Which Industries Use AI for Business Planning Most? (2026)

Yet AI adoption is not the same across every sector. Some industries are using AI for business planning at a remarkable pace, while others are still exploring its potential. Understanding where AI is making the biggest impact can reveal how businesses are gaining a competitive edge and what the future of planning may look like.

In this guide, we'll explore the industries using AI for business planning the most in 2026 and discover how they're putting it to work.

Introduction — The Gap Between "We Use AI" and "We Plan With It"

Everyone seems to be talking about AI in business planning. But here's where things get interesting.

McKinsey's 2025 State of AI report says that 88% of organizations now use AI regularly in at least one function, up from 78% the year before. Meanwhile, Federal Reserve analysis of Census Bureau data found that only about 18% of U.S. firms had adopted AI by the end of 2025. That's a huge gap!

So who's right? Both.

The reason is simple: they are measuring different things. And that gap between "we use AI" and "we plan with AI" is exactly what this article is about.

Think of AI adoption as three layers.

• Layer 1: Tool Access

Someone uses AI for a prompt, a quick assist, or a simple task. The tool is available, but nothing really changes.

• Layer 2: Workflow Embed

AI becomes part of a recurring process such as forecasting, reporting, budgeting, customer analysis, or planning workflows.

• Layer 3: Decision Integration

AI output helps shape board packs, capital allocation decisions, hiring plans, and larger business strategy discussions.

Here's the problem: most AI adoption statistics 2025 combine all three layers into the same bucket. As a result, a CEO who ran a single prompt last quarter can appear in the same survey category as a finance team that closes its books with AI agents. That's why the headline numbers can feel confusing. They tell us who is using AI, but they don't always tell us how deeply AI is embedded in the business.

Before we look at the industries using AI the most, try a quick test: Which layer describes the deepest level of AI use in your company today?

If the answer is Layer 1, you're statistically average. If the answer is Layer 3, you're in the top 6% of firms, where AI is already helping guide important business decisions.

And that distinction changes everything. Because when AI starts influencing decisions instead of simply assisting tasks, it becomes part of the company's AI business strategy. That's where the biggest shifts are happening, and it's exactly where we'll focus next.

The Three Industries That Have Wired AI Into Business Planning

Not every industry is adopting AI at the same speed.

After twelve months of cross-survey data, three industries consistently rank at the top for worker usage, firm-level adoption, and planning function penetration: Information, Financial Services, and Professional Services.

Why these three? Because their core work revolves around documents, models, and structured analysis, exactly the kind of work AI handles best.

Industry Reference Table

Let's see what makes the top three different.

A. Information Sector — The Native Adopters

The Information sector includes software companies, publishers, telecoms, and data services.

Here, AI is often treated as a baseline operating expense, not a special project. That helps explain why 70% of workers use GenAI at work, the highest rate of any tracked sector, while firm-level adoption has reached 37%.

The product itself is software or data. AI simply extends existing engineering practices instead of requiring a new discipline or a separate budget.

For many of these companies, AI in business planning is part of the product roadmap itself. Strategic plans, financial models, and competitive analyses are generated, tested, and improved inside the same tools used to build the product.

B. Financial Services — The Compounding Adopters

Banks, insurers, and asset managers run on structured data and regulated decisions, making them a natural fit for AI.

Today, 63% of finance-sector workers use GenAI at work, while firm-level adoption has reached 30%. Gartner also found that 59% of CFOs now use AI inside the finance function, up from 37% in 2023. The most common uses are knowledge management (49%), accounts payable process automation (37%), and error or anomaly detection (34%).

The biggest advantage is that the gains keep building. A bank that automates KYC review keeps that improvement and adds the next one on top. Then the next.

Regulatory pressure makes the case even stronger because every AI capability that lowers compliance costs can create an immediate, auditable ROI.

Still, adoption is not effortless. About 25% of finance organizations are unsure how to move from planning a pilot to actually running one. Data literacy and data quality remain the biggest barriers.

C. Professional Services — The Document Factories

Law, accounting, consulting, and engineering services all have something in common: text and analysis are the deliverable. That's why AI fits so well.

Federal Reserve data shows that 62% of workers use GenAI at work, while firm-level adoption has reached 33%, second only to the Information sector in the Census BTOS panel.

The real impact is economic.

For years, billable hours limited how much work a professional could produce. AI moves that cap upward. Firms that bill for output rather than hours could reshape the industry's economics, and those that move first may gain a durable structural advantage.

For founders, this trend should feel familiar.

Every time you use AI to build a strategic plan, evaluate competitors, model different scenarios, or test business assumptions, you're doing the same type of work that consulting, accounting, and advisory firms have traditionally charged clients to perform.

That's why Professional Services has become one of the fastest-moving sectors for AI adoption.

AI compresses time-to-output for exactly this type of work. Tasks that once required days of research, analysis, and document creation can now move much faster.

In other words, many founders are already operating inside the professional-services model. The difference is that the analyst, researcher, and strategist increasingly live inside the same AI-powered planning workflow.

The Middle Tier, the Quiet Movers, and the Floor

The top three industries get most of the attention, but the next tier tells an equally interesting story.

Real estate, wholesale trade, and manufacturing are climbing faster than their industry labels suggest. At the other end of the spectrum, the lowest-ranked sector sits almost exactly where intuition would place it.

