AI Agents for Strategy and Planning Teams: Scenarios and Evidence

AI agents for strategy and planning teams work best as preparers, not deciders. They gather and structure evidence, draft scenarios, test assumptions against sources and keep a record of what changed, while leaders keep the choices, the trade-offs and the accountability. The practical gain is that the team spends less time assembling inputs and more time arguing about what to do.

Key takeaways

• Start with an evidence base the agent can cite, not with scenarios. Scenarios built on unsourced claims only look rigorous.
• Give the agent a narrow role: collect, structure, compare and draft. Choosing, ranking and committing stay with named leaders.
• Keep every scenario tied to explicit assumptions, so the team can see which belief each option depends on.
• Test the agent's work with a fixed set of review questions and known cases before anyone relies on it.
• Treat the agent as a standing member of the planning cycle with a memory, so last quarter's reasoning is still available this quarter.

What can an agent actually do for a strategy team?

Strategy work has a recognizable shape. Someone gathers market notes, internal numbers, customer signals and competitor moves. Someone else turns them into a view of the situation. A group then debates options, and a leader decides. Most of the delay sits in the first two steps, and most of the lost value sits in what gets forgotten between cycles.

An agent is useful in exactly those places. In a well-scoped setup it can:

• Pull material from approved internal and external sources on a schedule and file it in a consistent structure.
• Summarize long documents and flag the passages that bear on a specific planning question.
• Maintain a register of assumptions, with the source behind each one and who owns it.
• Draft scenario narratives from a template the team has agreed, and list what each scenario would require to be true.
• Notice when new information contradicts an assumption already on the register, and tell the owner.
• Prepare the pre-read for a planning session, including the open questions nobody has answered yet.

Notice what is missing from that list: ranking the options, recommending a winner as if it were settled, or committing budget and headcount. Those belong to people. Anthropic's guidance on [building effective agents](https://www.anthropic.com/engineering/building-effective-agents) makes a related point: simpler, well-bounded workflows with clear checkpoints tend to be more dependable than open-ended autonomy, and a planning function benefits from that discipline.

Why build the evidence base before the scenarios?

Scenario work fails quietly when the inputs are loose. A fluent paragraph about a possible future reads the same whether it rests on a solid source or on a half-remembered conversation. An agent makes this risk larger, because it writes fluently about everything.

The remedy is an evidence register that exists before any scenario is drafted. Each entry holds a small number of fields:

• The claim, stated in one plain sentence.
• The source, with a location a reviewer can open.
• The date the source was last checked, so staleness is visible.
• The type of evidence: internal record, external publication, expert judgment or the agent's own inference.
• A confidence note written by a human owner, not by the agent.
• The decisions or scenarios that depend on this claim.

This is a retrieval problem as much as a writing problem. The agent should answer from the register and the approved document set, and say so when the register does not contain what it needs. The [AWS overview of retrieval-augmented generation](https://aws.amazon.com/what-is/retrieval-augmented-generation/) describes the general pattern: ground the model's answers in retrieved, authoritative content rather than relying on what it absorbed during training. For a strategy team, that means every sentence in a briefing can be traced back to an entry, and an untraceable sentence is treated as a draft idea, not as evidence.

One rule keeps the register honest: the agent may propose entries, but a person approves them. Inference is allowed, but it is labeled as inference.

How does a scenario workflow run, step by step?

The workflow below suits a planning cycle that repeats on a regular rhythm. It assumes one accountable leader and a small group of analysts.

• Step one: the leader writes the decision question and the time horizon. A good question is narrow enough to be answerable, such as whether to enter a new segment, not what the future holds.
• Step two: the team lists the forces that could change the answer, and the agent adds any it finds in the evidence that the team missed.
• Step three: the agent fills the evidence register for each force, marking gaps explicitly instead of smoothing them over.
• Step four: humans review the register, correct entries, assign owners and rate confidence.
• Step five: the team chooses a small set of contrasting scenarios, and the agent drafts each one from the register, listing its assumptions and what it would take for the scenario to be wrong.
• Step six: the agent prepares a comparison of options under each scenario, using the criteria the leader set in advance, and flags where an option only works in one future.
• Step seven: the session happens. People argue, change criteria, reject options. The agent records the reasoning and the dissent, not just the outcome.
• Step eight: the leader decides and states why. The agent files the decision, its assumptions and the signals that would trigger a review.
• Step nine: between cycles, the agent watches those signals and reports when one moves.

The last two steps matter most and are most often skipped. A decision record with named signals turns planning from an annual event into a loop. It also protects the team from hindsight, because what was believed at the time is written down.

What does this look like in practice? An illustrative scenario

This is an illustrative scenario, not a description of a real client.

A regional distributor is deciding whether to add a second warehouse or to improve how it uses the one it has. The operations director owns the decision. The planning analyst asks the agent to build the evidence base.

The agent collects internal order patterns, supplier lead time notes, lease and transport information the finance team has approved for use, and a handful of external industry publications the analyst selected. It builds the register and marks three gaps: no reliable view of how customers would react to slower delivery, no recent supplier commitments in writing, and a transport cost assumption that rests on a single old quote. It does not fill the gaps with guesses.

The analyst and the director close the first gap by interviewing two account managers, and the second by asking procurement to confirm terms. The third stays open and is carried into the scenarios as a stated weakness.

