If You Can’t Explain It, You Can’t Deploy It: The New Standard for Enterprise AI Forecasting

Retailers lose more than $1.7 trillion every year to inventory distortion. Even Nike, with world-class analytics, ended up with a public glut of unsold shoes. The math was not the problem. The issue was explainability. 

Enterprise buyers rarely reject AI because the algorithms are wrong. They reject it when no one can defend a number in the boardroom. If a forecast cannot be traced, explained, and reproduced, it will not pass procurement, audit, or budget review. That lack of trust, not a lack of models, is what stalls adoption. 

What Explainable Forecasting Means 

Explainable forecasting means every number is paired with context: 

• Confidence levels: a confidence range that helps leaders price risk.

• Top drivers: the factors that matter most, with direction and weight.

• Anomaly flags: alerts that highlight results needing review.

• Readable change log: a simple record of updates, overrides, and data shifts.

• Reproducible runs: forecasts that can be recreated when questions arise.
   

This is not an academic standard or a compliance checklist. It is a way to make better decisions. The NIST AI Risk Management Framework calls out transparency and explainability as essential, and for good reason. People act on what they understand. 

Five Common Failure Modes 

1. No confidence levels. Point estimates encourage overconfidence and leave finance teams guessing.
    
2. Opaque drivers. If planners cannot see what moves the forecast, they override it without oversight.
    
3. Glossy UI over a black box. A chatbot interface without governance will not pass an audit.
    
4. Unmanaged overrides. If changes are undocumented, no one can learn from mistakes.
    
5. Irreproducible training. Without versioned data and models, last quarter’s forecast cannot be explained.
    

A Simple Checklist for Enterprise AI Forecasting Teams 

• Add confidence levels to every forecast.

• Show the top five drivers with clear direction and weight.

• Keep a human-readable change log for all updates, retrains, and overrides.

• Treat forecasts like code: version, review, and audit them.

• Set override rules and run regular postmortems.

• Measure results in cash outcomes: buys avoided, stockouts prevented, service level, and days inventory outstanding.
   

A Real-World Example 

Kenvue, the global consumer health company behind brands like Tylenol and Band-Aid, faced persistent demand volatility across key SKUs. Forecast misses were driving excess inventory in some regions and stockouts in others, eroding service levels and tying up capital. 

By shifting to explainable forecasting, Kenvue’s planners could see not only SKU-level predictions but also the drivers behind them, for example, promotional lift, seasonal patterns, and distribution bottlenecks. Confidence intervals and anomaly flags helped finance leaders challenge assumptions early. 

The results were tangible: forecast accuracy improved by 37 percent, stockouts dropped, and planners recovered millions in working capital. Just as important, finance and supply chain leaders gained confidence in the system because every override was logged and every material shift could be explained. What had been a contentious monthly debate became a repeatable decision-support process. 

Why This Matters Now 

Generative AI adoption is high, but governance and explainability remain sticking points; McKinsey’s 2024 global AI survey highlights inaccuracy and explainability risks and notes few organizations have governance practices in place. The EU AI Act has raised expectations across industries, even where rules do not yet apply.  

CFOs and CIOs are asking harder questions. Procurement teams now treat AI systems like regulated financial tools, demanding reproducibility, data lineage, and documented overrides. Budgets are tighter, and every black-box decision invites scrutiny. Vendors who cannot show their work are losing deals, while those who prioritize explainability are becoming strategic partners. 

How to Get Started 

• Week 1: Add confidence levels to dashboards.

• Week 2: Surface the top drivers that explain most variance.

• Week 3: Build a simple change log showing when and why numbers moved.

• Week 4: Introduce override policies and review every miss as carefully as every win.
   

These steps create a baseline of trust that helps technical teams and business leaders align. By the end of a single planning cycle, leaders can see a measurable reduction in uncertainty and faster sign-off on procurement and inventory decisions. 

The Payoff 

Cleaner buys. Fewer stockouts. No last-minute fire sales to clear mistakes. Teams move from intuition to repeatable practice. Debates shift from hunches to evidence. Every override becomes a learning opportunity. 

For founders and product teams, this is the core lesson: explainability is not a feature. It is the product. I tell my team often that if a system cannot show its work, it will never make it through procurement. Enterprises do not just want forecasts, they want answers they can defend. Companies that deliver that level of visibility will be the ones that scale. 

Author bio:

Dr. Zohar Bronfman is the CEO and co-founder of Pecan AI. He holds dual PhDs in computational neuroscience and philosophy of AI.