Your teams are experimenting with AI. Adoption is fragmented. Prioritization is weak. Nobody has decided what to kill, what to scale, and who owns the call. That gap is compounding waste, eroding credibility, and leaving your best people underleveraged.
We help CTOs and CPOs at mid-market B2B SaaS companies turn scattered AI activity into a delivery system leadership will fund and teams can execute.
Without focus and decision boundaries, AI work burns capacity, fragments delivery, and quietly erodes the credibility of the people leading it.
Expectations exceed reality. Ambition exceeds capacity. You're being squeezed from every direction.
The problem isn't knowledge. It's translating what you know into a plan you feel good about, leadership can fund, and the team can execute without blowing up the roadmap.
"AI isn't hard. Coordinating humans under pressure is hard."
It's a shifting paradigm you navigate. Most leaders respond with reasonable moves that worked before. In this environment, they backfire.
Credibility decays while expectations rise. Competitors compound learning while you're still reading articles.
Local wins, scattered tooling. Nobody can explain what's happening at a system level. Trust drops.
Massive program before clarity or permission. Delivery breaks, teams revolt, and the board asks why you bet the quarter.
Tools ship fast, behavior doesn't. You import someone else's playbook and still can't answer the board's real question.
Cloud, CI/CD. You could pick a vendor, define a 24-month roadmap, and execute top-down. Grand strategies worked because the platform held still.
Generative AI, agents, new models quarterly. Capabilities shift faster than plans. A grand strategic bet made today will be obsolete before it delivers.
The winning strategy isn't picking the perfect AI tool today. It's building an organizational structure designed to make, measure, and adjust decisions at 90-day intervals.
Three steps to co-create a defensible AI strategy for your PDLC. Then 30 days of weekly coaching to pressure-test it in the real world. You build the plan, so you own it and can defend it. The result: clarity on what to kill, what to scale, and who owns the call.
Map business pressures, PDLC friction, current AI state, and political dynamics. Build a shared picture of where you actually are.
Pressure-tested hypotheses with leading and lagging measures, realistic obstacles, and 30-day checkpoints. Reasoned bets, not promises.
Build the narrative for leadership buy-in. Explicit permission, clear decision boundaries, and a story you can take to the CEO and board.
A single, board-ready document tailored to your organization. AI value thesis, PDLC friction points, Smart AI Bets with hypotheses and ROI framework, messaging for every audience, and a first-week action plan.
After the plan is built, we stay with you. Leadership pushback, adoption friction, bet refinement, and messaging that didn't land.
AI adoption principles, operating structure guide, and experiment templates your team can use independently going forward.
Different audiences need different stories. We help you find the language that's authentic to you and effective with each group.
Need more help after? We support rollout as a follow-on engagement, or your team can execute with the plan and cadence we build together.
CEO and board expected an AI transformation plan in weeks. Competitors were marketing AI features aggressively. The engineering team was tapped out: mobile platform at 30% test coverage, rollbacks every other sprint, 9-month backlog, zero capacity for prototype discovery. Net revenue retention was slipping.
In 3 weeks, we moved them from scattered anxiety to two converged 90-day Smart Bets.
Don't let someone else define your AI direction.
You're working with people who've built and led product development organizations. We've sat in your chair.
Martin has built and scaled product development teams and led multiple transformations, including AI adoption and agile at scale. He focuses on building delivery systems that compound learning, not just output. He brings a mix of management consulting rigor and real operator experience, having sat in the seat where these decisions get made.
Scott shares Martin's passion for modernizing how products are built, shipped, and iterated. He built his career leading engineering and product teams through transitions exactly like this one. Across hundreds of organizations, he identified recurring patterns in how strong product teams operate under pressure.
Your board is going to ask why AI hasn't moved the needle yet. You need a defensible answer. Not a slide deck. A plan you built, anchored in real delivery outcomes, with clear first steps and things you decided to stop.
1 Kong Inc. / Wharton, "Enterprise AI Spending 2025" study, 2025.
2 MIT, "State of AI in Business," July 2025. 95% of enterprise AI pilots delivered no measurable P&L impact.
3 Based on direct conversations and roundtables with CTOs, CPOs, and VPs of Engineering at mid-market B2B SaaS companies conducted by OLO Solutions, 2024–2026.
4 Pluralsight, "AI Skills Report," 2025.