance school autonomy with stronger governance, visibility, and compliance.

As part of this transformation, CEWA selected Zycus’ end-to-end Source-to-Pay platform following an 18-month evaluation and selection process involving multiple stakeholder groups across finance, technology, and school leadership teams.

CEWA has now successfully implemented key Zycus Source-to-Contract (S2C) and Procure-to-Pay (P2P) capabilities, including Supplier Management (iSupplier), strategic sourcing (iSource), Contract Lifecycle Management (iContract), eProcurement, and eInvoicing. The deployment also integrates with CEWA’s dual ERP environment, establishing a stable digital procurement layer across the organisation.

The go-live marks a significant milestone in CEWA’s procurement transformation journey, delivering:

• Improved governance and visibility across sourcing, supplier, and purchasing activities 
• A single source of truth for supplier information and compliance management 
• Structured, scalable procurement processes designed to support both organisational control and school-level flexibility 
• Enhanced support for local, Aboriginal, and diverse supplier inclusion initiatives
• A scalable procurement foundation designed to support future AI-led orchestration and intake management capabilities 

“Procurement should enable schools – not slow them down. Our objective was to create a procurement model that delivers stronger governance and visibility while still supporting the independence and flexibility our schools need,” said Peter Davis, Team Leader, Procurement & Contracts, Catholic Education Western Australia. “Zycus gives us the platform to scale that vision across the organisation.”

As part of its next phase of transformation, CEWA plans to expand the rollout of Merlin Intake across its school network, creating a single intelligent front door for procurement requests and enabling more streamlined, AI-assisted procurement experiences for staff and stakeholders.

Future roadmap initiatives also include exploring Agentic Sourcing and Autonomous Negotiation capabilities to further automate sourcing and supplier engagement processes.

“CEWA’s transformation demonstrates how procurement can evolve into a modern, intelligent function without compromising operational flexibility,” said Aatish Dedhia, Founder and CEO, Zycus. “We are proud to support CEWA as they build a scalable, AI-enabled procurement ecosystem designed around both governance and user experience.”

About Catholic Education Western Australia (CEWA)

Catholic Education Western Australia is the state’s largest non-government education provider, supporting more than 78,000 students across 150 schools and employing over 11,000 staff throughout Western Australia. Since 1843, CEWA has delivered education services focused on community, inclusion, and long-term impact.

About Zycus

Zycus delivers procurement outcomes and not just transactions. Its leadership in Source-to-Pay is independently recognised by leading analysts, and customers worldwide.

By moving beyond Source-to-Pay to Intake-to-Outcomes, Zycus is defining the next generation of procurement. Its unified platform uniquely combines native Intake, Agentic AI, and an end-to-end S2P core in a single architecture.

The Merlin Agentic AI Platform brings this vision to life – guiding every request through Merlin Intake, unlocking hidden savings through Autonomous Negotiation, and continuously executing toward outcomes, not workflows.

Procurement is on the brink of a structural shift, one that goes far beyond the latest ‘analytics’ wave. Agentic AI is not only changing how procurement is analyzed, but how it operates. Procurement is often defined by endless back-and-forth. By closing that loop, AI agents are transforming how the function operates. These AI agents can work independently of existing systems, while also coexisting with them as digital workers. The result is a shift from a chain of manual handoffs into autonomous, auditable workflows, freeing humans to focus on policy, relationships and trade-offs. 

Unlike earlier waves of procurement technologies that supported individual tasks, agentic systems can plan, execute, follow up, and document work across systems. The result isn’t task-level automation but redesigning how procurement operates as an integrated system of action. The enterprises that see real impact will be those that deploy agents with clear governance and accountability, not as a novelty interface layered on top of the same operational bottlenecks. 

The shift is fundamentally reshaping who does the coordination work. Instead of humans orchestrating procurement functions with stakeholders, suppliers, and systems, agents can manage workflows end-to-end. Human expertise is brought in only when decisions require judgment, accountability, or trade-offs such as approving exceptions, resolving conflicts, setting policy, or making decisions that carry real business risk. The impact of this shift is visible across several core areas of procurement:

Removing manual steps in auditing, coordinating, and communicating with suppliers 

Supplier management is full of routine tasks that consume a disproportionate amount of time. Enterprises find themselves checking whether confirmations came back, reconciling mismatched line items, seeking updated ETAs, requesting missing certificates, and recording outcomes across multiple systems. Individually, these tasks seem routine; collectively, they can create significant operational drag and divert attention from higher-value work. Agentic AI can run this coordination loop end-to-end, continuously monitoring for exceptions, initiating and managing supplier communications, summarizing responses, and updating systems of record with a consistent audit trail. By shifting this work from people to agents, procurement teams move from reactive follow-ups to proactive oversight – minimizing human error, shortening cycle times, and freeing humans to focus on judgment, relationships, and strategic decision-making rather than administrative coordination. 

A clear example of this in practice is inbound inventory replenishment in large manufacturing enterprises. Replenishment is often driven by constant monitoring, manual outreach, and fragmented coordination. Agents monitor inventory and shortage signals, initiate targeted supplier outreach, and gather real-time availability and lead-time data. The agent then proposes a sourcing plan for human approval, before issuing purchase orders, tracking confirmations and shipments. When issues arise, it coordinates directly with suppliers to resolve discrepancies such as revised ETAs, partial shipments, or documentation gaps. By taking on this day-to-day coordination, agents can reduce a sourcing manager’s manual coordination and communication by up to 95%, reducing time spent on status checks, surfacing issues earlier, and ensuring consistent documentation.

Coordinating supplier compliance and reducing the work of tracking and capturing supplier details 

Compliance has become a core procurement responsibility, yet it remains highly manual. Tasks such as collecting declarations, validating certifications, tracking expiration dates, and refreshing supplier data often require working across fragmented systems and repeatedly following up with suppliers to close data gaps. Agentic AI enables a shift from periodic compliance checks to continuous compliance by requesting, validating, refreshing, and documenting supplier information on an ongoing schedule, while flagging issues early. In practice, this can mean agents routinely requesting refreshed certificates ahead of expiration, validating submissions within hours rather than weeks, and maintaining an up-to-date compliance record without periodic clean-up efforts.

Agents can streamline supplier data and compliance management by collecting required attributes, such as legal entity details, tax IDs, banking data, and certificates, through guided workflows. It can validate submissions for completeness, detect duplicates, route higher-risk suppliers for enhanced due diligence, and continuously monitor expirations to trigger re-certification before deadlines. At the same time, it maintains audit-ready evidence, clearly recording what information was collected, when, and how it was verified. Together, these capabilities shift supplier compliance from a periodic, manual exercise into a continuous, proactive process that reduces risk while lowering the operational burden on procurement teams.

Managing tenders and RFPs autonomously to ensure faster tendering 

Despite years of digitalization, running a tender still resembles a document-and-email marathon in many organizations. Requirements are gathered through meetings and email threads, documents are revised and re-circulated multiple times, and supplier questions arrive sporadically across multiple channels. Responses come back in inconsistent formats, forcing procurement teams to spend significant time chasing clarifications, tweaking spreadsheets, and manually stitching information together before evaluation can even begin.

