Why AI Alone is Failing to Fix the Supply Chain Issue

Artificial intelligence has become a key tool in supply chain planning to maintain flexibility amid growing turbulence. However, Boston Consulting Group (BCG) finds that despite heavy investments, many businesses are not receiving meaningful returns.

Its report, Supply Chain Planning 2026: Why AI Alone Isn’t Enough, explores the issues manufacturers face. As organisations deal with global uncertainty, AI has risen in popularity, yet leaders find it often fails to deliver promised results.

Supply chain planning is no longer just a back-office function; it is now a core strategic capability driving efficiency and resilience. While organisations invest in AI and advanced planning systems (APS) hoping for better forecasts, many face barriers.

BCG surveyed 181 global supply chain leaders across various sectors, including consumer, industrial goods and technology. The report highlights that without cohesive strategies, investments fail to translate into performance gains.

Impact of organisational maturity

Though most companies have invested in APS, few translate these into consistent gains. Many underutilise capabilities and miss real benefits.

Organisational maturity is crucial; those with higher maturity report 25% higher forecast accuracy than low-maturity businesses. Higher planning maturity leads to better reliability and responsiveness.

Companies with global operations show the most significant advances, followed by those in EMEA. Consumer companies lead in maturity, while healthcare and industrial goods lag behind.

BCG notes that companies must redesign processes and build cross-functional working methods. They must apply decision-led planning to deliver excellence. Without structural redesigns, the benefits of tools like AI remain out of reach.

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

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.

Challenges in demand forecasting

Despite widespread AI adoption, leaders still face supply chain issues. While more than 70% of companies invest in APS, 78% of leaders cite inaccurate demand forecasting as their biggest challenge.

This often stems from the wrong focus. Instead of addressing underlying issues, leaders layer AI into inefficient existing systems. Consequently, they spend money on tools that cannot help, missing out on returns.

If companies utilise APS as an evolving infrastructure rather than a one-time implementation, they could unlock significant value. AI and autonomous agents can make APS smarter, but only if positioned accurately.

Currently, AI is not utilised properly within the supply chain. Only around 20% report meaningful value from AI so far, with just 7% reporting value from agentic or gen AI usage.

Strategies for unlocking value

Leaders are recognising that one tool does not fix all challenges. Investment does not close the gap if a company lacks a strong operating model.

While APS requires embedding in redesigned processes, AI thrives when layered deliberately into legacy systems. For businesses looking to gain value from their investments, they need to follow some 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 to improve alignment.
• Move towards exception-based workflows that reduce firefighting and lead to faster decision cycles.
• Clarify decision rights and forums 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.