The team then picks three contrasting futures: steady demand with stable suppliers, growth concentrated in one region, and supplier disruption. The agent drafts each, lists the assumptions, and compares the two options under each. The comparison shows that the second warehouse only looks better in the growth scenario, while improving the existing site holds up in all three but caps upside. The director decides to improve the existing site and sets two review signals: sustained growth in the one region, and a change in supplier reliability. The agent now watches for both.

The agent prepared the options and kept the memory. The director made the call and owns it. For related ground on supplier signals and alerting, see AI for a supply chain planning team.

Where should leaders keep the decision?

It helps to settle decision rights before the first run, not after the first disagreement. A simple guide separates three kinds of action.

• The agent may act alone: collect approved sources, summarize them, update the register as a proposal, draft scenarios from the template, prepare pre-reads and log what happened.
• The agent must ask first: change a confidence rating, remove an assumption, send material outside the planning group, add a new external source to the approved set, or contact anyone on the team's behalf.
• The agent must never do: rank options as final, approve spend, change criteria the leader set, or present an inference as a verified fact.

Two further habits keep this boundary real. First, permissions should match the role. If the agent only needs to read finance summaries, it should not hold write access to the finance system. The [OWASP authorization guidance](https://cheatsheetseries.owasp.org/cheatsheets/Authorization_Cheat_Sheet.html) is a sound reference for least-privilege thinking. Second, every output that informs a decision should show its sources and its open gaps, so a leader can challenge it quickly.

There is also a human reason for this boundary. People defend decisions they understood and owned. If a recommendation arrives with no visible reasoning, the team either rubber-stamps it or ignores it, and both outcomes waste the preparation.

How do you know the agent's preparation can be trusted?

Trust here is earned by checking, not by impressions of fluency. Strategy outputs are hard to grade because there is no single right answer, but there are still things you can test.

• Source fidelity: does each cited claim actually appear in the cited source? Sample entries each cycle and check them by hand.
• Gap honesty: when the register lacks evidence, does the agent say so, or does it fill the space with plausible text?
• Contradiction handling: plant a known conflict between two sources and see whether the agent surfaces it.
• Balance across scenarios: are the contrasting futures treated with equal effort, or does the first one get the detail and the rest get filler?
• Stability: if you ask the same question twice, do the material conclusions hold?

Keep a small fixed set of such cases and rerun it whenever the agent's instructions, sources or tools change. Anthropic's write-up on [demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents) is a useful starting point for turning these checks into something repeatable. For the wider governance frame, the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) offers a vocabulary for mapping, measuring and managing risk that a leadership team can adopt without building a program from scratch.

Two failure modes deserve particular attention. The first is anchoring: the first scenario drafted tends to shape all the others, so ask the agent to draft the contrasting scenarios independently from the register. The second is untrusted content. If the agent reads web pages or documents from outside the company, those can contain instructions meant to steer it. External material should be treated as data to analyze, never as commands, and the agent should have no ability to send or change anything on the strength of what it read.

If your team already produces status and risk reports, the discipline is similar. The article on status and risk reporting in a project management office covers how to keep reports grounded in records, and the same habits carry over to planning packs. For the recurring side of the work, AI agents for operations teams describes how scheduled tasks and reports are kept dependable.

How does a managed setup work, and where should you start?

Be Human designs, implements and operates this kind of agent as personalized infrastructure. A first engagement defines one useful role, such as evidence steward for the planning cycle, together with the memory and tools it needs, the approval boundaries and one observable success criterion, for example that every briefing claim traces to a register entry a reviewer can open. The agent keeps its identity, memory, channels, tools and permissions under human control. Managed operations can then include monitoring, recurring evaluations, integration maintenance, permission review, incident response and controlled improvements, so the setup stays accurate as sources, tools and questions change.

A sensible first task is narrow: build and maintain the evidence register for one live decision, and nothing else. It is easy to review, it produces value even if the scenarios are written by hand, and it shows quickly whether the team's sources are good enough. Expansion to scenario drafting and signal monitoring follows once the register has proven reliable. If your archive is large and varied, the lessons in research and archives for publishers on organizing source material apply well to planning libraries.

Take the free Be Human AI diagnostic to find the first task worth handing to an agent.

Frequently asked questions

Can an AI agent decide strategy for us? It should not. An agent can prepare evidence, draft scenarios and surface contradictions, but choosing among options involves values, risk appetite and accountability that belong to named leaders. Keep the agent in a preparing role and record the human decision and its reasoning.

What data does a strategy agent need? Start with the smallest set that answers one decision question: approved internal summaries, a short list of external publications and any notes from interviews. Add sources deliberately, with an owner for each, rather than connecting everything at once. Less data with clear provenance beats broad access with unclear provenance.

How do we stop the agent from inventing facts? Require every claim to point to a register entry and have the agent state when evidence is missing. Sample citations by hand each cycle and rerun a fixed set of test cases whenever instructions or sources change. Treat any uncited statement as a hypothesis, not as evidence.

How long before a planning team sees a benefit? The earliest benefit usually appears in the first cycle, when the pre-read and evidence register take less effort to assemble and the open gaps are visible sooner. The larger benefit comes later, when decision records and review signals carry reasoning from one cycle into the next. Judge it against the success criterion you set at the start, not against general impressions.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.