In boardrooms today, the word AI is everywhere. Dashboards are called AI-powered. Forecasts are called AI forecasts. Even a trendline in Excel can be labelled artificial intelligence.

When everything is called AI, nothing truly is. Blurred definitions make it harder for boards to see real opportunities, know where to invest, or what risks to expect.

This is why differentiating predictive analytics from AI matters. It’s not about downplaying either, but recognising their different strengths so both can deliver maximum value. Predictive analytics provides steady, explainable gains; AI opens new frontiers but brings new risks and additional governance. Clear separation helps us invest in both wisely.

Boards fund outcomes, not labels | Unless there’s hype

Boards fund business outcomes; like forecasting catastrophes or expenses; not common technology labels. Hype shifts focus from outcomes to the technology, leading us to search for problems to fit the solution.

And it’s easy to see why when we hear of NVIDIA projecting $3 – 4 trillion in AI infrastructure spending by 2030, and IBM reporting 61% of CEOs investing in AI agents at scale.

Remember the Big Data hype and failed Hadoop programs? Many solutions ignored business outcomes until it was too late.

With today’s AI hype, there’s a perceived inherent business outcome. AI gets funded, and projects are painted with the AI brush. Gartner estimates 65% of AI projects will be abandoned through 2026 due to a lack of AI-ready data. Grouping all projects under one AI umbrella only fuels future distrust should they fail.

Decades of forecasting

Predictive analytics isn’t new. Actuaries, analysts, and forecasters have used it for decades, from loss reserving and catastrophe modelling to yield curves, options pricing, and credit scoring.

These roles built trusted methods that delivered clear business value. The distinction mattered because it set expectations for what their work could achieve. They weren’t called AI specialists, nor was reserving called AI. But why does that matter?

Why keep them separate?

AI offers incredible benefits but also risks and considerations beyond analytics. Predictive analytics is proven. Explainable, repeatable, and often regulated, it has underpinned planning, pricing, and risk management for decades.

AI brings something different. Adaptive and creative, it can automate tasks in new ways, but carries risks around bias, opacity, and oversight.

When we confuse the two, predictive analytics inherits AI’s “baggage.” Simple forecasts suddenly need AI-level governance. Confidence in reliable tools erodes. Progress slows not because methods fail, but because they’ve been mislabelled.

And that “baggage” is significant. AI governance requirements include:

• GDPR Article 22: Automated decisioning — Systems making decisions without human intervention must explain logic and consequences, allow appeals, and avoid discrimination.
• EU AI Act: Classifies AI by risk. High-risk systems like credit scoring or employee monitoring must be transparent, maintain governance logs, and undergo assessments.
• UK AI Regulation (ICO): Currently principles-based, focusing on explainability, bias audits, data minimisation, and legal basis for profiling.
• FCA, PRA, EU DORA: Model Governance – AI models must be versioned, monitored, retrained, and traceable.

The lesson isn’t to stop, but to be precise. Predictive analytics should keep delivering steady benefits. AI should be funded where adaptability and scale can make a real difference. Boards need clarity to strike that balance.

From analytics to AI

Analytics often follows a maturity path:

• Descriptive: what happened
• Diagnostic: why it happened
• Predictive: what’s likely next
• Prescriptive: what we should do

Each stage adds value, but none automatically qualifies as AI. Analytics uses historical data to forecast outcomes. AI goes further: it makes decisions and generates outputs without explicit input. Predictive models tell you what’s likely to happen; AI decides what to do. AI can be placed at each layer of this maturity path; it does not mean AI is a step on this path.

As an example, over a decade ago, I wrote about “Many-To-Many Currency Conversions in Microsoft SSAS,” which ran complex FX conversions. They were clearly descriptive analytics.

AI enters when systems learn, adapt, and optimise without explicit programming. For example:

• Adding currency forecasting to that work = predictive analytics.
• An AI system autonomously adjusting forecasts in real time from new data, without manual intervention = AI.

So:

• Descriptive, diagnostic, and predictive analytics are not AI, though AI can enhance them.
• Prescriptive analytics sometimes overlaps, especially when recommendations become automated decisions.

The point isn’t to place AI on the ladder but to see it as an enhancer at any stage. Predictive analytics remains valuable and distinct; AI brings different capabilities that build on it.

Speaking in business outcomes

It’s tempting to blur the terms for investment. But boards don’t want algorithms or ladders explained; they want outcomes:

• What problem does this solve?
• How confident are we in the result?
• What capability does this unlock?

Scrap the AI label, focus on outcomes. Instead of:

• “AI claims engine” (likely just a predictive model); say:
• “A forecasting tool that improves reserve accuracy by 15%, helping us hold less capital without increasing risk.”

This simple shift builds credibility and guides smarter investment. It avoids unnecessary AI governance, reduces overhead, and raises the chance of success and ROI. If you want dynamic, intelligent automation layered on top, that’s when AI deserves separate investment.

Final thought

Predictive analytics builds trust through its track record; AI requires governance to earn it. Clear separation helps boards set expectations and fund both appropriately. Both have their place: forecasting is steady and proven, while AI is adaptive and transformative.

Data leaders must help boards see the difference, avoiding slowed proven approaches or oversold emerging technologies.

Clarity isn’t just language. It ensures the business gets full, distinct value from both analytics and AI.

References:

• https://www.reuters.com/business/nvidia-ceo-says-ai-boom-far-over-after-tepid-sales-forecast-2025-08-28/

• https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-ceo

• https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk

Note: The views and opinions expressed in this article are the author’s own and do not represent any company’s position or opinion.

About the Author: 

David Tuppen is Chief Data Officer at Enstar Group, where he leads data platform modernization and drives enterprise-wide transformation to strengthen decision-making, governance, and operational performance. His focus is on enabling business value through scalable, well-governed data solutions that align with regulatory requirements and support long-term growth.

Tuppen has held senior leadership roles at organizations including Milliman, Wipro, and Athene, with a particular focus on Data, AI, and finance transformations in the BFSI sector. He also holds a Master of Science in Information Management.