One Planning Model. Four Different AI Experiences

Organizations have spent decades investing in planning technology with a simple goal: improve decision-making. Yet despite increasingly sophisticated planning models, forecasting processes, and reporting capabilities, many planning cycles still look remarkably similar to what they did years ago.

Finance teams spend significant time gathering inputs and validating assumptions. Managers work through reports to understand what changed. Executives sit through forecast reviews searching for clarity. Developers maintain and enhance the models that keep the entire process running.

Everyone has more data than ever before. Yet many organizations still struggle to turn information into action quickly enough.

That’s the paradox of modern planning. Most organizations don’t suffer from a lack of data. They struggle to make sense of it quickly enough to influence outcomes — a theme we explored in Why Planning Complexity Has Become Retail’s Next Major Challenge.

The challenge isn’t collecting information. It’s helping the right person understand what matters, why it matters, and what should happen next.

That’s where AI becomes interesting.
Not because it can generate content.
Not because it can summarize a report.
But because it has the potential to fundamentally change how people interact with planning data.

As AI becomes increasingly embedded in IBM Planning Analytics, I believe we’re asking the wrong question. Most conversations focus on what AI can do. The more important question is:

Who is AI helping?

Because a CFO, an FP&A manager, a sales leader, and an IBM Planning Analytics developer may be working from the same planning model, but they’re trying to accomplish very different things.

The future won’t be defined by a single AI experience. It will be defined by many.

The CFO: from reviewing reports to making decisions

Most finance leaders don’t need another dashboard, another report, or another meeting to review the report. They already have access to more information than any previous generation of finance executives. What they need is clarity.

They need to understand what changed, where risk is emerging, and which decisions require attention. Historically, answering those questions has required navigating layers of reports, spreadsheets, emails, meetings, and follow-up analysis. By the time leaders developed a complete picture, the opportunity to influence the outcome was often already shrinking.

Imagine instead starting the day with a conversation rather than a report. A CFO could ask:

• What changed in the forecast since last week?
• Which assumptions pose the greatest risk to achieving our operating income target?
• What decisions should leadership be discussing today?

The value isn’t that AI creates another summary. The value is that it reduces the time between identifying an issue and acting on it. Planning has always been about supporting decisions. AI has the potential to make that support immediate, contextual, and accessible when leaders need it most.

The FP&A team: spending less time preparing and more time advising

If any group is positioned to see immediate benefits from AI, it’s FP&A.

Many finance teams still dedicate a large portion of their time to preparing information rather than interpreting it. Data collection, validation, commentary creation, presentation development, and recurring management requests consume valuable cycles that could otherwise be spent partnering with the business. Those activities are necessary, but they aren’t where finance creates its greatest value.

The real value comes from helping the organization understand what the numbers mean, why they changed, and what actions should follow. Imagine being able to ask:

• What are the largest forecast variances this month?
• Which drivers are contributing most to margin erosion?
• Which business units require management attention?
• Given recent trends, which scenarios should we evaluate?

These questions aren’t new. Finance organizations have always sought these answers. What’s changing is how quickly they can get them.

The opportunity isn’t to replace FP&A professionals. It’s to eliminate much of the administrative effort surrounding planning so finance teams can spend more time influencing decisions and less time preparing materials. In many organizations, that shift alone could fundamentally change how finance partners with the business.

The business user: making planning insights more accessible

One of the most overlooked audiences in discussions about AI and IBM Planning Analytics is the business user.

Regional leaders, sales executives, operations managers, and department heads often understand their part of the business better than anyone else. They know their customers, markets, products, and operational challenges. What they don’t necessarily know is how a multidimensional planning model works. And frankly, they shouldn’t have to.

For years, we’ve expected business users to learn the language of planning applications before they could access meaningful insights. They needed to understand dimensions, hierarchies, reports, cubes, and navigation structures simply to answer relatively straightforward business questions. But what if the software adapted to the user instead?

Imagine a sales leader asking why revenue forecasts are declining in a specific region. Or an operations manager asking what’s driving inventory growth next quarter. Or a department leader asking what changed since the last forecast submission. Those questions shouldn’t require specialized technical knowledge. They should simply require curiosity.

The goal isn’t to turn every manager into an IBM Planning Analytics expert. The goal is to make planning intelligence available to everyone who needs it. When users can interact with planning data as naturally as they interact with colleagues today, the barrier between insight and decision-making begins to disappear.

The IBM Planning Analytics developer: from builder to architect

Developers have a different relationship with IBM Planning Analytics.

Their days are often filled with writing rules, building TurboIntegrator processes, troubleshooting issues, maintaining standards, documenting solutions, and supporting users. All of these activities are essential, but they can leave less time for the work that creates the greatest business value.

The best IBM Planning Analytics developers have never been defined by how much code they write. They’re defined by how effectively they translate business challenges into scalable, maintainable solutions. That’s where AI becomes particularly compelling. Imagine asking:

• Why is this rule producing this result?
• Document this process for future support teams.
• Identify optimization opportunities within this model.
• Design a workforce planning solution based on these requirements.

The goal isn’t to eliminate the developer. The goal is to elevate the developer.

By reducing the time spent searching for answers, documenting repetitive work, or performing routine administrative tasks, AI can create more capacity for architecture, design, innovation, and business partnership. The most valuable developers have always been solution architects first and technologists second. AI simply gives them more time to operate at that level.

The bigger opportunity

For years, planning success has been measured through familiar metrics such as forecast accuracy, cycle time, process efficiency, and model performance. Those metrics will continue to matter. But they were never the objective.

No executive wakes up wanting a more efficient forecast process. No sales leader asks for a better cube structure. No finance team pursues planning excellence simply for the sake of planning. The real objective has always been better decisions.

The organizations that create the greatest value from AI won’t necessarily be the ones with the most sophisticated models. They’ll be the ones that make insight easier to access, reduce friction between questions and answers, and help people act with greater confidence.

That’s why I don’t believe the future of IBM Planning Analytics is a single AI-powered dashboard, chatbot, or assistant. It’s an ecosystem of experiences designed around the people using the platform.

A CFO should engage with planning data differently than an FP&A analyst. A sales leader should have a different experience than a developer. An operations manager should interact with the platform differently than an executive. The planning model may remain the same, but the questions, context, and decisions are fundamentally different depending on who’s engaging with it. And that’s exactly what makes the opportunity so powerful.

The future of AI in IBM Planning Analytics isn’t about replacing people. It’s about amplifying them. Helping leaders make decisions faster. Helping finance teams provide stronger guidance. Helping business users access insight without barriers. Helping developers spend more time solving business problems and less time maintaining technology.

The planning model becomes the foundation. AI becomes the interface. Better decisions become the outcome.

If this topic is of interest to you

If this topic is of interest to you, I encourage you to join me and the Cubewise team at IBM TechXchange 2026.