Agentic AI can transform this process into a predictable, end-to-end operation. Agents can synthesize stakeholder inputs into a clear scope and evaluation criteria, manage supplier longlists and invitations, coordinate timelines, and consolidate supplier responses. They can identify gaps of information, draft evaluation summaries, and prepare award recommendations with clear rationale and supporting evidence for approval. The result is not just faster outcomes with less coordination effort, but stronger governance, better decision-making, and sourcing processes that scale without overwhelming procurement teams. 

In practice, this means agents can run the tender process end to end. For example, an agent can gather requirements from stakeholders, reference historical tenders, and draft an initial RFx using approved templates. It can manage supplier communications and timelines, handle Q&As and reminders, and consolidate incoming responses into clean, comparable formats. Before evaluation begins, the agent can flag missing fields, chase clarifications, and standardize data so bids arrive structured and complete. It can then generate an evaluation pack incorporating comparison tables alongside a narrative summary for the sourcing team. The net effect is a compression of tender timelines from weeks to days by reducing back-and-forth and ensuring bids arrive structured and complete. 

Managing the bidding process to improve accuracy and accelerate RFP responses

Speed matters in sourcing, but accuracy matters more. In most procurement teams today, bids arrive in different formats, with missing fields, unclear assumptions, or inconsistent data. Buyers then have to go through lengthy and frustrating rounds of clarification, rebuilding spreadsheets so bids can be compared fairly and neatly. This back-and-forth is what stretches sourcing cycles, impacts costs, agility, and exacerbates supplier relationships, and ultimately drains organizational efficiency. 

Agents streamline this process by acting as bid quality control. Before a bid ever reaches an evaluation team, agents can prevalidate submissions, highlight anomalies, flag inconsistencies automatically requesting clarification from suppliers, as well as standardizing how responses are captured so evaluation teams receive clean, comparable inputs, enabling faster and more confident awards. Teams using this approach report fewer clarification rounds, cleaner and fairer evaluation rounds, and faster awards because bids arrive complete and comparable the first-time round.

Beyond sourcing…

The impact of agentic AI extends beyond tenders and sourcing. Across day-to-day procurement operations, agents can handle tasks that maintain operational efficiency. They can support contract management by drafting first versions from approved templates, highlighting missing clauses, tracking key obligations, and prompting renewals based on usage and performance. Agents can also monitor supplier risk, propose mitigations, and generate contingency playbooks before disruptions occur.

The common thread is not automation for its own sake, but effective orchestration including fewer manual handoffs, less chasing across systems, and clearer escalation to people only when judgment or accountability is required. 

When implementing AI agents, leading teams should focus on one high-volume operation where they need additional capacity and deploy agents end-to-end, treating them as digital workers rather than another procurement tool. They should define clear autonomy boundaries such as what an agent can do without approval versus when it must escalate, standardize templates and data fields so agents can produce consistent, comparable outputs, and treat supplier experience as a first-class KPI. In doing so, teams can transform faster and clearer coordination into a competitive advantage. 

At Zycus Horizon EU & UK, two independently commissioned research studies landed on the table simultaneously – one authored by Forrester Consulting, one by The Hackett Group, both commissioned by Zycus.

Different methodologies, different sample sets, the same essential conclusion: at the precise moment agentic AI is reshaping the source-to-pay landscape, most procurement organisations have not yet established who is accountable for how that technology gets deployed. 

This is not a story of procurement versus IT. Both functions have indispensable roles. It is a story of clarity of ownership – and of what happens when that clarity is absent. It is also, critically, a story for chief executives and boards: the governance norms your organisation sets now will determine whether AI in procurement becomes a source of competitive advantage or a source of strategic drift. 

Zycus, which commissioned both studies and built the Merlin AI Agentic Platform that sits at the centre of this conversation, has made a deliberate bet: that the procurement profession needs not just better technology, but better frameworks for leading it. The research is the evidence base. The May 13 summit is where that evidence becomes action. 

The confidence gap 

The Zycus – Forrester study — Don’t Delegate AI: Why Procurement Leaders Must Personally Shape, Not Surrender, AI-Driven Decisions – surveyed 261 global procurement leaders and found a structural disconnect at the heart of AI adoption. 

69% Confident in their AI vision

31% Confident in their team’s ability to execute

38pts The execution gap

That 38-point gap is where strategic AI ownership tends to migrate quietly — not because IT reaches for it, but because procurement hasn’t yet claimed it. IT’s involvement in agentic AI is not only appropriate but essential: this is a foundational platform area, like ERP or cloud infrastructure, where technology expertise, security standards, and integration architecture all require IT leadership. What the research cautions against is the CPO abdicating strategic judgement — allowing the ‘what’ and ‘why’ of AI to be determined by those who cannot fully see the commercial context. 

“AI doesn’t just automate procurement. It encodes procurement judgement into systems.” 

— Don’t Delegate AI — Forrester Consulting, commissioned by Zycus 

When AI determines which suppliers get flagged, which contracts trigger review, which spend gets scrutinised – it is executing someone’s judgement at scale. The Forrester research, commissioned by Zycus to ground its platform development in practitioner reality, makes clear that judgement must belong to procurement. The top three value-leakage areas – requirement scoping, post-award compliance, and obligation tracking, each affecting 48% of organisations – are precisely where agentic AI is expected to deliver returns. But only if CPOs define the rules of engagement. 

The barriers to scaling are equally telling: budget constraints at 49%, governance gaps at 43%, competing priorities at 43%. Forrester’s verdict is direct: these are not technology problems. They are leadership and ownership problems. 

The adoption index: Where organisations actually stand 

The Hackett Group’s Agentic AI in Procurement Adoption Index 2026, developed in partnership with Zycus, adds ground-level precision to Forrester’s strategic argument. Drawing from organisations where 78% report revenues above $1 billion and 60% manage more than $1 billion in annual spend, it maps enthusiasm against reality. 

58% of procurement leaders expect agentic AI to have a high to very high positive impact. Only 2% anticipate little to no effect. The technology has believers. What it lacks, in most organisations, is the governance architecture to match the ambition. 

Six findings every CPO should know 

1.  Awareness hasn’t cascaded. 75%+ of executives believe in AI’s value. Only 60% are confident their teams share that understanding.  
2.  Control confidence is low. Fewer than 50% of CPOs feel confident in their ability to monitor and control agentic AI systems.  
3.  IT and procurement both lead strategy. IT guides strategy at 58% of firms; procurement at 53%. Collaboration is the norm – but accountability must be explicit.  
4.  Workflows beat point tools. 65% prefer integrated agentic workflows over individual agents. Orchestrated autonomy, not isolated automation, defines the era.  
5.  Scaling is imminent; readiness is not. Deployment sits at 20–30% across S2P processes. Scaling within 12 months is expected. Governance maturity lags.  
6.  Tail spend is the proving ground. 86% are likely to deploy AI agents for tail spend negotiation autonomously. Only 36% are satisfied with current approaches.

