How to Evaluate AI Strategic Planning Software: A Buyer's Framework

Author: Eric Levine, Founder of StratEngine AI | Former Meta Strategy & Operations | UCLA Anderson MBA | CPA

Published: July 16, 2026

Reading time: 8 minutes

Summary

Every strategic planning vendor now says it has AI, but the word covers two different products. One reads your execution data and runs structured analysis over it. The other is a text box that forwards your prompt to someone else's model and pastes the reply into a goal tracker. They cost about the same and demo about the same, so the useful question isn't which tool is best; it's how to tell what you're actually looking at.

This is a criteria-first framework: six evaluation criteria, ten questions for the vendor demo, and a red-flag checklist for spotting a wrapper. The three questions that separate substance from marketing are to make the vendor name the denominator, the model provider, and the certificate. All three are checkable against live vendor docs and public records; all three are routinely dodged. None of the major vendors publish list pricing, so build the shortlist on capability and let procurement handle the rest.

Gartner has a name for the central problem: agent washing, the rebranding of assistants, RPA, and chatbots without substantial agentic capability. What you can catch in a sales call is whether a vendor will answer specific questions specifically. That is what the rest of this is for.

Key Takeaways

  • Gartner has a name for the central problem: agent washing, rebranding assistants, RPA, and chatbots without substantial agentic capability. It estimates only about 130 of the thousands of agentic AI vendors are real.
  • The three questions that separate substance from marketing: make the vendor name the denominator, the model provider, and the certificate. All three are checkable; all three are routinely dodged.
  • Marketing pages and help docs often disagree. One major vendor's AI page says your data never trains models without naming a provider; its own help center names OpenAI and a 30-day retention window.
  • Pricing is not a comparison axis here. None of the major vendors publish list prices, so build your shortlist on capability and make procurement do the rest.

Why is comparing AI planning software harder than it looks?

The difficulty isn't that information is scarce. It's that the category's vocabulary was designed not to discriminate.

Gartner named the pattern in a June 2025 release: agent washing. It defines the term as "the rebranding of existing products such as AI assistants, robotic process automation (RPA) and chatbots" without substantial agentic capabilities. Senior Director Analyst Anushree Verma estimated that only about 130 of the thousands of agentic AI vendors are real. Gartner also predicts over 40% of agentic AI projects will be cancelled by the end of 2027, citing unclear business value among the causes. Read both numbers as what they are: an analyst estimate with no published methodology, and a prediction rather than a measured failure rate. The direction is still the point.

That's the commercial version. There's a regulatory version too, and it's more concrete, because when AI claims get inflated in a securities filing somebody eventually has to describe the actual product under oath.

In April 2025 the SEC and DOJ brought parallel actions against Albert Saniger, founder of the shopping app Nate. The app was marketed as using AI to complete purchases autonomously, with claimed automation rates above 90 percent. According to the complaint, the actual automation rate was essentially zero: purchases were being completed manually by contract workers in the Philippines and Romania. The company had raised over $42 million. That's the outer bound of the wrapper problem, and it took federal enforcement to surface it, not a product demo.

What are the six criteria that separate these tools?

Ranked roughly by how much they'll matter a year in.

What does the AI actually read?

This is the first fork in the road, and most of the category sits on the wrong side of it. Ask what the model sees when it answers. If the answer is "whatever you type into the prompt," you're buying a chat window with your logo on it. Its analysis is bounded by what you already knew well enough to type. If the answer names your initiatives, owners, dependencies, and where things stalled last quarter, the tool can say something you couldn't have prompted for. The distinction isn't AI quality. It's whether the system has access to the state of your business or just to your description of it.

Does it run logic or fill templates?

A model trained on the internet has read thousands of completed SWOT grids, so it can produce a fluent one about your company in seconds. That's template-filling, and it's the difference between framework templates and framework logic: the shape of the analysis without the reasoning that gives the shape meaning. The test is whether the output ranks and refuses. Ask the tool which three of your twelve initiatives to kill. A template-filler returns twelve initiatives with balanced commentary on each. A system running actual logic returns three, with the constraint that forced the cut. Unranked output is the tell, because prioritization is the part that requires modeling trade-offs rather than describing them.