A. The Quiet Movers (Real Estate, Wholesale, Manufacturing)

These industries may not be viewed as AI leaders, but the data suggests they're moving in that direction.

Real Estate has reached 58% individual GenAI work usage. That may sound surprising until you consider how much of the industry revolves around leases, contracts, reports, and property analysis. The work is document-heavy, giving AI many of the same advantages it has in professional services, just without the same headlines.

Wholesale Trade has reached 48% individual usage. Vendor agreements, RFP responses, and demand planning all fit naturally with language models. Adoption often goes unnoticed because most of the benefits happen behind the scenes, inside planning and operational workflows rather than customer-facing products.

Manufacturing may be the most important sector to watch. According to Federal Reserve data, GenAI work adoption grew 58% year over year, the fastest growth rate in the entire panel. Firm-level adoption remains relatively low, but the trajectory is steeper than any other industry tracked.

Interestingly, the earliest wins are not happening on the production floor. They're appearing in contract review, demand forecasting, and back-office finance, where AI can work directly with documents, forecasts, and structured business data.

B. The Floor — Accommodation and Food Services

At the bottom of the rankings sits Accommodation and Food Services, with 21% individual GenAI work adoption and 8% firm-level adoption, the lowest levels tracked in the Census BTOS panel.

The reason is largely structural.

In many industries, employees spend their time creating documents, analyses, forecasts, or plans. In hospitality and food service, the core job is often delivering a service in real time. AI can help write a report faster. It cannot easily shorten the moment a barista hands over a coffee or a server delivers a meal.

That doesn't mean AI is absent from the sector. The biggest gains are happening away from the service floor. Marketing content, demand forecasting, supplier contract review, and back-office financial planning are becoming common AI use cases.

In other words, AI adoption is real in hospitality and food service. It simply shows up in the planning function far sooner than it shows up in the customer experience.

Why 88% Adoption Still Means Very Little — The Bitter Pill

By now, one thing should be clear: adoption and impact are not the same thing.

In fact, the most important insight in the last twelve months of AI data isn't how many companies use AI. It's how few are turning that usage into meaningful business results.

Adoption-to-Impact Gap Table

The message is hard to ignore.

Eighty-eight percent of companies use AI somewhere. Only 39% report any EBIT impact. Just 6% have reached the level McKinsey classifies as high performers.

That's the difference between using AI and building an AI business strategy around it.

A. Pilot Purgatory

This is where many companies get stuck.

A team launches a pilot. The pilot works. Everyone is excited. Then nothing happens. Nine months later, the tool still isn't part of daily operations, no workflow has changed, and no headcount has been reallocated around the new capability.

Gartner found that 91% of finance teams report only low or moderate impact after launching an AI pilot, even when the pilot itself succeeds.

Why? Because many pilots are treated like experiments instead of business initiatives.

The solution is simple: treat pilots as production work from day one.

Before launching, define the EBIT line the project is expected to improve. Assign a named owner with budget authority. Write the rollback procedure before implementation.

Without those three elements, a pilot is not a business project. It's a research project.

B. The Adoption-as-Metric Trap

"We use AI in 80% of our functions." It sounds impressive. It also tells the board almost nothing.

Adoption percentages reward license purchases and tool access. They do not measure whether workflows have changed or whether the business is producing better results.

The gap between McKinsey's 88% adoption figure and its 6% high-performer figure captures the entire problem. Most organizations can say they use AI. Very few can prove it is moving the business forward. That's why adoption should never be the primary metric.

AI should be measured the same way any capital allocation decision is measured: by results.

Track EBIT contribution by workflow, not seats by department.

For founders using AI for strategic planning, competitive analysis, or decision-making, the real question is not "How many people are using AI?" The real question is: How much value is AI creating?

That mindset matters because McKinsey found that nearly two-thirds of organizations have not yet started scaling AI across the enterprise. Most are still experimenting, testing ideas, or running pilots. And that brings us back to the number that matters most.

Not 88%. The 6% of companies that have already turned AI adoption into measurable business impact.

Conclusion — The 6% Question

After all the statistics, rankings, and industry comparisons, one question matters more than any other: Has AI changed the way your company plans and makes decisions?

The question is not whether your industry is adopting AI. By now, the answer is almost certainly yes, somewhere. The better question is whether the planning loop inside your own business has been rewired around it.

The past twelve months of data reveal an interesting pattern. Many founders overestimate how far their industry has progressed with AI while underestimating the small group of competitors that have already moved from pilot projects to production systems. That's where the real gap exists.

The industries leading the chart are not leading because they bought the most AI licenses. They are leading because the work they do already looks like the work AI does well: creating documents, building models, analyzing information, and supporting decisions.

That brings us back to two numbers.

• The 88% figure measures how many companies have touched AI.

• The 6% figure measures how many have wired AI into the planning function in a way that shows up in EBIT.

A founder's job is to know which number describes their company today and decide which number they want to belong to twelve months from now. And if your industry wasn't among the leaders in this article, don't miss the bigger lesson. The same opportunity still exists.

Look for the workflows where your core output is a document, a model, or a piece of structured analysis. That's usually where AI in business planning creates leverage first and where the biggest gains often appear before anyone else notices them.