Part 2: AI for Strategic Planning
Does this sound familiar? Your leadership team spent four months crafting a strategic plan. The board approved it in December. By March, three assumptions had changed. By June, a competitor moved into your space. By September, the plan was an expensive paperweight.
This is what happens when strategy is treated like an annual event instead of a living system.
Annual planning made sense when change was slow. Today competitors pivot in weeks. Customers shift in months. Conditions swing in quarters.
Markets move continuously. Planning happens once a year. That gap creates three problems.
Scenarios go stale. Teams build versions in January and reality follows none of them. By the time you adjust, others have moved.
Signals get trapped. Frontline teams spot market shifts before reports do, but there is no clean way to feed those insights into the plan.
Agility erodes. When the plan lives in slides, every tweak is rework. So people stop adjusting and drift.
The answer is not better annual planning. It is watching leading indicators across customers, competitors, costs, and compliance that move before results do, and building a system that responds to them.
The teams gaining ground are not winning with prettier annual decks. Many stopped planning annually at all.
Strategist John Hagel's Zoom Out, Zoom In model keeps a 10 to 20 year view of where the market is headed and a 6 to 12 month set of moves that get you closer. Two questions guide it: What will our market look like in 10 to 20 years? Which two or three moves this year matter most?
AI strengthens the loop. It watches market signals, tests how shifts affect your current plan, and flags when a change is warranted. The long view holds steady. Near term moves adjust as data changes.
Quick picture: A B2B software firm keeps "trusted AI for regulated industries" as the 10 year direction. For the next 12 months it focuses on three moves: SOC 2 upgrades, a healthcare pilot, and a pricing test. AI monitors competitor releases, RFP language, and win rates by segment. When RFPs start asking for model cards and audit trails, the team shifts a quarter earlier. No drama. Just an informed course correction.
Continuous strategy means watching market signals early and adjusting before results slip. Use the four practices below to keep the plan current with light effort.
Watch live indicators, review by people. AI watches customers, competitors, costs, and compliance, then highlights what changed, how big it is, and why it might matter. People review and decide. Weak clues become early calls, not late surprises.
Keep models current. Instead of three static scenarios built in January, AI keeps assumptions and ranges current as conditions evolve so leaders see the impact in real time. Choices reflect today, not last quarter.
Manage assumptions as assets. Give each critical assumption an owner, a source, and a review date. Treat its evidence like it has a half life. AI monitors evidence freshness, flags contradictions, and suggests quick tests such as a targeted customer call list, a small pricing A/B, or a limited scope pilot. When evidence ages out or conflicts, run a short review to reaffirm, revise, or retire. You catch aging or conflicting evidence early, so the plan stays accurate.
Connect strategy to execution. AI turns daily operational data into a simple strategic readout by linking work in flight to objectives, surfacing variances on leading indicators, and calling out where a decision is needed. Leaders see whether teams are moving toward outcomes or just staying busy. Execution stays aligned to intent, and decisions land while evidence is still fresh.
Here is where most strategy consultants give you the what. I am going to give you the how, the what goes wrong, and the specific pattern that matters most.
Start with a 30 day AI assisted market signal loop. Add this lightweight routine to your operating rhythm:
Daily, automated: An AI agent produces a 5 line digest comparing this week's indicators to a 90 day baseline.
Weekly, 15 minutes: A quick standup reviews the top three items and assigns a single action per item.
Monthly, one page: A decision journal captures the trigger, the choice, and a 30 day check.
Keep it human and brief. Use plain language. Name the owner. Close the loop in 30 days.
Most monitoring systems track too much and catch too little. Focus on divergence between segments. Here is the pattern: When win rates drop 12% or more in enterprise deals but SMB stays flat, and your top rep mentions "they went with the compliance focused vendor" twice in two weeks, that is a regulatory shift signal appearing three quarters before it shows in revenue.
The key is cross referencing quantitative divergence (one segment drops while another holds) with qualitative repetition (the same phrase from different sources). Divergence tells you something changed. Repetition tells you what.
The monthly one pager must answer four questions:
Without that structure, decision journals become documentation theater. With it, they become a learning system that compounds over time. After six months you will start seeing which types of signals reliably predict outcomes and which are noise.
