Why are Global Organisations Failing to see Gains from AI?

AI has become a key tool in supply chain planning over the recent years, being implemented across companies in order to remain flexible amid growing turbulence.

However, Boston Consulting Group finds that, despite heavy investments in AI, many businesses are not receiving meaningful returns on investment. 

Its report, Supply Chain Planning 2026: Why AI Alone Isn’t Enough, explores the biggest issues manufacturers are facing in their supply chains and the role which AI is playing in these concerns.

The role of AI

As organisations around the world have been dealing with uncertainty, turbulence and high levels of risk over the last year, supply chain leaders are looking for ways to mitigate risk and streamline operations. As a result, AI has risen in popularity as a tool, but leaders are finding that it doesn't offer the results they were promised.

Through increasing instability across global businesses and ongoing pressure to build supply chain resilience, supply chain planning is no longer a back-office function. Now, it is a core strategic capability, driving efficiency and company resilience when done correctly.

Organisations around the world have turned to investments in AI tools and advanced planning systems, with the hope of this translating to better forecasts, better decisions and more resilience, but many organisations are facing barriers with these new tools, unable to develop cohesive strategies and gain results from these changes.

To explore how businesses are implementing AI and how supply chains are responding to it, Boston Consulting Group (BCG) surveyed 181 global supply chain leaders at companies working across consumer, industrial goods, technology, media and telecommunications (TMT), energy and healthcare.

Through this, it developed its report, Supply Chain Planning 2026: Why AI Alone Isn’t Enough, which brings together key issues and concerns these leaders have.

All supply chain, procurement and logistics leaders should attend:

• Supply Chain LIVE: The Net Zero Summit - QEII Centre, London, March 4-5
• Supply Chain LIVE: The US Summit - Navy Pier, Chicago, April 21-22

Co-located with Procurement & Sustainability LIVE, these events bring together COOs, CSOs, and senior decision-makers at a moment when supply chains and commercial performance are increasingly interconnected.

A lack of maturity

Though most companies have invested significantly in some form of advanced planning system (APS), few have been able to translate these investments into consistent performance gains. Many companies that have these tools in place underutilise their capabilities and are therefore unable to see the real benefits.

Differences in organisational maturity become of higher consequence when exploring planning capabilities. Organisations which report higher maturity have a 25% higher forecast accuracy than businesses which report low-level maturity. A company witnesses better reliability, responsiveness and confidence if it has higher planning maturity. 

Maturity also differs by region and by industry. Companies with global operations have the most significant advances in planning maturity, followed by companies based in Europe, Middle East and Africa. Consumer companies report the highest maturity levels, with healthcare and industrial goods lagging behind the other industries. 

BCG finds that companies need to be able to redesign processes, build cross-functional ways of working, apply decision-led planning and embed new behaviours at scale in order to deliver excellence. If a company lacks the ability to undergo structural redesigns, it will be unable to see the benefits of new tools such as AI.

“AI can be a powerful catalyst in manufacturing supply chains, but its impact depends on how it is integrated," says Andres Garro, Managing Director and Partner at BCG, and lead co-author of report.

"The companies achieving the strongest results are embedding AI into disciplined planning processes and reliable data foundations, using it to accelerate decisions and improve performance at scale. Leaders that align technology with governance and data today will be best positioned to compete in more complex and fast changing markets.” 

Main adoption concerns

Leaders are still facing issues across their supply chains, despite widespread adoption of AI and other tools. Despite more than 70% of companies investing in APS to manage their supply chains, 78% of leaders still state that inaccurate demand forecasting is their biggest challenge. 

Part of this comes from applying it with a wrong focus. Instead of using AI to address the underlying issues within their supply chain, leaders are layering it into existing planning systems which have already proven to be inefficient. In doing this, they are spending money on tools that are not able to help them, therefore missing out on returned investments.

If companies utilise APS as an evolving infrastructure rather than a one-time implementation, businesses can unlock significant value. This way, AI and autonomous agents make APS more efficient and smarter, working alongside legacy systems as an enhancement – but only if it is positioned accurately. 

At present, AI is not being utilised properly within the supply chain, with only around 20% reporting meaningful value gained from AI so far, with as little as 7% reporting value from agentic or GenAI usage. 

Looking ahead

Leaders are recognising that one tool does not fix all. Companies which have seen the benefits within their supply chain planning are aware that investment does not close the gap if the company lacks a strong operating model. Moreover, different tools are implementing differently – though APS requires being embedded in redesigned processes, AI thrives when layered deliberately into legacy systems and processes. 

For businesses looking to gain value from their investments, they need to follow some small but significant pointers:

• Take decision-led approaches that follow designed use cases
• Develop a single version of truth by ensuring high data quality and cross-functional planning in order to improve alignment and visibility
• Move towards exception-based workflows that reduces firefighting and leads to faster decision cycles
• Clarify decision rights and forums in order to reduce fragmented accountability
• Focus on workflow redesign, training and retirement of manual spreadsheets
• Invest in targeted upskilling to help prepare teams for AI-enabled planning