Using agentic AI tools in business planning

In the realm of business planning, how do AI agents differ from traditional automation tools?

It seems like everyone’s talking about AI agents and their potential to elevate routine business planning. But it’s not just talk: from local DeepSeek builds to specialized AI agents for finance teams, businesses are on the hunt for their next AI advantage.

AI agents represent a fundamental shift in how we conceive of artificial intelligence in the workplace: they’re systems that are capable of executing commands, interpreting goals, devising strategies, and even recognizing when those strategies aren't working. 

But evaluating this shift and the opportunities that come with it requires context, which is why business leaders want answers to basic but complex questions. What is an AI agent? How do AI agents differ from traditional automation tools? And, most pressingly, how can agentic AI tools give teams focused on business planning a genuine competitive advantage?

In this post, we examine what makes AI agents different from your typical automation tools, going beyond marketing hype to understand the evolution of their true capabilities and limitations. We’ll show you the AI workflow limitations that agentic AI solves and provide examples of AI agents delivering real value in the business sector.

AI agents are intelligent systems designed for action

An AI agent is an intelligent system designed to take action, not just analyze data. Instead of waiting for a prompt from a human or running on rigid scripts, AI agents use advanced reasoning to understand goals, decide how to accomplish them, and execute tasks across business planning tools and workflows.

Before AI agents, there was deterministic automation

Imagine it’s Monday morning, and your financial reporting system kicks into gear at exactly 9:00 AM. It pulls data from the same five databases, applies the same formulas, and generates the same report format it's been producing for the past three years. If someone added a new revenue stream last week, too bad – the system doesn't know to look for it. If the CFO wants a different analysis this month, someone needs to manually reprogram the entire workflow.

This is deterministic automation, the backbone of business operations before AI changed the game. "Deterministic" simply means that, given the same input, you'll always get the same output. No surprises, no adaptation, no judgment calls.

Think of deterministic automation as a vending machine. Press B4, and you'll get a snack from the same slot every time. The machine doesn't care if you're allergic to peanuts or if you'd prefer dark chocolate today. It doesn't learn that you usually buy two items on Fridays. It just follows its programming: money in, button pressed, item dispensed.

In business planning, deterministic automation shows up everywhere:

• Scheduled reports that pull the same metrics every week, regardless of what's actually important right now
• Approval workflows that route purchase orders based solely on dollar amounts (under $10K goes to your manager, over $10K to the VP)
• Data transformations that apply the same business rules month after month (multiply units by price, sum by region, format as table)
• Alert systems that flag when inventory drops below 100 units – even if that SKU has been discontinued

In their time, these systems were revolutionary. They eliminated manual work, reduced errors, and created consistency. But they also created a hidden cost: the "automation tax" of constantly maintaining and updating rigid systems that can't adapt to changing business needs.

The frustration with deterministic automation is what drove the search for something smarter. Business leaders were tired of systems that couldn't handle exceptions, couldn't learn from patterns, and couldn't tell the difference between a critical issue and business as usual.

Agentic AI evolved from standalone large language models (LLMs)

The first time someone showed a CFO that ChatGPT could draft a board presentation, they probably said, "Wait, it actually understands what I'm asking for?"

But let’s say the CFO, thrilled at the possibility of an AI-drafted board presentation, asks: "Great, now can you pull last quarter's actual numbers to update this analysis?"

The answer would be silence – or, if you’re asking ChatGPT, an onslaught of fictional numbers straight out of the LLM’s imagination. That’s because while standalone LLMs can think, reason, and create, they can't see your data, check your calendar, or update your forecasts.

It's working entirely from its training data – a vast knowledge base that stops at its training cutoff date. These solutions don’t have access to any of your business data or context, and they are totally in the dark when it comes to your business plan.

This highlights the two fundamental limitations of standalone LLMs:

• They're disconnected: They have no access to your proprietary data, real-time information, or internal systems.
• They're passive: They wait for your prompt, respond, then wait again. Standalone LLMs lack initiative and follow-through, and they don’t automatically monitor for updates or variances.

Through standalone LLM tools, the CFO had access to incredibly sophisticated AI that could understand any business concept. But these tools were disconnected from their business’ core planning software. As a result, analysts would still have to copy and paste data themselves, manually cross-check their analyses, and update their databases by hand. 

AI applications proved the business need for greater AI efficiencies

Imagine your AI model finally got access to your business planning platform. To get results, you’d need to give it an action plan. This is exactly what AI workflows do – they give LLMs a predefined path to follow, complete with access to your actual business tools.

Instead of copying and pasting data manually, that same CFO could say, "Generate our weekly revenue report," and watch as the AI workflow:

• Automatically pulled fresh data from Salesforce
• Analyzed it using the LLM's intelligence
• Generated insights in the company's standard format
• Emailed it to the leadership team

No more manual data pulls, no more formatting headaches.

But the problem with AI workflows is they lack flexibility. They’re tied to a predetermined path, in this case:

pull data → analyze → format → send 

Let’s say, for example, that the CFO from our example needed to ask why a specific commercial region or business area was under-performing. The AI workflow wouldn’t be able to adapt, since there was no step for "notice something unusual and investigate further."

