Why the Current Innovation Model is Under Pressure: Part 3 - Digital, Data and AI Fitness

Spend a few minutes on LinkedIn or browsing vendor websites and you will see bold statements about how AI will transform supply chains. Planning will be automated, warehouses will run themselves and real-time data will give leaders complete visibility. It is a compelling story. Yet when I ask supply chain leaders what is actually happening in their organisations, the picture is much more hesitant. Pilots are common, scaled deployments are rare and the gap between ambition and reality keeps widening.

One director in our network put it starkly: “AI is not the problem. We are.” It is not that the technology does not exist. The problem is that many organisations are not fit to use it. In a recent conversation one senior executive described it as becoming “AI fit.” Like training for a marathon, it requires progression through stages. You cannot sprint if you have not yet learned to walk. Fitness is needed at three levels: organisational, governance and people. Many firms try to leap to advanced analytics or generative AI without first building the basics and the result is predictable disappointment.

The Skills Gap

The first barrier is people. Supply chains are full of operational expertise. They know how to run warehouses, optimise logistics and manage suppliers. But they are much thinner on hybrid roles that sit between technology and operations. Data engineers, product owners and digital translators remain in short supply.

Gartner has found that fewer than 20 percent of supply chain leaders believe they have the right mix of digital skills in their teams¹. BCG’s research points in the same direction, showing that two-thirds of digital transformations stall primarily because of gaps in people and capabilities rather than shortcomings in technology².

Leaders I have spoken to describe the same pattern. They can start an AI pilot with the help of consultants or vendors but struggle to embed it because the in-house expertise is not there. Without teams who can manage data pipelines, validate models and integrate tools into workflows, a proof of concept becomes a stranded success.

Data Foundations

Even when skills are present the data itself is often the bigger barrier. Clean, consistent and accessible data is the raw material of digital transformation. Most companies know this but many still run on fragmented systems and spreadsheets.

McKinsey has found that poor data quality is one of the most common reasons AI projects fail³. The World Economic Forum has described data as the new oil of supply chains but noted that only a minority of firms have the pipelines to refine it properly⁴.

One supply chain director told me that their dashboards looked impressive but were built on flawed data sets. “It was garbage in, glossy chart out,” they said. The visuals were polished but the underlying numbers were wrong and the result was a rapid loss of trust. Once credibility is damaged it is hard to recover and boards quickly grow sceptical about further investment.

The AI Adoption Gap

AI has become the emblem of digital ambition in supply chain. Surveys show that nearly two-thirds of leaders see AI as the key to lowering costs and increasing resilience. Yet only around 1 percent report that they have been able to fully automate processes with AI or robotics⁵.

That figure rarely surprises practitioners. Most firms are somewhere between curiosity and pilot stage. A handful of proofs of concept run in demand forecasting or inventory optimisation but scaling them across the enterprise remains rare.

Culot and colleagues reviewed AI adoption in supply chain and concluded that the barriers are less about algorithms and more about readiness. Integration with existing systems, governance structures and data consistency remain the weak points⁶. In Europe the challenge is compounded by underinvestment and talent shortages. Bruegel has argued that the continent lags the US and China because of fragmented regulation and limited funding for scale-ups⁷.

Risk Appetite and Adoption Hesitation

Another reason adoption stalls is risk. Directors talk about the career-defining stakes of large programmes. If it works you are the hero. If it fails you may be out of a job. This risk aversion means that even when leaders recognise the need for digital investment they hesitate to champion it.

That hesitation is reinforced by the legacy of previous failed or underwhelming programmes. When boards have seen millions spent on ERP or analytics with modest returns they become reluctant to approve another wave of spending. The perception of risk overshadows the promise of reward.

The People Dimension

Perhaps the most overlooked factor is people. Technology is often framed as a way to reduce headcount. In executive discussions the first question is sometimes, “How many people can I have less?” As one leader pointed out that framing is unhelpful. The better question is how to scale the business with the same workforce.

Becoming AI fit does not mean replacing staff with algorithms. It means equipping people to use digital tools so they can do more. That shift in mindset is not yet mainstream. Employees fear replacement while boards hope for cost savings. Very few companies strike the balance of using technology to augment rather than replace.

There is also the question of human judgment. AI is already better than people at pattern recognition and statistical prediction. What it lacks is empathy and contextual understanding. In retail, logistics and supply chain those human elements remain critical. A book that has influenced many in our network, Power and Prediction, makes this point clearly. Prediction is the machine’s strength. Judgment about what to do with those predictions still belongs with people. Without human input, even the best algorithms risk producing irrelevant or damaging outcomes.

Digital Fluency at Leadership Level

Even when staff are willing to adopt new tools, digital fluency at leadership level is often missing. In one pilot, executives were asked to review agile backlogs but did not understand how to engage with them. The initiative defaulted back to IT.

Accenture has shown that companies where business leaders own digital initiatives are twice as likely to succeed⁸. But ownership requires confidence. Most supply chain directors did not train as data scientists. They came up through warehouses, distribution centres and commercial roles. They know the business inside out but that does not automatically prepare them for digital decision-making. They do not need to code but they do need enough fluency to evaluate what digital proposals mean for their operations. That gap is still wide.

The Investor Lens

Investors see both the promise and the frustration. Private equity firms regularly cite digital transformation as a lever for value creation but acknowledge the execution risk is high. Many investors prefer to back startups that focus on specific technologies rather than wait for large corporates to deliver their own programmes.

The irony is that corporates already have the scale to benefit most from digital. What they lack is the readiness to deploy it. This gap between potential and execution is one of the defining features of the current innovation model.

Why It Matters

The danger is that digital transformation becomes a cycle of over-promising and under-delivering. Every failed pilot or delayed programme erodes confidence. Staff become sceptical, boards cautious and investors impatient.

Yet the upside remains significant. McKinsey estimates that companies which embed digital in their supply chains can reduce costs by up to 20 percent while improving service levels⁹. The challenge is not the technology. It is whether organisations can prepare themselves to capture the benefits.

Conclusion

The story we hear from supply chain leaders is consistent. Digital ambition runs ahead of digital fitness. The technology is there but organisations are not yet ready to use it at scale. Skills are lacking, data is fragmented, adoption is slow and leadership fluency is uneven.

Until firms become genuinely AI fit, digital transformation will remain more promise than performance. The lesson is that innovation does not fail because the tools are inadequate. It fails because organisations try to sprint before they have learned to walk.

JP Doggett

Sources

1. Gartner, Supply Chain Digital Transformation and Talent Survey, 2022
2. Boston Consulting Group, Flipping the Odds of Digital Transformation Success, 2020
3. McKinsey & Company, Why Data Quality Matters for AI Adoption, 2021
4. World Economic Forum, Digital Transformation of Supply Chains, 2022
5. World Economic Forum, From Disruption to Opportunity: Strategies for Rewiring Global Value Chains, 2023
6. Culot, Giovanna et al., Artificial Intelligence in Supply Chain Management: A Systematic Literature Review, Journal of Purchasing and Supply Management, 2024
7. Hoffmann, Max, What is Holding Back AI Adoption in Europe, Bruegel Policy Contribution, 2024
8. Accenture, The Art of AI Maturity: Advancing from Practice to Performance, 2022
9. McKinsey & Company, Digital Supply Chains: Creating Value, Building Capabilities, 2020