// OLO Solutions

Your AI work will be priced as activity or as asset.

Your teams are using AI. Activity is real. Results are showing up in pockets. But almost none of it is building toward sustainable value that will move your next valuation. Product and engineering is the tricky one.

We help mid-market B2B SaaS CEOs turn fragmented AI activity in product and engineering into a small number of leadership-backed bets you can actually defend. Before the next board or PE review. If you are the product or engineering leader carrying this question without an internal partner to help you, we have sat in your seat too.

Take the AI Litmus Test Or request a fit call

ai_catalyst.status

// Right now

credibility: eroding_quietly

focus: scattered_experiments

authority: nobody_made_the_call

team_leverage: underleveraged

leadership_narrative: "we're_exploring"

// After AI Catalyst (30 days)

credibility: defensible

focus: 2-3_smart_bets

authority: explicit_and_funded

team_leverage: activated

leadership_narrative: "here's_what_and_why"

STATUS: READY

ENGAGEMENT: 30 days

// The Pressure

Unstructured AI adoption breaks teams before it breaks technology.

Without focus and decision boundaries, AI work burns capacity, fragments delivery, and quietly erodes the credibility of the people leading it.

72%

of enterprises plan to increase GenAI spending this year.¹ The money is flowing. The pressure to show results is right behind it.

40%

of AI productivity gains are lost to rework.² Teams save time generating output, then spend it fixing, rewriting, and verifying. Net gain is smaller than anyone reports.

23%

of the product and engineering leaders we've spoken with have a strategy to compound AI gains across their org. This is directional, based on OLO conversations and roundtables, not a formal market study.³ The rest are running experiments with no system to absorb what works.

89%

of organizations say upskilling is more cost-effective than hiring new AI talent.⁴ The people who can make AI work in your PDLC are already on your team. They need a system, not more tools.

20%

of companies are capturing nearly 75% of AI's economic gains.⁵ The spread is widening. The bottom 80% looks almost identical from the outside.

// The Squeeze

The Delivery Squeeze is real.

Expectations exceed reality. Ambition exceeds capacity. The leader accountable for delivery is being squeezed from every direction. Title varies. CTO. VP Engineering. CPO. VP Product. Head of Product Development. The squeeze does not pick by title. It picks by accountability.

The Delivery Squeeze - the leader accountable for delivery is 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.

// Why the Obvious Moves Fail

You're treating AI like a stable platform you install.

It's a shifting paradigm you navigate. Most leaders respond with reasonable moves that worked before. In this environment, they backfire.

⏳

Wait and see

Credibility decays while expectations rise. Competitors compound learning while you're still reading articles.

🧪

Bottom-up experiments

Local wins, scattered tooling. Nobody can explain what's happening at a system level. Trust drops.

💥

Big transformation

Massive program before clarity or permission. Delivery breaks, teams revolt, and the board asks why you bet the quarter.

🛒

Hire an AI vendor

Tools ship fast, behavior doesn't. You import someone else's playbook and still can't answer the board's real question.

// The Paradigm Shift

AI is arriving, not arrived.

The Old Game

Stable Platforms

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.

The New Game

Emerging Paradigms

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.

// How We Work

AI Catalyst: One month to turn AI activity into board-defensible bets.

We turn fragmented AI activity in your product and engineering organization into a small number of leadership-backed bets you can actually defend. We do not hand you a playbook and disappear. We execute the bets with your team. The result: clarity on what to kill, what to scale, who owns the call, and a board narrative the CEO can take into the next review.

Diagnose where AI sits today

Map AI activity across your PDLC against business pressure, delivery friction, and current results. Build a shared picture of where you are and where the bets should land.

Pick 2-3 safe, reversible bets

Hypotheses tied to measurable business outcomes with named owners and 30-day checkpoints. Safe and reversible, not heroic. What gets de-prioritized to fund each one is named in advance.

Execute and govern

We deliver the work alongside your team and build a test-approve-scale-stop governance the team runs after we leave. The CEO walks away with a board-ready narrative they own.

// execution_included We do the work. Not just the plan. The bets we pick together get delivered against your timeline, with your team. You stay the protagonist. We stay backstage.

// What You Walk Away With

Four things the CEO owns at the end.

📊

AI Readiness Assessment

Where AI should and should not sit in your delivery system. An honest read on what is working, what is theater, and what to stop.

🎯

Prioritized AI Roadmap

2-3 safe, reversible bets tied to measurable business outcomes. Named owners, 30-day checkpoints, and what gets de-prioritized to fund each one.

📖

Board-Ready Narrative

An executive document ready for your next board, operating partner, or investor review. The story you tell, anchored in bets you have already started executing.

🔧

Governance Playbook

A test-approve-scale-stop framework your team runs independently after we leave. Each new AI bet runs through it. No more ad hoc tool adoption.

// What AI Catalyst Is Not

Clear about what we don't do.

✗ Not a tool audit or vendor selection exercise

✗ Not an implementation project or code-level engagement

✗ Not a maturity assessment or benchmarking exercise

✗ Not a presentation you sit through. You co-create the plan.

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.

// In Practice

From overwhelmed to leading confidently.

BuildPlan Technologies

~$20M ARR • ~140 employees • 45 Eng/Product/Design

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.

52%

Frontend test coverage (up from 30%). Customer-impacting incidents dropped 40%. Protected the competitive advantage without adding headcount.

Discovery sessions completed. Learned that ~90% accuracy is fine when corrections are easy. Prevented wasted engineering chasing diminishing returns.

AI Strategy

Approved by leadership. The CTO delivered a vertically-aligned system, not just a feature plan. The narrative shifted from internal friction to compounding momentum.

// The Cost of Inaction

Inaction is compounding.

The Status Quo

Uncoordinated tinkering continues → Pressure mounts → Next leadership meeting: "We're still evaluating"

Eroded credibility

With AI Catalyst

Week 1: Audit & hypothesize → Week 2: Build the bets → Week 3: Align leadership

Defensible momentum

Don't let someone else define your AI direction.

// About the Founders

Operators, not consultants.

You're working with people who've built and led product development organizations. We've sat in your chair.

Martin Wilson

Co-Founder

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.

LinkedIn → Email →

Scott Varho

Co-Founder

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.

LinkedIn → Email →

// Ready?

If AI is already happening inside your product org, let's make it coherent.

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.

Request a Fit Call Read the case study

// What We Believe

Where we stand.

Alignment before action Pilots before permission

Compounding learning Isolated experiments

Deliberate delivery evolution Big-bang transformation

Decision rights and authority Mandates without clarity

Outcomes tied to the PDLC Tool adoption as progress

Credibility you can defend Slide decks nobody believes

¹ Kong Inc. / Wharton, "Enterprise AI Spending 2025" study, 2025.

² Workday / Hanover Research, "Beyond Productivity: Measuring the Real Value of AI," January 2026. Nearly 40% of AI time savings lost to rework across 3,200 respondents.

³ Based on direct conversations and roundtables with product and engineering leaders (CTOs, VPs of Engineering, CPOs, VPs of Product) at mid-market B2B SaaS companies conducted by OLO Solutions, 2024–2026.

⁴ Pluralsight, "AI Skills Report," 2025.

⁵ PwC, "2026 AI Performance Study," April 2026. 1,217 senior executives surveyed across 25 sectors; 74% of AI's economic value captured by top 20% of organizations.