Does it fit how you already plan?

If you run OKRs and the tool is built around a balanced scorecard, you will spend the first quarter translating rather than planning. This one is unglamorous and it kills more deployments than model quality does. Ask to see your framework, with your vocabulary, in the demo environment. Not a case study. Yours.

Does it fit your team size and process maturity?

Tools built for enterprise strategy offices assume a strategy office: someone whose job is maintaining the plan. Drop that on a 30-person company and the tool decays into an expensive dashboard nobody updates. The real version of this question is who maintains the data, and what happens when they don't. If the AI's value depends on data hygiene that requires a full-time owner you don't have, the AI has no inputs.

Where does your strategy live once you type it?

Your strategic plan is among the most sensitive documents in the company: unannounced pivots, acquisition targets, which teams shrink. Before it goes into a text box, find out where the text box sends it. Two reference points are worth knowing, because they let you ask a sharper question than "are you secure." NIST's AI Risk Management Framework (AI 100-1, January 2023) organizes AI risk into four functions: govern, map, measure, and manage. It's explicitly voluntary and, in its own words, "does not prescribe risk tolerance." ISO/IEC 42001:2023 specifies requirements for an AI management system and, unlike the NIST framework, is certifiable by accredited third parties. That difference is your question. "We follow NIST AI RMF" is a statement a vendor makes about itself. ISO 42001 implies a certificate issued by an accredited body, which means you can ask to see it. One is a posture; the other is a document.

What is the total cost against the honest baseline?

The baseline isn't a competing platform. It's a $30-per-seat chatbot and the spreadsheets you already have, which is what most of the category is actually being compared against whether or not the vendor admits it. The purpose-built tool has to beat that, not beat nothing. Fair warning on the arithmetic: none of the major vendors publish list pricing. ClearPoint's pricing page offers no figures at all, explaining that there's "no generic pricing, because every team runs strategy differently," and routes you to a 30-minute call. Cascade shows tiers but no dollar amounts on its enterprise and essentials plans. Quantive's pricing URL now redirects to WorkBoard, which acquired it. You cannot build a cost comparison from public information, so compare capability, then let procurement fight about money.

What ten questions should you ask in the vendor demo?

Bring these verbatim. The value isn't in the answers so much as in which ones produce a straight response and which produce a pivot to a case study.

  1. Which model powers this, and who operates it? A specific answer names a provider or names their own model. A vague answer is a choice.
  2. Does my data leave your infrastructure, and if so, to whom? Follow-up: get it in writing, in the contract, not the brochure.
  3. Is my strategy data used to train any model, yours or a third party's? Then: for how long is it retained, and by whom?
  4. Are you ISO 42001 certified? By which accredited body? May I see the certificate? "We follow NIST AI RMF" is not an answer to this question.
  5. What does the model read when it answers, beyond my prompt? Ask them to enumerate the fields.
  6. Show me it saying no. Ask the tool which initiatives to cut, in the demo, live. Watch whether it ranks or hedges.
  7. What does it do when the data is stale? Every real deployment has stale data. Silence about it is a design gap.
  8. What was built before ChatGPT existed, and what came after? Useful for locating the AI in the product's history: bolted on, or built in.
  9. Who else has my configuration? Team size, framework, industry. Not the flagship logo, the company like mine.
  10. What happens to my data if you're acquired? Not hypothetical. WorkBoard acquired Quantive in May 2025, with Quantive customers transitioning to the WorkBoard platform "over the coming months." Two of the category's known names became one, and the customers moved.

What are the red flags that mean "wrapper"?

A fast checklist. Any one of these is a reason to dig; two or more and you're probably looking at a wrapper.