The Hackett Group also identifies the highest-risk implementation areas: fraud detection, autonomous negotiations, contract management, payables management, and stakeholder management. Contract management appears on both the highest-value and highest-risk lists – which is not a contradiction. It signals that the areas of greatest potential are exactly where thoughtful CPO-led governance design is most critical. 

The investment signals confirm that scaling is coming regardless of readiness. Study respondents expect to direct approximately 12% of their technology budgets toward agentic AI within three years. Spend analytics, purchase order processing, market and pricing intelligence, supplier onboarding, and contract management top the list of expected returns. Zycus has built the Merlin AI Agentic Platform specifically to address these S2P process areas – with pre-built, interoperable agents designed to work as orchestrated workflows rather than isolated point tools, directly reflecting the 65% preference Hackett Group surfaced in their research. 

On governance – Hackett Group/ Zycus Research 

The top governance concern: loss of human oversight and control, driven by unclear cross-functional accountability and poor data quality. The top compliance risks: data privacy, data security, and legal/regulatory compliance. These require a whole-of-enterprise response, set from the top.

A message for the C-suite and board 

Both studies carry a signal that goes beyond the CPO’s desk. AI in procurement will shape how billions in third-party spend are managed, how supplier risk is monitored, how commercial negotiations are conducted, and how contract obligations are enforced across the supply base. These are not departmental technology decisions. They are enterprise-level stakes that belong on the board agenda. 

CEOs and boards need to actively set the expectation that AI deployment in commercially sensitive functions operates under clear cross-functional accountability – not in a grey zone where strategy and execution belong to different owners by default. That means defining, at the enterprise level, who is accountable for what AI does, how decisions escalate when AI operates at the edge of its guardrails, and how performance against outcomes gets measured, reported, and reviewed. 

The Hackett Group research is explicit that organisations have far greater maturity in general technology management than they do in strategy, governance, change management, and talent development. The infrastructure instincts are there. The organisational architecture to govern what AI does inside that infrastructure is, in most cases, still being built. Boards should be asking whether that architecture is being designed intentionally – or emerging by accident. 

Organisations that will lead in this era are those where the C-suite creates the conditions for procurement and technology to collaborate with shared objectives, mutual accountability, and a governance framework robust enough to earn the trust of regulators, suppliers, and stakeholders. 

The Agentic AI Procurement Summit · 13 May 2026 · Global Virtual 

The research has been published. Now comes the practical question: what do I actually do on Monday morning? On 13 May 2026, Zycus hosts the first and only global Agentic AI Procurement Summit – a virtual event with dedicated streams for US, EMEA, and APAC audiences. The conversations that began in Vienna at Zycus Horizon EU+UK will now open to thousands of procurement executives worldwide. 

Aatish Dedhia, Founder & CEO, Zycus

Christopher Sawchuk, Principal & Global Procurement Advisory Practice Leader, The Hackett Group

Jeffrey Rajamani, Senior Analyst, Strategic Sourcing & Procurement, Forrester Research

Christopher Sawchuk and Jeffrey Rajamani – the analysts who authored the defining research of this moment – will jointly map a practical adoption roadmap for procurement leaders at every stage of readiness. This is not a repeat of the Vienna conversations for those who missed them; it is the next chapter, taking the research findings into implementation territory. Practitioners from EY and IBM will address real-world implementation realities, sharing how governance frameworks, change management programmes, and technology integrations are being built inside large enterprises right now. A live demonstration of the Zycus Merlin AI Agentic Platform will show autonomous source-to-pay workflows in action – not slides, not hypotheticals. And delegates will leave with concrete frameworks for taking ownership of the AI agenda within their own organisations, applicable regardless of where they currently sit on the adoption curve. 

ALSO AVAILABLE FREE — AGENTIC AI ADOPTION INDEX TOOL BY ZYCUS 

Before the summit, find out exactly where your organisation stands. The Zycus Agentic AI Adoption Index benchmarks your readiness against real industry data from The Hackett Group study – giving CPOs a clear-eyed view of where they are and where leaders are pulling ahead. Access it free at zycus.com. 

The bottom line 

The Forrester and Hackett studies, both commissioned by Zycus to ground its platform development in practitioner reality, converge on a message that is urgent without being alarmist. Agentic AI is already inside the procurement function. It is reshaping spend analysis, purchase order flows, contract management, and increasingly, the strategic decisions that define commercial outcomes. The question is no longer whether to adopt. It is whether the adoption will be led or inherited. 

The organisations that will succeed are not those who deploy AI fastest. They are those who deploy it with the clearest accountability, the most thoughtfully governed workflows, and the strongest alignment between the commercial judgement of procurement and the technical expertise of IT. That alignment requires leadership from the top. 

“Successful agentic AI isn’t about blindly following the robots. It’s about humans and AI working together in partnership.” 

— Jeffrey Rajamani, Senior Analyst, Forrester Research — Zycus Horizon EU+UK, Vienna 

The roadmap is being written. Zycus – through its research partnerships, the Merlin AI Agentic Platform, and the May 13 summit – is providing the tools, the evidence, and the community to help procurement leaders write it on their own terms. The question is whether your organisation will help shape that roadmap, or inherit someone else’s version. 

Serving millions of residents across South East Queensland, Seqwater is undertaking a strategic shift to modernise its procurement function – transitioning from fragmented, manual processes to a digitally enabled, insight-driven operating model.

As part of this transformation, Seqwater has selected Zycus’ full-suite S2P platform, deploying capabilities through a phased implementation approach. The first phase introduces AI-guided procurement via Merlin Intake, establishing a unified entry point for business users and enabling more intuitive, compliant engagement across procurement activities.

Seqwater has now successfully implemented Zycus Source-to-Contract (S2C) capabilities, including Contract Lifecycle Management (iContract), Supplier Management (iSupplier), and strategic sourcing (iSource). This milestone establishes a robust digital foundation with embedded workflows, enhanced accountability, and end-to-end visibility.

The go-live represents a step-change in procurement maturity, delivering:

· End-to-end pipeline visibility, with real-time insights into sourcing activities and award status

· A single, authoritative source of supplier data, improving consistency, accessibility, and control

· Supplier self-service functionality, enabling real-time management of compliance documentation and invoice tracking

· Enhanced transparency across risk, value, and activity to support stronger governance and decision-making

· A transition to structured, system-driven processes that reduce manual effort and increase operational efficiency

“This transformation represents a pivotal shift for Seqwater” said André Harvey, Chief Procurement Officer, Seqwater. “Moving to a single, integrated platform provides the visibility, governance, and structure required to manage procurement at scale and support the organisation’s long-term objectives.”

Seqwater will continue to expand its deployment across the Source-to-Pay lifecycle, with Procure-to-Pay (P2P) capabilities planned in the next phase. This will further enhance automation and deliver a more seamless, user-centric buying and invoicing experience.

“Seqwater’s go-live marks a significant milestone in building a modern, intelligent procurement function,” said Aatish Dedhia, Founder and CEO, Zycus. “We are proud to partner with Seqwater as they advance toward a fully integrated, AI-enabled procurement ecosystem.”

For more information about Zycus, click here.

