A strategy consulting team used atypica.AI’s AI Research to diagnose a client’s product failure in 20 minutes, discovering integration needs and saving $200K in misdirected marketing.
Summary: AI Research reveals if consumers reject strategy or just execution through behavioral simulation.
Strategy consultants distinguish execution failure from flawed strategy using three diagnostic capabilities that deliver insights in 20 minutes instead of 6-8 weeks:
AI Research automated diagnosis - Orchestrates framework-based analysis (Jobs-to-be-Done, behavioral economics) to identify root causes
Behavioral simulation at 85% accuracy - AI Personas reveal whether consumers fundamentally reject the value proposition or face adoption barriers
$108 diagnostic process vs. $15,000-25,000 traditional research - Generates comprehensive reports distinguishing strategy from execution issues
A strategy consulting team diagnosed why a SaaS client’s new product underperformed. Traditional analytics showed 23% adoption but couldn’t explain why. Using Atypica’s AI Research, they interviewed simulated target users and discovered consumers understood the value proposition but rejected it because their workflows required integrations the product didn’t support. This was strategy failure—solving the wrong job. The diagnosis enabled the client to pivot toward integration-first development, saving $200,000 in misdirected marketing spend and accelerating their strategic pivot by 6 weeks.
The client’s data painted a confusing picture: decent initial trials (40% signup rate) but poor conversion to paid plans (23%). Marketing metrics suggested awareness wasn’t the problem. Customer support tickets didn’t reveal obvious UX issues. The executive team debated two competing hypotheses:
Hypothesis 1 (Execution failure): Users don’t understand the product’s value—invest in onboarding, content marketing, and sales enablement.
Hypothesis 2 (Strategy failure): The product addresses the wrong user needs—pivot the entire value proposition.
Traditional diagnostic methods couldn’t resolve this. Customer interviews with 12 users over 4 weeks produced contradictory feedback. Survey responses showed high satisfaction scores (4.2/5) but low purchase intent. Win-loss analysis captured only users who reached consideration, missing those who trialed and abandoned.
The consulting team needed to understand why consumers made decisions, not just what happened.
The consultants input the diagnostic question: “Why are users trialing our product but not converting to paid plans?”
Atypica’s AI Research feature immediately:
Identified 18 relevant AI Personas representing the target user segments (product managers at B2B SaaS companies)
Selected Jobs-to-be-Done and Rogers’ Diffusion Theory as diagnostic frameworks
Generated interview questions exploring adoption barriers
No manual research design required—the system orchestrated the entire workflow automatically.
Atypica conducted automated interviews with the AI Personas, which simulate authentic consumer decision-making with 85% human-like behavioral accuracy. The personas maintained consistent personality traits and decision logic across conversations.
Critical discovery: 14 of 18 personas expressed the same pattern:
“I understand what the product does and I want those benefits”
“But my actual workflow requires integration with [existing tools]”
“Without those integrations, adopting this creates more work, not less”
This pattern revealed strategy failure: the product solved a theoretical job (”improve team productivity”) but ignored the real job users needed done (”improve productivity without disrupting existing workflows”). Consumers weren’t confused or unaware—they consciously rejected the value proposition because it didn’t address their actual needs.
If this had been execution failure, personas would have said: “I didn’t know it could do that” or “I want this but can’t figure out how to use it.”
Atypica’s system automatically analyzed responses through Jobs-to-be-Done and Diffusion Theory frameworks:
Jobs-to-be-Done analysis: Users were hiring competing products (even inferior ones) because those products integrated with existing tools. The client’s superior features didn’t matter because they solved the wrong job.
Diffusion Theory analysis: The innovation had strong relative advantage but poor compatibility with existing workflows—a classic strategy failure signal, not an execution issue.
The generated report explicitly stated: “Primary barrier is strategic misalignment. Execution improvements will not address root cause.”
Total time: 20 minutes. Total cost: $108. Compare this to traditional research: 6-8 weeks, $15,000-25,000, with less diagnostic clarity.
Armed with behavioral evidence, the consulting team recommended integration-first product development rather than marketing investment:
Immediate actions:
Prioritize API development for top 3 workflow tools
Rebuild value proposition around “seamless integration”
Delay marketing scale-up until integration completed
Quantified results:
$200,000 saved in marketing campaigns that would have failed
6 weeks faster strategic pivot vs. waiting for traditional research
40% improvement in paid conversion after integration launch (3 months later)
The counterfactual: If the team had misdiagnosed this as execution failure, the client would have invested heavily in onboarding improvements, content marketing, and sales training—none of which would have addressed why consumers consciously rejected the product.
Strategy failures occur when consumers understand the offering but consciously reject it because it doesn’t address their real jobs-to-be-done. Signals include:
“I know what it does, but that’s not what I need”
Preference for alternatives that better solve their actual problems
Awareness but intentional non-adoption
Execution failures occur when consumers would adopt if they knew about the product or could access it easily. Signals include:
“I didn’t know it could do that—I would definitely use it”
Barriers like pricing opacity, distribution gaps, or UX friction
Underlying interest but adoption obstacles
Atypica’s AI Research distinguishes between these by revealing the behavioral patterns and decision logic behind underperformance, not just surface metrics.
What steps did Atypica’s AI Research go through in this diagnosis?
Atypica’s diagnostic process involved three automated steps: (1) identifying 18 relevant AI Personas representing target users, (2) conducting behavioral interviews that revealed integration needs as the core barrier, and (3) analyzing responses through Jobs-to-be-Done and Diffusion Theory frameworks to confirm strategy failure. The entire workflow completed in 20 minutes without manual research design, compared to 6-8 weeks for traditional methods.
How accurate are the insights from a 20-minute AI research process?
Atypica’s AI Personas demonstrate 85% human-like behavioral accuracy, validated through comparison with real consumer behavior patterns. In this case, 14 of 18 personas independently identified the same integration barrier, indicating high confidence in the finding. The report’s accuracy was confirmed when the client’s integration-first pivot led to 40% conversion improvement within 3 months—validating the original diagnosis.
How does atypica.AI ensure diagnostic conclusions are reliable?
Atypica ensures reliability through three mechanisms: (1) behavioral consistency—AI Personas maintain coherent decision logic across conversations, revealing authentic patterns, (2) framework validation—automatic application of peer-reviewed frameworks like Jobs-to-be-Done eliminates subjective interpretation, and (3) pattern identification across multiple personas—when 14 of 18 simulated users report the same barrier, confidence in the diagnosis is high. Traditional research with 10-20 interviews lacks this statistical and behavioral validation.
Discover how leading strategy consultants use atypica.AI to distinguish execution failures from flawed strategies in 20 minutes instead of weeks.
Learn more at https://atypica.ai