Leadership mandates 'use AI' without direction, leaving data/engineering teams overwhelmed with possibilities but no clear path to business value. Teams waste months exploring low-impact ideas.
A structured discovery tool that interviews stakeholders across business units, catalogs repetitive tasks and bottlenecks, scores them for AI feasibility and ROI, and outputs a prioritized roadmap with implementation blueprints.
SaaS subscription ($500-2000/mo per org) with enterprise tier for consulting-assisted discovery workshops
The Reddit thread and broader market signals confirm this is a top-3 pain point for data/AI teams. Leadership mandates without direction create real organizational dysfunction — wasted quarters, team burnout, political landmines. The pain is acute and recurring (every budget cycle restarts the 'what AI should we build?' debate).
TAM is substantial. ~20,000 companies globally with 500+ employees have AI teams. At $1,000/mo average, that's $240M/year SaaS TAM. The adjacent consulting TAM (AI strategy engagements) is $5B+. However, the pure-software version of this might have a natural ceiling — many enterprises will want humans in the loop, which limits pure SaaS scale.
Mixed signals. Enterprises already pay $500K+ to consultants for this exact output, proving the value exists. But a $500-2000/mo SaaS tool is a different buyer (AI lead, not C-suite). The challenge: this tool needs to be 'strategic' enough to justify budget but cheap enough that a team lead can expense it. Procurement at Fortune 500s for new SaaS tools is painful. The $500/mo tier may be too cheap to be taken seriously; the $2000/mo tier may trigger procurement review.
Highly buildable. Core is a structured questionnaire/interview engine + scoring algorithm + report generator. LLMs make the interview and analysis components much more powerful than they would have been 2 years ago. A solo dev can build an MVP in 4-6 weeks: conversational AI interviews stakeholders, scores pain points against AI feasibility criteria, generates a prioritized roadmap. The hard part isn't tech — it's making the framework credible enough that enterprises trust the output.
This is the strongest dimension. Nobody owns the 'AI use case discovery' space as a standalone product. Consulting firms do it manually at absurd cost. AI platforms assume you've already decided. There's a clear gap for a $500-2000/mo self-serve tool that productizes what McKinsey charges $1M for. The window is open but will close as AI platforms add discovery features.
This is the biggest concern. Use case discovery is inherently episodic — you run it once or twice a year, not daily. Monthly SaaS subscription is hard to justify when the core job is done in 2-4 weeks. You'd need to add continuous monitoring (new pain points surfacing, progress tracking on implementation, ROI measurement post-deployment) to justify ongoing subscription. Without that, this is more of a one-time consulting engagement dressed as SaaS.
- +Massive competition gap — no one owns this space as a product, only as expensive consulting
- +Pain is real, validated, and recurring across industries (every org with an AI mandate has this problem)
- +LLMs make the core product dramatically better than what was possible 2 years ago
- +Clear wedge into enterprise accounts — start with discovery, expand into implementation tracking
- +Price point ($500-2K/mo) is in the sweet spot between 'too cheap to matter' and 'needs procurement approval'
- !Episodic usage pattern threatens subscription model — customers may churn after initial discovery phase
- !Enterprise sales cycles are 3-9 months; Fortune 500 is a brutal first market for a solo founder
- !AI platform vendors (Dataiku, DataRobot) could add discovery features as a free tier to lock in customers
- !The output (prioritized roadmap) needs to be credible enough that VPs present it to C-suite — that's a high bar for a new tool
- !Risk of being seen as 'just a fancy survey tool' if the AI scoring isn't demonstrably better than a spreadsheet
End-to-end AI platform with a 'Use Case Library' and AI project management. Includes templates for common AI use cases and helps teams go from idea to production.
AI-powered problem discovery platform that analyzes business data to surface opportunities for AI automation and optimization. Used by large enterprises for strategic AI planning.
Big consulting firms run AI opportunity assessments and roadmap workshops. McKinsey has QuantumBlack, BCG has BCG X, Deloitte has Deloitte AI Institute. They send teams to interview stakeholders and produce prioritized AI roadmaps.
Enterprise AI application platform with pre-built AI applications for specific industries. Offers an application development platform and a library of turnkey AI solutions.
Platforms that let business users build AI models without coding. They lower the barrier to AI experimentation but assume users already have a defined problem and dataset.
An AI-powered interview bot that talks to 5-10 stakeholders across business units (via chat or async forms), identifies repetitive tasks and bottlenecks, scores each for AI feasibility (data availability, complexity, expected ROI), and generates a one-page prioritized roadmap with implementation difficulty estimates. Ship as a web app where the AI lead invites stakeholders. No integrations needed for V1 — just conversations and a smart report. Make the output look like a $200K consulting deliverable.
Free: 1 discovery session with up to 3 stakeholders, basic report. Paid ($500/mo): Unlimited stakeholders, detailed blueprints, quarterly re-assessment. Enterprise ($2000/mo): Custom scoring frameworks, integration with Jira/Linear for tracking, SSO, consultant-led kickoff workshop ($5K one-time). Scale path: Evolve into continuous AI opportunity monitoring — connect to business data sources to automatically surface new use cases as the business changes. Long-term, become the 'AI strategy OS' that sits above execution platforms.
8-14 weeks. 4-6 weeks to build MVP, 2-4 weeks to land 3-5 design partners from LinkedIn/Reddit outreach to AI leads frustrated with this exact problem. First paying customer likely month 3-4. Enterprise contracts ($2K/mo tier) likely month 6-9. The Reddit thread itself is a prospecting goldmine — those commenters ARE the buyer.
- “leadership is simply saying 'Use AI, create something' without any direction”
- “too many ideas, no clear direction”
- “scaling it and bringing actual business value is always challenging”
- “technology-first initiative pretending to be a business one”
- “People push for AI use cases which are totally shitty and useless”