Leading AI platform for enterprise procurement Zip has announced the launch of its Enterprise Transformation Office (ETO).

ETO is a dedicated team of former Fortune 500 procurement leaders who will work directly with customers to drive large-scale procurement transformation.

Led by Jason Moore, Head of the Enterprise Transformation Office at Zip, who previously served as Senior Director of Enterprise Procurement at Discover, Moore has brought together a team of seasoned executives who collectively bring decades of experience running procurement at some of the world’s largest organisations:

• Peder Gustafson, former Chief Procurement Officer at UnitedHealth Group (Fortune 3), with additional CPO experience at Ameriprise Financial and VP-level procurement roles at Medtronic. 
• James Czarnecki, former SVP and Chief Procurement Officer at MGM Resorts International, with 15+ years of experience in supply chain, process excellence, and enterprise transformation.
• Danielle McQuiston, former VP and Head of North America Procurement at Sanofi, where she oversaw more than $4 billion in spend and 100+ employees across all spend categories and business units including pharmaceutical, animal health, vaccines, and consumer health.
• Jason Powell, former Global Head of Procurement at Figma and held procurement leadership roles at Stripe and Asana.

The team is already advising Fortune 500 leaders across financial services, retail, consumer packaged goods, travel and hospitality, manufacturing, healthcare and lifesciences.

“The leaders who transformed procurement at the world’s largest companies are now at Zip, helping their peers do the same,” said Rujul Zaparde, Co-Founder and CEO of Zip. “As enterprises adopt agentic AI, they need not just powerful software, but also strategic partners who understand the complexity of change at scale. That’s exactly what our Enterprise Transformation Office delivers.”

“We’ve sat in our customers’ seats,” said Moore. “We’ve managed the politics, navigated the complexity, and led the transformation. Now we’re bringing that experience to every enterprise working with Zip.”

“With evolving market pressures, mature procurement functions are increasingly being viewed as strategic capabilities, driving significant value back into the business,” added Gustafson. “I’ve had the privilege to lead world-class procurement functions but doing so required the ability for stakeholders to easily, and intuitively engage, something only possible through a well orchestrated process. Joining Zip is a perfect fit as it provides an opportunity to partner with procurement leaders on that same journey while sharing best-practices and lessons learned.”

The launch of the ETO follows a breakout year for Zip, which recently announced $6 billion in total customer savings, 50+ purpose-built AI agents, and new enterprise customers including LinkedIn, PIMCO, Block, and Mars.

As the role of Chief Procurement Officer has evolved you have taken on, willingly or not, an increased number of responsibilities within your enterprise. Responsibilities that must be handled efficiently and without error for the company as a whole to run and produce as it should. Global use of AI in procurement has surpassed $1 billion, and “94% of business executives believe AI will enhance their operations in the next five years.” The reality is, you must begin leveraging AI immediately to the fullest of your capabilities, specifically agentics. 

Agentic AI is the latest evolution of artificial intelligence. It needs minimal human oversight, learns on its own, and can make decisions autonomously based on real-time data. To get ahead of this curve and maintain and grow your lead there are several avenues for you to implement agentic AI now for immediate results.

Sourcing goods

Agentic AI has the potential to check inventory in real time, review sales and demand forecasts, and automatically request new orders or actually create an order with a supplier once inventory reaches preset points. This reduces the chances of running out of stock and overstocking which in turn creates a better flow of operation. 

Negotiation with suppliers

Agentic AI processes data, automates operations, and offers critical insights for you when negotiating with your current and potential suppliers. With agentic AI, a large number of potential suppliers can be scanned, their performance information can be analysed, and the best ones, that suit a particular requirement, can be selected. This will speed up the process of identifying the right vendors and consume less time than manual searches.

Contract management and supplier relationship management

Annually businesses lose approximately 5% of spend, or $10,000 to $150,000 due to procurement fraud, and waste and abuse. Humans can only monitor so much, this is where agentics will give you immediate and substantial ROI. 

Via Contract Management (CM) and Supplier Relationship Management (SRM) agentic AI capabilities can replace all manual tracking and processes and eliminate your over-reliance on software such as Excel. AI agents can automate contract development, monitor compliance, and even renegotiation. 

Agentic AI is capable of drafting contracts, following an already set template and rules, which substantively accelerate the process and limit the number of errors. You can also apply agentic to match proposals on elements that would be beneficial to you or parts that would be overly beneficial to your suppliers.

Risk management and compliance

With agentic AI it is possible to analyse transactions in real-time to the point when a possible compliance-related problem arises it can be flagged immediately. The capacity to lower risk scores around a dynamic set of circumstances makes sure that the riskiest and the most important threats come to the top of the list. Automating alerts for suspicious behavior, Agentic AI helps to react to potential risks faster and minimises the time within which the risk may cause any damage, improving productivity of compliance teams.

In addition to operational responsiveness in real-time, agentic AI provides potent risk mitigation. It uses predictive analytics to identify threats, which could be fraud, regulatory change, or geopolitical disruptions so that an organisation can be proactive instead of reactive. Not only does it improve accuracy and reduce human error, it also eliminates bias in critical judgments by analysing large volumes of data to detect patterns of fraud and automating the process of checking compliance. Early warning systems enable companies to be proactive and make the compliance process proactive and strategic.

Payment processing, cost optimisation and process improvement

In terms of payment processing, agentic AI facilitates smart payment routing, based on real time data in order to select the most optimal paths, automatic reconciliation of discrepancies and improved fraud protection through dynamic payment authorisation. Also, it is possible to conduct international transactions via agentic AI; the conversion of currencies can be automated, the transactions can comply with international regulations, the payment can be monitored in real time, and the transaction would be done automatically without further human intervention within specific conditions.

Regarding cost optimisation, agentic AI lowers the demand of manual labor due to automation of repetitive processes and, therefore, cuts down the operational costs related to human error. It boosts the efficiency of processing, allows more effective use of resources, and prevents and solves disputes and frauds by detecting them in real-time and running a self-contained investigation. 

Agentics can improve cash flow management by making predictions of the flow helping organisations manage liquidity and prevent financial penalties. Agentic AI makes continuous process enhancement possible due to the constant learning of new information, rule-directed compliance within workflows, and provides case-specific customer experience. 

An undeniable opportunity sits before every CPO in the form of agentic AI. When coded and deployed purposefully and properly it will revolutionize your role, your department, and the company as a whole. To not explore this technology would be negligent at this point. Grab the future by the tail and unleash the power of agentics in procurement now. 

Chetan Dube is an AI Pioneer and the founder and CEO of Quant, which develops cutting-edge digital employee technology.

AI has transformed the way procurement lives and breathes.

Over the past few years, the workplace has seen an explosion of new digital tools flooding the market, each offering ways to deliver time and cost savings previously unimaginable a decade ago. Indeed, AI is touching all corners of the procurement function and those who don’t embrace today’s technology are set to be left behind. 

One of the biggest drivers in recent times has been the acceleration of generative AI. Since ChatGPT burst onto the market in November 2022, chatbots and large language models have exploded in popularity and surged in use. According to McKinsey’s research, it is estimated that GenAI could add up to $4.4 trillion to the global economy annually while increasing the reach of AI by 15% to 40%.

