AI is part of almost every software demo in 2026. In practice, though, a “feature” is not the same as productivity.
The BARC Score Integrated Planning & Analytics (IP&A) 2026 makes two things clear:
- IP&A matters more as companies in volatile markets need to plan, analyze, and decide faster.
- Vendors are putting a stronger innovation focus on agentic AI.
So the question is not whether AI will be used in IP&A. It is where can it deliver measurable value first without undermining governance or control?
Thesis
Agentic AI will not turn IP&A into an autopilot. Its first impact will be in processes that still rely on manual, recurring microtasks and where clear guardrails can be set. And that works when the relevant data is available, with the necessary quality and history behind it to feed the AI.
Why many AI approaches in IP&A fail
IP&A processes make weak AI obvious quickly.
- High stakes: Forecasts, budget changes, and management reports are not a sandbox.
- Traceability: Who changed what, when, and why?
- Heterogeneous data: Actual and plan data, master data, hierarchies, comments, and workflows.
If these foundations are not clean, AI can still produce text, but it cannot make the process more reliable.
Where agentic AI can automate first without losing trust
The first use cases that hold up will not be the flashy ones. They will be the ones that take up time.
1) Finding and triaging anomalies
Agents can check in the background for:
- unusual movements in cost centers or products
- notable driver changes in the forecast
- unexpected deviations between plan, actuals, or the past
Agents do not deliver “truth.” They point people to the issues that need attention.
2) Preparing variance explanations
In FP&A, most of the time is not spent on calculations, but on communication.
Agentic AI can draft:
- initial hypotheses about variance drivers
- consistent wording across business units
- signs of data quality issues
Drafts are fine. Approval and publication stay with people.
3) Forecast support instead of forecast autonomy
Agents can generate suggestions for:
- alternative driver assumptions
- sensitivities for price, volume, FX, and headcount
- scenarios that would not be set up manually
The value comes when suggestions are explainable and treated as options, not as the result.
4) Workflow triggers and “next best action”
An agent can trigger tasks when defined thresholds are exceeded:
- start a review
- notify the owner
- require a comment
- start data loading and validation checks
It may be boring, but that is exactly why it works.
What should deliberately remain manual
Not every step in the IP&A process is suitable for automation, especially decisions with high risk:
- Strategic goals and prioritization: Whether growth or margin is more important is not a calculation. That remains a management decision.
- Materiality: Not every variance is automatically important. What matters for steering depends on the business context.
- Regulatory compliance: When planning, reporting, or forecasts touch regulatory requirements, clear human control is needed.
- Approvals: Agents can prepare suggestions. The final decision needs a person who is responsible for it.
The checklist for trustworthy agentic AI in IP&A
When a vendor sells agentic AI, these questions matter:
- Explainability: Why did the suggestion appear, and which data was used?
- Audit trail: What was generated, what was accepted, and by whom?
- Boundaries: What is the agent allowed to decide, and what is off-limits?
- Fallback: What happens when data is missing or uncertainty is high?
- Governance: How are prompts, rules, and models versioned and tested?
Agentic AI will not change IP&A through one big feature, but through many small automations.
If teams choose the first use cases well, they gain speed without losing control.
