Kelly Archer, Managing Partner and Joe Gibson, Head of Digital at 4C Associates, explore what sets good and bad AI apart in the procurement space.

Given the widespread consensus that the future is AI-driven, there’s one observation that never fails to amuse us. Time and again, people are still shocked by the idea that not all AI is created equal

AI is not new. Workflow automation has been around for decades. The technology dates back to when we first started tinkering with process flows at the turn of the century. The real change? AI has simply become accessible to the masses (thanks, ChatGPT!). More importantly, it’s found its voice, literally, through conversational models.

The current hype around AI barely scratches the surface of its potential, particularly when we compare generative AI to configured conversational AI. But this is just the tip of the iceberg. What many miss is that there are significant technical, procurement, and long-term delivery implications tied to the AI solutions you choose to invest in.

It seems like everyone is suddenly an AI “expert.” Practically overnight, the same technophobes who wouldn’t have touched a data model or architectural diagram with a ten-foot pole are now rebranding as digital innovators, hell-bent on revolutionising their industries with AI. It’s both fascinating and slightly ironic. In the case of Procurement and supply chain management professionals, the utilisation of AI capabilities is a real thing – advanced demand forecasting, optimising supplier selection and more recently, autonomously negotiating contracts, are all tangible examples.

Choosing the right path to AI  

But the real question is: where does your business start its AI journey? In our view, there are three primary paths to consider. 

First, we have AI extensions—enhancements to existing products, adding functionality without reinventing the wheel. Then there are AI solutions, typically targeted at specific industries or use cases. Finally, there are AI platforms, which act as an integration layer, connecting various workflows across your technical architecture. 

Each option has its pros and cons. Your choice will shape the costs of future innovation, the level of expertise your organisation will need, and, crucially, the longevity of your IT architecture, data security, and—most importantly—your organisational culture. 

With Gartner predicting at least 30% of GenAI projects will be abandoned after the proof-of-concept phase, organisations and more specifically, procurement and supply chain functions must recognise that AI is more than a tool. It’s the DNA of your business’s future.

AI everywhere, people-centricity nowhere

Businesses are mutating into organisms, industry convergence is everywhere, and corporate battlelines are being redrawn. However, very little emphasis has been given to human-centricity. Not in the workplace anyway. 

Procurement functions, for example, need to drive strategic value, manage both internal and external relationships, as well as sustainability. Yet, despite all this transformation, we’re still drowning in a sea of outdated procurement systems and rigid processes that treat people as cogs in a machine. There’s little thought given to how these tools affect the people using them. Procurement professionals need intuitive, user-friendly systems that let them focus on value creation rather than bureaucratic box-ticking

Whilst we have been busy teaching machines how to think, they’re teaching us to question everything. Cost-cutting, efficiency, data-governance, and the almighty bottom line. We are seeing a proliferation of new cultural norm data is no longer merely ‘collected’ or ‘feared’ — it’s now worshipped. 

Attitudes towards AI crystalise 

Data-cultures have become the norm. Cultures predicated on data are a collective mindset/practices within an organisation that define how data is handled, valued and leverage to drive decision making and innovation. 

 Are you on the right (Dovish): Do you align your company environment to Big Tech giants (Apple, Amazon, or IBM) who see data as an endless, extractive resource?

Or are you on the left (Hawkish): Do you lean into the push for data ethics (Salesforce), privacy (Motorola), and the emerging ethos of digital rights?

Gone are the days when AI was confined to optimising logistics or predicting consumer trends. Now, it’s a reflection of who we are—our biases, our ethics, and our societal hierarchies. 

Then again, that is the weird part: AI models carry the same prejudices we, as humans, can’t seem to shake. You’re not just buying a machine learning solution. In reality, you’re onboarding the assumptions and blind spots of an entire culture of developers, data scientists, and executives. And that is the crux of the problem today.

Our data shows that around 52% of software functionality in procurement and supply chain functions is never used. These are important statistics for commercial professionals looking to deploy AI and/or any software across the function. We cannot be the guardians of the bottom line if we cannot get our people to use the software we’re crying out for. 

AI success (or failure) is cultural, not technological 

If you don’t think about the cultural implications of the AI systems you’re integrating, you are profoundly at risk. Today, AI has a bit of a trust problem. Tomorrow, it will be embedding your future corporate values. And those values can make or break a company in this era where what you do with data can be as important as what you do with your product. For any sustainable competitive advantage, the birth of the inseparable triplet – Culture, data and AI is upon us, but the advantage will be with organisations that weigh them in that order. 

Remember, organisational culture is everyone’s and no one’s problem. In an age of AI-everywhere, without a robust, governed, and harmonised data-culture, people will become the problem and, honestly, AI won’t be the solution.

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