But two and a half years later, a new kid on the block has emerged – agentic AI. The technology refers to a type of artificial intelligence system that acts as an autonomous agent that is capable of making decisions, taking actions and learning from interactions without the need of human intervention. The key difference from chatbots such as ChatGPT or Copilots is that generative AI is based on data it has learned and is primarily static, whereas agentic AI is continuously processing new information and learning from its environment.

Agentic AI: Operating with minimal human intervention

Nicolas Walden, Europe Practice Leader, Procurement Advisory for The Hackett Group, believes that one of the core advantages of agentic AI is that it is ‘always on’. “Agentic AI, by contrast, is a newer and more autonomous type of AI that can make decisions, take actions, and learn independently,” he tells us. “It can solve more complex problems by analysing data, setting goals, and adapting to new information. Agentic AI is designed to operate with minimal human intervention. It is used to automate workflows and business processes, frequently through a combination of intelligent agents. Examples include autonomous vehicles, cybersecurity threat detection, black-box trading algorithms, and logistics route optimisation and planning. While GenAI capabilities have been widely deployed for around two and a half years, the development of agentic AI is still in its early stages.”

Manoj Chaudhary, the CTO of AI integration company Jitterbit, believes agentic AI is the next frontier of procurement innovation and helps overcome challenging tasks through immediate support to help support actions. “AI serves as a digital partner, accelerating processes and democratising data access. The latest advancements—agentic AI and AI agents—take this partnership further, helping tackle increasingly complex tasks and offering real-time support for decision-making,” he says. “These concepts represent two different approaches to use cases for AI, yet are often grouped together by developers. We see agentic AI as an autonomous decision-making capability that can act independently within defined parameters, allowing enterprises to delegate operational decisions that do not need oversight but still prioritise ethical standards, data integrity, and security. This approach ensures that businesses can achieve efficiency and innovation without compromising on accountability and control. 

“AI Agents, on the other hand, are task-oriented for predefined actions and follow specific user commands to automate workflow management, assist in complex development tasks, and provide real-time decision-making support. These agents offer immense potential to improve key areas of business process management with orchestrated oversight, including workflow automation, resource allocation, and performance monitoring.”

GenAI vs Agentic AI: What’s the difference?

While Simon Geale, Executive Vice President at Proxima, explains that generative and agentic AI are likely to be used interchangeably over the coming months, in addition to the real likelihood of other forms of AI being included too. “At their core, the difference is that generative AI creates content based on data (sometimes referred to as large language models, or LLMs), and agentic AI is able to carry out processes and tasks, making decisions based on learned logic, which includes data. You might say that GenAI is more like an assistant who thinks and creates and agentic AI is more like an employee who decides and does. You might also observe that the combination of the two has the potential to be quite special, automating not just processes, but also production.

“In procurement terms, this takes us further down the path of not just full automation, but moreover automated augmentation; we will be able to do more, faster on the current proviso that process and data are in decent shape. That said, whilst generative AI depends on accurate data (the narrower and more precise the better), it can also be part of the solution to getting to accurate data, using a balance of inputs and probabilities ‘to get to clean’.” 

AI’s next step

And Burley Kawasaki, VP of Product Marketing and Strategy at Creatio, affirms agentic AI is the next leap forward in enterprise automation – moving beyond thinking to actually doing. “While generative AI produces content like text, code, or imagery by recognising patterns in data, agentic AI goes further and actually takes action. It executes tasks autonomously, moving beyond suggestions to actual orchestration.

“By combining machine learning, automation, and natural language processing, agentic AI can make decisions and manage workflows with or without human input. This shift from output to orchestration is what makes it so impactful. In customer relationship management, that might mean qualifying leads, responding to service requests, or even orchestrating personalised customer journeys, all without manual input.

“Crucially, the real value lies in balancing the AI triad. Predictive AI offers foresight, generative AI brings creativity, and agentic AI drives execution. Together, they create a powerful framework for AI-native automation across the enterprise that drives quantum leaps in intelligent productivity and results.”

Future-proofing procurement transformation

Looking forward, Chaudhary believes both technologies will be increasingly relevant throughout the remainder of 2025 and beyond. “These complementary systems will redefine business operations, setting new standards for productivity, strategic execution, and adaptive management and enterprises that harness both capabilities effectively will be able to carve out a competitive edge in an increasingly automated world.”

As a result of the boom in demand for new technology solutions, there is a fear among some sections of the industry that AI is here to take human jobs. But Kawasaki stresses this is not the end goal. As AI advances, the real opportunity lies in augmentation – freeing human talent from manual tasks to focus on creativity, strategy, and innovation. “When paired with no-code platforms that enable non-technical users to build AI-driven workflows, agentic AI becomes not only powerful but widely accessible. Ultimately, the future of enterprise AI won’t be defined by what it creates, but by what it enables us to achieve.”

AI-powered B2B marketplace for automotive fleet parts Autoket has announced its full rebrand and relaunch as Supply Veins. 

This strategic shift reflects a broader vision to build the next generation of intelligent supply chain technology across air, land, and sea by addressing the communication breakdown that occurs between buyers and suppliers.

Supply Veins is an AI-powered communications platform that streamlines procurement, centralises supplier data, and enhances buyer-supplier relationships for greater efficiency. “We discovered the real supply chain problem was a universal communication gap, not just in auto parts,” said Charles Masters Rodriguez, a West Point graduate and US Army veteran and Co-Founder and CEO of Supply Veins. “Supply Veins is our solution to foster seamless interactions between buyers and suppliers across all industries. We’ve created the first Unified Supplier Communication System (USCS).”

The platform is particularly beneficial for businesses that routinely purchase products from multiple suppliers and struggle with purchase order management and tracking. Current pilot program participants span diverse sectors, including auto manufacturing, pharmaceutical manufacturing, distributors, hardware startups and SMBs.

Supply Veins, Autoket at the time, participated in the PenFed Foundation for Military Heroes’ Veteran Entrepreneur Program as part of the Fall 2024 cohort. Since 2018, the Veteran Entrepreneur Program has supported 100+ military founders. Through the Foundation and its network of investors, mentors, and subject matter experts, alumni founders have accessed several million dollars in startup capital, grown a network of key stakeholders, and received invaluable mentorship from industry experts.

“We are incredibly proud of Charles and the evolution of Supply Veins in the marketplace,” said Andrea McCarren, President of the PenFed Foundation for Military Heroes. “This strategic pivot, showcasing remarkable adaptability and innovative spirit, is a testament to the lasting impact of the Veteran Entrepreneur Program.”

The biggest craze of 2025 so far has been the arrival of agentic AI and the scale of opportunity on offer.

You could be forgiven for thinking agentic AI is simply generative AI but that’s not quite the case. In actual fact, agentic AI is heralded as the next frontier of artificial intelligence within procurement. Indeed, agentic AI refers to a type of artificial intelligence system that acts as an autonomous agent that is capable of making decisions, taking actions and learning from interactions without the need of human intervention. The key difference from chatbots such as ChatGPT or Copilots is that generative AI is based on data it has learned and is primarily static, whereas agentic AI is continuously processing new information and learning from its environment.

