Demand AI: building trust in AI-driven planning

This scenario is becoming increasingly common as organizations embrace artificial intelligence in their supply chain operations. While the promise of improved accuracy and efficiency is compelling, the ”black box” nature of AI systems presents a unique challenge: How do we trust something we can’t fully understand?

The stakes are high. According to recent implementations, AI-driven demand planning can significantly improve forecast accuracy and reduce manual workload. Yet, the path to achieving these benefits is paved with change management challenges and trust-building requirements.

In any AI solutions, there’s a notorious problem: the (lack of) transparency of decision-making processes within neural networks. Unlike traditional statistical models, where you can trace every step of the calculation, deep learning networks operate through complex layers of weighted connections – much like our own brains.

”If we just let AI run wild, it can hallucinate,” explains Benjamin Obling, a longstanding expert in integrated business planning at Roima Intelligence.

”Sometimes it will predict exponential growth or completely unrealistic numbers because it’s picked up patterns we can’t see. That’s why we need fences.”

These ”fences” are critical control mechanisms that ensure AI predictions remain within realistic bounds while maintaining their predictive power. Think of them as guardrails that keep the system on track while allowing it to leverage its full analytical capabilities. Guardrails designed and implemented by you and your demand planning team – based on your experience.

Yet, even with deeply involved S&OP teams, trusting AI forecasts is still difficult.

Maybe it’s because planners feel threatened by this emerging technology. Feel they will soon become obsolete?

However, AI adoption in demand planning isn’t about replacing human expertise, but about adjusting it. Modern implementations show us that the most successful approaches combine AI’s computational power with human business intelligence.

Here’s what this looks like in practice:

• AI handles the heavy lifting of pattern recognition and basic forecasting
• Human planners focus on strategic decisions and market intelligence
• The system flags unusual patterns or predictions for human review
• Market knowledge and business context inform final decisions

”There are things that the algorithms or the network cannot know. For example, new product introductions, upcoming promotions, or even external events... It wouldn’t have a clue about these,” Obling says. That will continue to be the tasks assigned to the demand planner; understanding the business strategy and the dynamics in the market intelligence.

The key to successful AI adoption lies in a carefully structured implementation approach. Here are three imperatives leading organizations are doing right:

• Start with parallel running: Run your AI system alongside existing processes initially. This allows teams to validate outputs and build confidence without risk.
• Create clear intervention points: Define specific triggers that prompt human review. This might include unusual pattern detection or significant deviations from historical trends.
• Maintain transparency: While the AI’s internal workings might be complex, its outputs and recommendations should be clear and explainable to stakeholders.

Really, it’s about introducing AI in your demand planning gradually and with high levels of confidence at each level – and for each team member. If you fail to secure business-wide trust in the AI predictions, you will have a hard time reaping the benefits.

Perhaps the most exciting development is how AI is transforming the demand planner’s role. Instead of spending hours crunching numbers and maintaining spreadsheets, planners can focus on higher-value activities:

• Strategic market analysis
• New product introduction planning
• Promotional impact assessment
• Cross-functional collaboration

”We’re not removing the demand planner’s role,” notes Obling. ”We’re elevating it from data manipulation to strategic business planning.”

Obling emphasizes that your demand AI needs to be taught and trained to react in a favorable way that suits your business.

”The network and the automated prediction will pick it up eventually anyway. If demand increases, it’ll also increase, but the question is, do we increase it right away? That’s more a business decision that an AI model can’t tell you.”

As AI continues to evolve, the challenge will not be technical implementation but rather building and maintaining trust in these systems.

Organizations that successfully navigate this challenge will find themselves with a powerful competitive advantage: the ability to make faster, more accurate decisions while maintaining human oversight where it matters most.

The future of demand planning includes both human expertise and artificial intelligence, and the winners will find the sweet spot where both work together, each doing what they do best. The demand AI race is already on, now it’s about getting out on the other side alive and with both feet on the ground.