The AI product roadmap your board will fund.
Our 7-Phase AI Value Activation Framework helps you define what to build to drive revenue growth, build working prototypes, and deliver a defensible production-ready AI roadmap — in weeks instead of months.
Quick facts
The AI Product Strategy at a glance.
Duration
6 weeks
Audience
Mid-market product leaders (e.g. CEO, CPO, VP of Product, or similar).
Current AI maturity
Experimental, prototype-phase
Outcomes include
Investment confidence before build commitment.
A crystal clear path from concept to production.
Knowledge if your data can support the AI use case, and if not, what it will take to get it there.
Actionable business case with ROI tied to your KPIs.
Download the AI Product Strategy Fact Sheet
Purpose
Rapid AI product strategy for revenue growth grounded in engineering and economic reality.
Strategy grounded in engineering
Built by AI practitioners who have designed, shipped, and scaled production AI.
Financial modeling before build
ROI, TCO, payback, and FinOps guardrails are central to the engagement.
Speed to market
GTM faster than the competition with AI product engagements last weeks, not months.
“MojoTech is everything I could ask for in a product development partner. They quickly became trusted members of our product and engineering teams, and we relied on them to independently drive critical initiatives ranging from new feature development to enterprise integrations and legacy modernization.”
Amit Gulati
SVP Engineering
Deliverables
Six weeks. Two parallel tracks. One mission. An AI Product Strategy you can defend.
Two tracks work in parallel to validate opportunities and accelerate delivery.
AI Feature Lab Track
A forward deployed engineer pod that builds working prototypes and tests assumptions to surface hidden risks before the scope is locked.
Advisory Strategy Track
An AI strategy pod that defines the highest-value AI opportunities to invest in, builds the financial case, and produces the transformation roadmap.
AI Product Strategy
A board-ready, business-unit-specific summary of which AI products and features will create the greatest revenue growth and business value, in what order, and why. Offers a clear product vision tied to your desired value proposition and success metrics.
Investment Business Case
The financial case your CFO can defend. This deliverable includes ROI modeling, Total Cost of Ownership, payback period and FinOps governance guardrails that prevent compute cost overruns before they happen.
MVP-to-Scale Prioritized Roadmap
A sequenced gameplan to identify which AI initiatives to target first ordered from Quick Wins to Strategic Bets to Innovation Opportunities. Ready for rapid hand-off to the development team to accelerate competitive advantage.
Technical Blueprint
A developer-grade AI architectural guide designed so engineering can begin immediately with no re-discovery phase. Covers solution architecture, UX flows, AI behavior modeling, and integration direction.
Data Readiness Report
A full audit of existing data to identify any quality or sensitivity issues, gaps, or necessary improvements required to make your AI product features a reality in production.
Working Prototypes
Functional, tested, and validated prototypes on the highest priority features. Provides the evidence base for a full production build investment decision.
Results
Actions speak louder than words.
161%
increase in plan conversions for BCBSRI
30M
membership growth for Credit Karma
300%
increase in bookings for Angi
5M+
active users added since launch for MoneyLion
$1B+
revenue from b2b customers for UnderArmour
Team construction
Meet your dedicated team of AI experts.
AI Product Strategist
avg. industry experience: 14 years
Ensures you are investing in the right opportunity, with a clear customer need, differentiated value proposition, and measurable business case.
Data & AI Architect
avg. industry experience: 11 years
Determines what is technically viable, what data and systems are required, and how the product can operate securely and cost-effectively at scale.
Product Designer
avg. industry experience: 10 years
Creates an AI experience customers can understand, trust, and use, including the right human oversight and controls.
Forward Deployed Full Stack Engineer
avg. industry experience: 12 years
Turns the strongest concepts into working prototypes, tests critical assumptions in your environment, and accelerates the path to production.
Next steps
What happens after my strategy engagement?
You will have a fully-vetted AI product initiative that’s ready to hand to engineering. Now comes the fun part — bringing the right ideas to life.
FAQ
Frequently asked questions.
Most mid-market product teams are excellent at shipping products. The gap is usually not execution capacity, it is the lack of experience in feasibility testing, design for probabilistic systems, financial modeling for AI investments, and architecture guidance for consumer-facing AI at scale.
Ask your team whether they have a structured methodology to score AI opportunities against Business Value and Technical Feasibility, a model to stress-test FinOps costs, and an AI Interaction Model as a defined design artifact. If those gaps exist, that is where we add value.
Same 7-Phase AI Value Activation Framework, different lens and scope. For the AI Transformation the primary value driver is internal efficiency to create cost savings. For the AI Product Strategy engagement, the primary focus is on external products and features aimed at driving revenue growth. Mid-market product leaders are typically the right buyer for this engagement; COOs and CFOs managing operational efficiency at scale are the right buyer for the Enterprise track.
Building AI without proper vetting produces what most AI pilots become: technically functional, but never adopted at scale. The specific risks are building the wrong feature, discovering data gaps during engineering, shipping a UX that fails on uncertain AI outputs, and exceeding compute budgets with no FinOps governance in place. Spending six weeks removing those risks upfront is not slowing down, it’s the fastest path to a production AI product users actually trust.
Take action