AI agents can manage up to 90% of repetitive tasks, freeing humans to focus on higher-quality, more important tasks. Agentic AI is quickly becoming an increasingly prominent topic on a procurement leader’s lips amid an industry-wide digital transformation drive. 

In this article, CPOstrategy explores five of the biggest benefits of agentic AI in procurement. 

Faster, smarter sourcing

Agentic AI can autonomously identify new suppliers and evaluate them based on key criteria such as performance data, ESG metrics and risk profiles before onboarding or contract negotiations. As a result, this reduced lead time and ensures more dynamic supplier ecosystems. AI agents can take part in negotiations and utilise historical data and market trends to ensure organisations get a good deal without the requirement of manual intervention. By automating tedious data-entry tasks, AI agents free up procurement teams to focus on high-priority projects and can accelerate a businesses’ development.

Proactive risk management

Agentic AI can observe market fluctuations, geopolitical events and supply chain disruptions in real-time before acting to mitigate the risks within an organisation’s own operations. This could be through the likes of rerouting shipments or offering alternative suppliers to use, among many other solutions, depending on the level of anticipated disruption. By flagging potential problems prior to them becoming serious, AI agents can allow procurement teams to pivot proactively and stay ahead of risks.

Reducing costs while maximising value

Reducing costs is a Chief Procurement Officer’s bread and butter. By drawing on market data and procurement trends, agentic AI tools can uncover cost-saving opportunities without compromising quality. Agentic AI can leverage historical data to automatically generate RFPs, negotiate terms with vendors or even draft and revise contracts with legal alignment. As a result, this could lead to decreased cycle times and increased savings through optimised terms and pricing.

Boosting customer engagement

Agentic AI can act like a digital procurement assistant, helping with tail-spend purchases, recommending preferred suppliers for departments or even ensuring purchases comply with policies. 

The technology can be strategically deployed as intelligent procurement chatbots, delivering real-time, personalised responses to supplier or stakeholder questions. By leveraging historical procurement data, transaction records, and behavioural trends, these AI agents enhance engagement, foster stronger supplier relationships, and streamline procurement support, all while ensuring key stakeholders feel both understood and valued.

Rapid onboarding and tail-spend management

Agentic AI transforms procurement by autonomously managing rapid supplier onboarding and tail-spend—those low-value, high-volume purchases that typically fall outside of structured processes. Instead of relying on manual effort, AI agents can verify supplier credentials, run compliance checks, draft contracts, and even source and purchase goods or services within company guidelines. This brings speed, consistency, and control to an area traditionally plagued by inefficiency and lack of visibility. For procurement leaders, it means reclaiming oversight of fragmented spend, unlocking hidden savings, and freeing up teams to focus on more strategic, high-impact initiatives.

Context for the article – two years ago  

In May 2023, I published an article in CPOstrategy on ChatGPT’s impending impact in procurement – my first accurate forecast after nearly a decade of AI “predictions gone wrong.” In 2023, I predicted that generative AI would open a new frontier for data processing, content creation, and decision support, and that procurement leaders must balance its opportunities against its risks. 

This article, two years on, is split into two parts: 

1. Part 1 (“Setting the Scene”) published in June 2025 revisited that May 2023 article and explained the significant AI technological advances across GenAI since. 

2. Part 2 (“AI-Enabled Source-to-Pay”) this article assesses how those capabilities are impacting the S2P markets, with real-world examples and looks forward another five-10 years. 

Two years on – Setting the scene… 

Key takeaways from part one for the Chief Procurement Officers (CPOs) summarised from this article: 

1. Strategic impact of AI: AI will significantly transform procurement functions and fundamentally alter the categories procurement manages. Categories a company procures and how suppliers sell will evolve significantly. Products and services will merge, traditional service models will transform dramatically, and some categories may entirely disappear due to automation and technological innovation.  

 
CPOs should actively monitor these shifts, develop new sourcing strategies to manage emerging categories, engage closely with suppliers to understand their evolving business models, and proactively prepare their organisations for this extensive reshaping of markets and supplier relationships. 

2. AI technology changes: Rapid advances in AI technology are driving significant improvements in AI performance, notably through increased GPU power, captured by Huang’s Law, which significantly reduces costs and boosts processing speed.  
    
   Modern AI now integrates multiple capabilities – perception (recognising patterns and speech), cognition (reasoning, problem-solving), and action (physical autonomy) – resulting in highly autonomous systems. This evolution supports increasingly sophisticated tasks within procurement, enhancing efficiency and effectiveness by automating routine operations, offering real-time insights, and enabling smarter decision-making.  

Procurement leaders should prepare for accelerated AI adoption by adapting their technology strategies and aligning AI deployments with their operational and strategic objectives. 

3. Impact on the supply base: AI technology is dramatically reshaping the supply base by fostering innovative business models and merging traditionally separate categories of goods and services.  
    
   Suppliers increasingly leverage AI for product development, service automation, customer engagement and sales, leading to the convergence of physical and digital offerings. Traditional service-oriented sectors will see substantial disruption or potential elimination, replaced by automated and AI-driven alternatives.  
    
   Procurement leaders must maintain strong engagement with suppliers, anticipate market evolution, and align procurement strategies and resources accordingly to capitalise on these shifts. Close collaboration and strategic alignment with innovative suppliers will be critical for businesses to remain competitive and responsive to market disruptions. 

Quote: “AI will transform procurement — not just how it operates, but what it manages” 

Business has access to so much AI…. so what? 

As AI moves from novelty to necessity, CPOs face a pivotal moment. This article bridges the strategic horizon set in our last piece with a pragmatic roadmap for action. It helps Chief Procurement Officers translate business strategy into procurement priorities – by identifying where AI can make the most material impact.  

The article outlines a CPO-specific method to build an action plan for the next 24 months, followed by a forward-looking view of how AI capabilities are likely to evolve across the procurement value chain over the next five to 10 years. The goal: to equip CPOs with an approach, tools, and confidence to lead AI-enabled transformation, not just in procurement, but across the enterprise. 

The first step is to scan the current Source-to-Pay (S2P) technology footprint and introduce the concept of a “platform architecture” to clarify which systems are most critical for your company.  

Understand the market – Source-to-Pay capabilities 

Before we assess how AI is being ingrained into procurement, it is useful to consider the capabilities in Source-to-Pay by looking at the Source-to-Pay technology landscape as of 2025. 

The starting point for any future AI journey is to map the as-is architecture and ensure that you are aware which functions you have covered. Many CPOs will have a well-documented architecture supporting their strategic, tactical and operational capabilities but some will not.  

The tables below should enable you to understand what technologies you have and don’t have by capability area (e.g. category management).  These tables are a quick scan designed to be illustrative. For reliable view, I recommend tools like ProcureTech or Spend Matters.  

Over the last four years, ProcureTech took the time to analyse over 4,000 procurement applications and selected the top 100. Rather than a formal assessment, I have relied on my experience and knowledge, supported by a quick market scan using Google and GPT-based searches to illustrate capabilities that are available in enterprise systems like SAP, Oracle and Microsoft Dynamics. This is an illustrative scan at the time of writing the article and should not replace a formal application selection process. 