The biggest mistake: turning the 15 minute standup into a 45 minute debate. If you cannot decide in 15 minutes, the signal is not clear enough yet. Table it and gather more data. The standup exists to surface decisions that are ready, not to manufacture consensus on ambiguous information.
I have watched leadership teams kill this system by treating it like traditional strategic planning. They want certainty before acting. Continuous strategy requires comfort with smaller bets based on earlier, weaker signals. If you wait for certainty, you are back to annual planning with extra meetings.
Harvard Business Review research shows that CEOs using AI for strategic planning uncover patterns and alternatives managers often miss, while cautioning that company specific context still requires human judgment. McKinsey research on AI in strategy development reinforces this: AI speeds analysis while leaders bring priorities, context, and conviction.
That framing is correct but incomplete. In our work with mid market software and services companies, we have found the bigger barrier is not analytical speed. It is psychological. CFOs resist continuous planning because volatile forecasts look bad to boards, even when that volatility reflects market reality. VPs resist updating assumptions because it exposes how much they were guessing six months ago. Until you solve the political problem of making uncertainty visible and acceptable, the analytical tools sit unused.
The value proposition needs to shift. Continuous strategy is not about being right more often. It is about being wrong earlier when course correction is still cheap. That requires leadership teams to reward fast learning over accurate predictions.
AI sees patterns in noise and second order effects. Humans bring experience, capability insight, and judgment about what the organization can actually execute. Together you get strategy that stays relevant because it evolves at the speed of your market.
Despite heavy investment in AI tools, McKinsey reports that most organizations struggle to see measurable impact from AI initiatives. This happens because companies treat AI as a project rather than as infrastructure. The ones that break through do three things:
Redesign their planning cadence. Annual offsite become quarterly strategy reviews. Monthly executive meetings become weekly indicator checks. This is not about more meetings. It is about shorter cycles between signal and response.
Connect their data. Finance, CRM, market intel, and operations feed a unified view that AI can analyze. The hard part is not the technical integration. It is getting Finance to expose forecast assumptions and Sales to share loss reasons in structured form. Data connection is an organizational design problem disguised as a technology problem.
Build AI capability across their organization. Leaders learn to use AI as a thought partner. Teams learn how to interpret what AI surfaces and decide what matters. Most companies skip this step and wonder why adoption stalls.
The shift requires rethinking how strategy is built, tracked, and adjusted. But the alternative is worse. Plans that age before implementation cannot survive in markets that move this fast.
At RightSeat, we help leadership teams build strategy systems that stay alive after the offsite ends. We start by mapping where your critical assumptions live, who owns them, and what evidence supports them. Then we connect the data sources that feed those assumptions (often CRM, financial systems, and market intelligence) and build monitoring loops that surface changes before they hit results.
The work is part strategy redesign, part change management, and part capability building. We run the first three monitoring cycles with your team so they learn what good signals look like and how to separate noise from insight. Most importantly, we help leadership teams navigate the political shift from "looking smart" to "learning fast."
Strategy that adapts as conditions change. Advantage that compounds while competitors work from last year's assumptions.
If you missed Part 1 of our AI for Strategic Planning series, read how AI gives teams back time by cutting spreadsheet overhead and restoring energy for real high impact thinking.
Sources:
Hagel, John. (2023). Strategy as a Catalyst for Change. https://www.johnhagel.com/strategy-as-a-catalyst-for-change/
McKinsey & Company. (January 28, 2025). Superagency in the workplace: Empowering people to unlock AI's full potential at work. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
McKinsey & Company. (February 5, 2025). How AI is transforming strategy development. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-ai-is-transforming-strategy-development
MIT Sloan Management Review & Boston Consulting Group. (February 13, 2024). The Future of Strategic Measurement: Enhancing KPIs With AI. https://sloanreview.mit.edu/projects/the-future-of-strategic-measurement-enhancing-kpis-with-ai/
Harvard Business Review. (September 11, 2024). How CEOs Are Using Gen AI for Strategic Planning. https://hbr.org/2024/09/how-ceos-are-using-gen-ai-for-strategic-planning