  • The marketing page won't name the model. Not damning alone. Damning in combination with the next one.
  • The marketing page and the help center disagree. This is real and checkable. ClearPoint's AI marketing page says "Your data stays private, never trains models, and is accessible only to you," and names no provider. Its own help center states that "an integral part of our technology stack is the use of OpenAI's AI models," that data is anonymized with PII removed before transmission, that "OpenAI retains this data for 30 days," and that OpenAI "does not use the data sent via the API to improve its models." Nothing there is scandalous, and the retention detail is arguably reassuring. The point is that you had to leave the marketing site to learn where your strategy goes. Read the help center of every vendor on your list. That documentation is dated September 2023, so confirm it still holds.
  • Certifications held "via trusted partners." Cascade's security page describes "SOC 2 & ISO 27001 compliance via trusted partners." That phrasing is doing work. Infrastructure certified by a hosting provider is not the same as the vendor being certified, and it's a fair thing to make them clarify.
  • Every output is a list, never a ranking. Covered above; it's the single best five-minute test in the demo.
  • The percentage has no denominator. Take this one seriously, because it's how a real company got charged. The SEC's January 2025 action against Presto Automation, its first AI-washing case against a public company, describes Presto advertising roughly 95% "automated order completion." That metric counted orders completed without restaurant staff involvement. It did not mean no humans: per the order, the pilot required a human agent to enter orders approximately 70% of the time. Both numbers were about the same product. They had different denominators. When a vendor quotes you an automation or accuracy percentage, ask what's in the denominator before you write it down.
  • "Our AI is proprietary" with nothing behind it. Presto called the deployed technology "our technology" while it was powered entirely by a third party's.

How do the major categories stack up against the criteria?

Types, not a ranked list. Ranking specific tools is the pillar's job; this is about which shape fits your situation.

Execution platforms with AI added (the OKR and scorecard incumbents) score well on framework fit and process maturity, because that's what they were built for, and the AI generally reads your execution data, which is a real advantage over a blank chat window. They're weakest on the logic-versus-templates criterion, since the AI layer often arrived after the product did.

General chatbots, the honest baseline, are unbeatable on cost and genuinely strong at exploratory work: research, option generation, stress-testing. They fail the "what does it read" test by construction, and they don't rank.

Purpose-built strategy engines are built around the analysis rather than the tracking, which is where they should win the second and fifth criteria. The trade is maturity and integration surface. StratEngine is one of these, so weigh that accordingly, and hold us to the same ten questions. If we dodge the denominator, the provider, or the certificate, mark it against us.

The framework matters more than my category preference. A vendor who answers all ten questions crisply is telling you something real, whichever box they're in.

Frequently asked questions

What's the difference between AI strategic planning software and just using ChatGPT?

Access and structure. ChatGPT reads what you type; strategic planning software with real AI reads your initiatives, owners, dependencies, and history, so it can flag things you didn't think to ask about. The second difference is that purpose-built tools are built to rank and refuse, where a chatbot returns a balanced list and lets you pick. If a vendor's tool can't do either of those, you are paying a large premium for a chat window.

How much does AI strategic planning software cost?

Publicly, nobody knows, and that's not evasion on my part. ClearPoint, Cascade, and the rest route pricing to a sales call rather than publishing figures. Which means any number you see quoted in a listicle is either dated or invented. Budget for annual enterprise contracts, expect per-seat pricing with a platform fee, and use the chatbot-plus-spreadsheets baseline as your reference point when the quote arrives.

How long does implementation take?

It depends almost entirely on your data, not their software. The tool can be provisioned in a day; it becomes useful when your plan, owners, and progress data are in it and current. That's why the maintenance question above matters more than any implementation timeline a vendor quotes: the honest constraint is whether someone owns keeping it accurate, and that's on your side of the contract.

About the Author

Eric Levine is the founder of StratEngine AI. He spent five years at Meta in Strategy and Operations, where he led global business strategy initiatives across international markets. He holds an MBA from UCLA Anderson and is a CPA. He builds AI-powered strategic planning tools used by operators, consultants, and executives to compress the mechanical work of strategy and reporting while keeping the judgment human.