From this table it is possible to see that the breadth and depth of functionality across ERPs and then deep within individual capabilities through start-ups is significant. The challenge is connecting this together in a meaningful way where the workflow and process and data can be governed meaningfully to create efficiency, effectiveness and insight. 

CPOs should map their current architecture and then try to decide which capabilities and processes are most critical, including those systems such as Master Data Governance or Process Orchestration that may be currently managed outside your function. 

Setting the right foundation 

Map your platform – Introducing the Platform Architecture  

When I am designing the technology layer of my client’s to-be operating models, I use tools such as Gartner Pace Layered architecture model to design a Platform Architecture which maps the business criticality and interconnectivity of systems.  

The Gartner Pace-Layered Application Strategy provides a structured approach to enterprise architecture by segmenting systems into layers based on their rate of change, business value, and purpose. These systems work at different speeds (pace) depending on the criticality with the top systems having the fastest clock speeds, often real time and the bottom system being slower, transaction by transaction. 

The layers shown in the diagram are: 

1. Systems of innovation – These sit at the top, supporting experimentation and rapid development of new capabilities, either for the business or for procurement. Examples could include experimentation in AI agents, supply risk or sustainability. These systems are agile, insight-driven, and help test or pilot new business models and new technologies.  

2. Systems of differentiation / transaction – Systems of differentiation provide competitive edge and are tailored to support role-based workflows. They are purpose-fit applications that help shape distinctive business experiences, such as AI agents or supplier collaboration portals. These are supported by SaaS applications and ProcureTech App. This is where most of your companies differentiation in procurement will sit. Systems of transaction are where the process flows sit, in either ERP systems or SaaS applications. Examples include eSourcing, eProcurement and Category Management. 

3. Master data and integration – This core layer governs the flow of data between systems, ensuring business-critical information (e.g. supplier or item master data) is accurate, consistent, and shared across platforms. 

4. Systems of analytics and insight – These aggregate and govern structured and unstructured data to generate actionable intelligence. They enable visibility, compliance, and performance improvement through advanced analytics. 

5. Systems of record – These are foundational systems like ERPs that ensure transactional and financial data integrity for core operations and reporting. This is where the orders and invoices sit that form the basis for tax and financial reporting. 

6. Infrastructure security and access control – At the base, this layer supports the secure operation of all systems above through resilient, well-governed infrastructure and cybersecurity protocols. 

7. Cross-cutting all layers is workflow and orchestration, connecting people and processes to ensure seamless, integrated operations. This is often forgotten, or left to a workflow email managed through an email or pop up in an app, which does not connect to people, process and data. 

[see attached ppt] 

YOUR NEXT MOVE 

Document your own company’s Source-to-Pay application landscape, and map this into the pace layered model to create your as is Platform Architecture and then work out where the major gaps are given your business and procurement strategy and your 3-year targets.  

Identify the critical master data items that need to be of exquisite quality to ensure your strategy can be executed, as I guarantee that most data is of mixed quality and poorly governed. 

Pick the top three areas and obtain funding to experiment in those three areas, ideally including master data and process orchestration. 

Quote: “Even the smartest AI won’t deliver value without a connected platform to support it” 

If we have all these systems and £ms of investment, why is it still so hard? 

Despite years of investment in ERP platforms and a growing wave of ProcureTech innovation, procurement still struggles with core execution challenges. Reliable spend insight remains hard to achieve. Transaction automation is patchy. And workflow orchestration – between users, suppliers, systems, and now AI agents – is often broken or incomplete. These issues are not just technical; they are also structural. 

At the heart of the problem lies fragmented and poorly maintained master data and transaction data. Without a clean, connected foundation, even the best AI tools will struggle to deliver value. There is also no true orchestration layer – no connective tissue that spans people, processes, systems, and now autonomous agents. In many cases procurement lacks clearly defined user journeys to design experiences that integrate data, workflows, and decisions coherently. 

Now, people are racing to deploy AI agents to automate tasks, answer queries, and drive action without fixing the foundation – data, orchestration, and design. These agents will be operating in silos, perpetuating the same fragmentation they were meant to solve. 

Key challenges in summary: 

1. The strategic elements of Source-to-Pay are knowledge based – which traditional systems and struggle to manage. 

2. Poor structure and governance of master and poor transaction data – partly as supplier invoice data is not captured and cleansed. 

3. Poorly designed procurement architecture with an absence of process flow – systems are designed technically, not with business process in mind, and around the “happy day” scenario and not real life. 

4. Lack of an orchestration layer to connect people, processes, and systems – Tools like Zip and ORO are starting to address this. 

5. Absence of well-defined user journeys for process integration – even if you have a well-considered architecture and an orchestration layer, unless you design by Person/real user journey, usage is likely to be poor, and you risk missing the other elements such as policy and change management.  

Identify your priorities 

How AI can help solve these fundamental challenges? 

1. The strategic elements of Source-to-Pay are knowledge-based – traditional systems struggle to manage this effectively. 

How AI helps: 

 
Some tools (as listed in Table 1) already support parts of strategic procurement – like category planning or supplier risk – but they are often siloed. AI co-pilots help connect these systems and bridge gaps where coverage is weak. For instance, an AI agent can combine sourcing data, internal strategies, and supplier insights to draft a category plan or negotiation brief. They also capture and reuse tacit knowledge from senior managers, making it more accessible and durable.  

In short, AI augments existing tools and fills blind spots, helping teams tackle strategic work with more speed, consistency, and context. This should be more than your category managers writing strategies or analysing data with Copilot. 

2. Poor structure and governance of master and transaction data (especially invoices) 

How AI helps: 

 
AI models, especially those trained on financial documents, can extract, clean, and standardise data from unstructured invoices, emails, and legacy systems. Some specialist Accounts Payable packages like Basware move you a long way forward extracting that data but there is still more to do to get the full level of source data in the systems of transaction or record. 

ML algorithms can detect master data duplicates, classify suppliers, and identify anomalies in spend data. Over time, AI can automate parts of data stewardship, helping improve master data quality continuously, rather than relying on periodic clean-ups.  

This is the low-hanging fruit that will unlock everything else. If I were a CPO, I’d start by using AI to fix master data and extract the transaction data we already have. 

Quote: “If I were a CPO, I would prioritise AI to fix my data” 

3. Procurement architectures lack true process flow and are built around technical assumptions 

How AI helps: 

Tools like Celonis and AI agents can highlight and resolve process friction by analysing user behaviour, exception rates, and bottlenecks across the S2P lifecycle. Large language models can simulate real-world ‘unhappy day’ scenarios to test resilience and spot design flaws and allow you to manage them on an ongoing basis. Over time, this enables more iterative, user-informed design rather than rigid technical blueprints. 

Beyond this there is no alternative but to implement the core components required across the Source-to-Pay lifecycle. Some believe that agents will fully circumvent the need for Source-to-Pay packages, but I think this may still be five years away – we still need applications to manage access, data and integration. Increasingly through systems of differentiation and transaction will move into systems of record and some differentiation will move into the agentic or orchestration later. 