The CFO or another member of the finance team would find themselves building more and more workflows to accommodate business needs:

• One for revenue reports
• Another for expense analysis
• A third for headcount planning
• A fourth for budget variance analysis

These workflows would accumulate into an ongoing maintenance burden, with any business change requiring dozens of workflows to be updated. If the company added a new region, someone would have to manually update every workflow that touched regional data.

Despite their ability to follow predetermined paths, AI workflows are unable to choose which path to take, combine paths creatively, or create new paths when faced with unexpected questions.

Agentic AI tools solve yesterday’s workflow limitations

Now imagine the CFO gives the same revenue report request, but this time to an AI agent. The agent pulls the data, starts analyzing, and notices something unusual: numbers in North America are way off-trend.

Without being asked, the agent flags the variance and decides to investigate:

• It digs into the company’s regional data, where it discovers that three major accounts churned in the same month
• The AI agent then cross-references against the CRM to find that all three affected accounts mentioned pricing concerns
• At the same time, it checks competitive intelligence to see a rival's aggressive discounting campaign
• Finally the AI agent produces a comprehensive report that not only shows what happened but why it happened and what to do about it.

With AI agents, the AI itself becomes the decision-maker in the workflow. The CFO doesn’t have to tell it how to investigate revenue issues: all they’d need to do is ask the AI agent to ensure revenue targets are met, and the agent figures out how.

Agentic AI uses the “ReAct framework”

The magic of agentic AI happens through something called the ReAct framework – a framework defining the agent's ability to reason and act in cycles.

Here’s how our example fits into this framework:

1. Reason: Noticing a variance, the AI agent might conclude: "Revenue is down 15% in North America. This is unusual. I need to investigate why."
2. Act: Next, the AI agent pulls detailed regional data, checking customer churn rates and analyzing revenue changes.
3. Observe: The agent makes an observation, such as, "Three major accounts churned citing pricing. This represents 12% of regional revenue."
4. Reason again: It then returns to the reasoning stage: "The timing suggests a connection. Let me check competitive activity."
5. Act again: The AI searches market intelligence, pulling competitor pricing data.
6. Iterate: It continues this cycle again until the full picture emerges.

The point of the ReAct framework is that the agent isn't following a script – it's making decisions based on what it discovers, just like a human analyst would. Even better, it’s learning and improving based on its own findings. After investigating the regional situation, the AI agent might proactively start monitoring competitive pricing across all regions. It remembers that pricing pressure caused problems before and adjusts its approach accordingly.

With AI agents, our CFO no longer needs dozens of workflows for every possible scenario. The agent can create its own workflows and modify them based on what needs to be done.

Multi-agent systems: Examples of AI agents working together

In a multi-agent system, AI agents can cooperate, negotiate, and learn from each other, often solving problems more efficiently than in a monolithic system.

At Pigment, we use three domain expert agents: the Analyst, the Planner, and the Modeler. These AI agents are all supported by a Reporter Agent and monitored by a Supervisor Agent. 

The Analyst

This agent evaluates data from both internal and external sources to pinpoint trends, recognize anomalies, and create an in-depth understanding of the factors influencing data and performance patterns.

The Planner

The Planner AI agent works in tandem with the Analyst to transform data insights into strategic action plans. Pigment developed the Planner for use cases related to business planning, like financial planning and analysis (FP&A). This AI agent develops recommendations based on historical performance, market conditions, and organizational objectives. For instance, when reviewing the earlier case of revenue decline in North America, the Planner agent would simulate various pricing strategies, evaluate their potential impact on customer retention, and propose targeted solutions to protect market share without compromising profitability.

The Modeler

The Modeler agent designs and maintains the sophisticated financial models that power both the Analyst's insights and Planner's recommendations. When business conditions change — like the competitive pricing shift we discussed earlier — this agent automatically updates forecasting models, adjusts revenue projections, and ensures data integrity across all planning scenarios. Think of the Modeler as the architect, continuously refining the mathematical foundation that enables accurate, real-time business planning.

The Supervisor

Much like for human teams, the Supervisor Agent acts as the lead for all the agentic AI tools, coordinating the other agents to complete tasks and communicating with the user where necessary.

The Reporter

Specialized in generating and publishing charts and data, the Reporter agent pushes the Analyst’s output into a variety of formats for easy consumption, including PDF, PPT, and audio files.

To learn more about multi-agent systems, read our related blog post: Building a hivemind: A deep-dive on multi-agent systems. You can also learn more about Pigment’s agentic AI tools here.

How to get started with agentic AI for business planning

Despite widespread enthusiasm and expectation surrounding artificial intelligence, most organizations are still two to three years away from fully capitalizing on its capabilities.

The question isn't whether AI agents will transform business planning, it’s how quickly businesses can adopt agentic AI tools to stay competitive. These tools are no longer just helping business planning professionals perform routine tasks – they're automating context-driven decision-making, investigation, and problem-solving.

Kickstart your AI agent journey today

Ready to see what AI agents can do for your business planning? Book a free, personalized demo to experience Pigment's multi-agent system in action – or explore our resource center to learn more about the future of AI-powered business planning.