4. No orchestration layer connecting people, processes, and systems 

How AI helps: 

 
AI agents can serve as a connective fabric across fragmented systems, acting as orchestrators of micro-tasks – routing information, prompting next actions, and maintaining flow across different tools. Platforms like Zip and ORO are embedding AI to automate intake, triage, approvals, and supplier onboarding in a coordinated, responsive way. 

AI agents, which are autonomous systems that initiate and perform tasks or decisions on autonomously or supervised on a user’s behalf. 

5. No well-defined user journeys – hurting adoption and change 

How AI helps: 

 
AI tools can help map and simulate user journeys based on behavioural data, identifying friction points and usage drop-off. AI-driven design assistants can also support rapid prototyping of procurement interfaces and workflows tailored to specific personas (e.g., requester, budget holder, category lead). With the right prompts, AI can even generate policy summaries or training content to reinforce process adoption. 

All that said, there’s no substitute for well-run design thinking sessions—bringing users together to identify issues, explore solutions, and align across both logic and emotion. AI can provide inputs, and draft great personas, but the well facilitated design thinking will allow you to align on logical rational (what should we do?) and political and emotional (why should we care and will we bother?). 

So, you have a sense of where you are, what your priorities are and where you need to focus. You should have a sense of where you need to build the foundation, such as data or extend applications or invest in AI. 

YOUR NEXT MOVE 

Given your existing footprint and architecture, your priorities and the gaps you wish to close out you can now built a to-be architecture and transformation plan such as the one below to close out the gaps. 

Figure 1: Example Transformation Map

That’s the next two years taken care of, but what about 5-10 years away? 

Procurement in the future  

Five years out 

In five years, AI will fundamentally reshape the foundations and flow of procurement. AI agents will be native and human and autonomous communication hard to differentiate. Machine to machine collaboration will happen at scale, hopefully with the right guardrails. 

How AI Will Help: 

Master data will shift from static, error-prone records to continuously updated, self-healing datasets. AI models will continuously detect inconsistencies, enrich attributes, and reconcile inputs across systems in real time. 

Over time, this will evolve into a continuous sensing and real-time response capability. These will synthesise internal data, market intelligence, and supplier insights to develop category strategies, evaluate risks, and generate negotiation playbooks. This shift will free up teams to focus on judgement and stakeholder influence.  

Strategic procurement will be transformed through AI co-pilots that synthesise internal data, market intelligence, and supplier insights. Over time, this will evolve into continuous sensing and real-time adaptation. 

Transactional procurement will become near touchless. AI agents will triage intake requests, validate POs, auto-match invoices, and route approvals based on context, not rigid rules. These agents will interface directly with both internal systems and external supplier platforms, enabling seamless, multi-agent orchestration across organisational boundaries. 

Tomorrow’s insights won’t be extracted – they’ll be embedded and automatic. AI will analyse activity, exceptions, and trends continuously, surfacing risks, savings, and opportunities as prompts – tailored to each role, process, or decision point.  

Those companies that do not respond these challenges may face fundamental disruption from competition and new entrants. 

Quote: “Insight will be embedded, not extracted” 

Supplier-side AI agents will play a key role. Acting on behalf of vendors, they’ll negotiate, confirm orders, and provide real-time updates – creating a dynamic, responsive ecosystem of machine-to-machine collaboration. These future demands readiness: cleaner data, better design, and trusted orchestration to make it real. 

10 years out 

10 years from now, procurement will be radically transformed – fluid, autonomous, and deeply embedded in business value creation.  

To understand the scale of change, think about the world pre- and post-Amazon, Uber, or ChatGPT. Now imagine all those shifts happening at once. That’s where procurement is heading. I believe that 10 years from now, we’ll be living in a post-transformation world – one with five times the technology, twice as smart, and twice as effective. 

Quote: “Procurement is shifting — from human-led coordination to AI-fuelled orchestration” 

How AI Will Help: 

Master data as we know it will disappear. In its place, AI agents will maintain a dynamic, real-time digital twin of the supplier ecosystem—drawing on contracts, transactions, third-party data, and direct machine-to-machine signals. 

Strategic procurement will evolve into continuous sensing and response. AI agents will monitor markets, regulations, supplier health, and innovation trends 24/7—generating options, not just reports. Strategic procurement will evolve into continuous sensing and response. AI agents will monitor markets, regulations, supplier health, and innovation trends 24/7 – generating options, not just reports. Category strategies will be living frameworks, adjusted in real time by autonomous systems collaborating with human oversight. 

Transactional procurement will be fully autonomous. Buyers will define outcomes, and AI agents – both buyer and supplier-side – will negotiate, place orders, manage compliance, and resolve exceptions through digital dialogue, without manual touchpoints. Approvals will be trust-based, outcome-led, and exception-driven. 

Insight won’t just be surfaced – it will be predictive, prescriptive, and self-acting. AI will not only tell you what to do, but initiate action unless told otherwise. 

Supplier-side AI agents will be indistinguishable from their human counterparts – negotiating, customising, and troubleshooting live in digital environments. The procurement landscape will shift from human-centric coordination to AI-led orchestration, demanding new skills, governance models, and trust infrastructure to thrive. 

In conclusion 

Two years ago, when I first wrote about ChatGPT’s potential impact on procurement, I called it a new frontier. At the time, many of us were still asking whether AI would truly change the way we work. Today, we know the answer. It already has. 

What I have seen since that first article has only reinforced my belief that procurement is on the edge of a profound transformation. AI is moving faster than expected, and the capabilities we once imagined for the next decade are already starting to appear. But the pace of progress is not the only story. The real challenge is what we choose to do with it. Many CPOs are still at the starting line, grappling with foundational technology issues that predate the AI wave. 

This article has outlined a practical path for Chief Procurement Officers to follow. Map your technology landscape. Improve your data quality. Establish orchestration across systems. Prioritise investments with a clear line to business value. Most importantly, do not delay. The future of procurement will not be built solely on tools or technology. It will be driven by vision, leadership, and the willingness to rethink how procurement creates value. 

If 2023 was the year to ask what AI might do, and 2025 is the year to decide how to act, then I believe 2027 will be the year AI-first procurement operating models start to scale. Procurement teams will begin to see AI agents working across buyer and supplier ecosystems. Autonomous sourcing cycles will become common. Strategic decisions will be supported by co-pilots drawing on structured data, supplier intelligence, and real-time risk signals. Category strategies will be dynamic, constantly updated in response to market and performance data. Procurement will become less about managing steps and more about delivering outcomes. 

If you are not a CPO but work in procurement, this is still your call to action. Start learning everything you can about AI today. Understand how it works, where it applies, and how it fits into your role. This shift is already redefining the profession. The gap between those who are AI-literate and those who are not will widen quickly. There is no time to wait. 

The groundwork for the future must be laid now. 

If this perspective resonates with you, or if you are looking for someone to help you shape the first steps, feel free to reach out. I am always open to a conversation. 

Let’s lead the next chapter of procurement together.