/the gap

Most AI pilots die between the demo and the P&L. I get them across.

The barrier is rarely the technology. It's adoption, ownership, and the work of wiring AI into how your team actually operates. In addition to strategy, I do the hands-on implementation, so a promising pilot becomes a number you can take to your board. If it won't pay off, we don't move to the next step.

Schedule a consultation → Send a message ~ 30 min · no pitch deck

$A faceted glass crystal refracting light, the clarity AI delivers when implementation is done right$

/results

5% → 65%

AI tool adoption driven across 200+ users

70%

Reduction in campaign build time with an AI automation stack

$275M

Revenue influenced through AI personalization and automation

2,600+

Hours hands-on implementing AI in Claude Code

/the problem

Why most AI initiatives stall after the pilot.

AI rarely stalls because the technology doesn't work. It stalls in the gap after the purchase: tools get bought, then never wired into how your team actually works. The barriers are organizational, not technical, and that's where the spend quietly disappears.

76%

AI use cases now bought, not built

16%

of AI deployments that are truly autonomous agents

~80/20

people-and-process vs. technology in failed AI projects

Market data: Menlo Ventures & MIT NANDA, 2025

AI pilots that go nowhere

Your team ran a trial. People were impressed. Nothing changed. No adoption plan, no measurable outcome, no next step.

Vendor demos, not strategy

You've sat through twenty vendor pitches. Every tool promises ROI. Nobody has helped you figure out which problems to solve first.

Tools without workflows

ChatGPT is installed on everyone's laptop. Most people use it to reword emails. The productivity gain is near zero.

Consultants who theorize

You hired someone to build a strategy. You got a 60-page deck. Your team still doesn't know what to do on Monday morning.

IT involvement that stalls

Every AI initiative hits a procurement queue. By the time it clears, the business context has shifted and momentum is gone.

No one owns adoption

Implementation happens. Training doesn't. Six months later you're paying for tools that 10% of your team uses regularly.

PRIME

/the framework

The PRIME framework for AI implementation.

This is how I close that gap: five phases that move you from AI curiosity to a result your team actually uses. Each one is practical and hands-on. No theoretical detours.

Potential Mapping

Identify where AI delivers real value in your business. Not hypothetical futures. Specific workflows, roles, and processes.

Roadmap & Strategy

Prioritize by impact and feasibility. Build a 90-day execution plan your team can actually execute without a transformation program.

Implementation Planning

Select tools, define success metrics, and sequence rollout so quick wins fund larger initiatives.

Migration & Execution

Hands-on implementation. I build alongside your team, not a slideshow handoff.

Enablement & Adoption

Training that drives adoption past 70%. Practical, role-specific, measured.

Full framework breakdown →

/services

How I work with companies.

01 Strategy

AI Strategy & Roadmap

Map where AI creates real value in your business, then a 90-day plan that ships, prioritized by impact, not a 60-page deck.

How it works

02 Deploy

AI Implementation & Deployment

I build and wire AI into your existing systems and new platforms, and get it live in production. Hands-on, not a slideshow handoff.

What I build

03 Adopt

Training & Adoption

Role-specific enablement for Claude, Microsoft Copilot, and ChatGPT that drives adoption past 70%, on the tools your team actually uses.

What's covered

See client results →

Ronan Keane, AI Strategy & Implementation Consultant

/about

Two decades of B2B operations, now pointed at AI.

I spent 20 years leading marketing and operations at B2B tech companies (Verizon, GTT Communications, Vonage Business), managing eight-figure budgets and generating hundreds of millions in revenue.

Now I help SMB and mid-market teams reach those results faster with AI, building hands-on since 2023. Real implementation, not slideware: usually a first working use case in two to three weeks, built to get used, not shelved.

Certifications

MIT · Applied Generative AI for Digital Transformation
Anthropic · Claude Certified Architect (eligible, in progress)
Wharton Online · AI Strategy for Business

Learn more

Testimonials

What my clients say

Genz & Associates

"Ronan's framework is brilliant. It provided the clarity and structure we needed to get executive buy-in and deploy our first AI model successfully."

Lisa Cool · Chief of Staff to CEO

The ABM Agency

"We went from guessing to predicting. The sales forecasting model has been incredibly accurate, directly impacting our bottom line."

Vincent DeCastro · President

Kodiak Solutions

"The AI readiness audit was a game-changer. Ronan identified critical data gaps we never would have seen, saving us months of rework."

Chris Jones · CPO

/questions

Straight answers to the questions executives ask.

How is this different from the pilot that went nowhere?

Most pilots stall for organizational reasons, not technical ones. Roughly 80% of the failure is people and process. So I don't stop at a working demo. Every engagement gets a named owner, an adoption plan, and a measured result before we scale. And if a use case won't pay off, we don't move to the next step. That's how I drive adoption past 70%, instead of leaving you with tools 10% of the team touches.

Isn't most of the value in AI agents now?

Not yet, and not where you should start. Only about 16% of deployed AI systems are true autonomous agents. The rest are copilots and fixed workflows. The proven returns today are in copilots and back-office automation. I get those working first, then stage agentic work where it's warranted.

I keep hearing 95% of AI projects fail. Is that real?

MIT NANDA reported that 95% of generative-AI pilots showed no measurable financial return within six months. That figure is debated: it used a narrow definition and a small sample. But the underlying point holds: most projects stall, and they stall for organizational reasons, not technical ones. That's exactly the gap I'm hired to close.

Should we build our own AI or buy tools?

For most teams, buy. Across the market, 76% of AI use cases are now purchased rather than built. Buying gets you to value faster and avoids carrying a custom system you can't staff. The real work is choosing the right tool, configuring it for your workflow, and driving adoption.

How do we keep AI costs from running away?

Treat it like a budget line, not a flat subscription. Usage-based billing is now the norm, so you need spend caps, role-based access to expensive models, and a real-time view of spend. I set this up as part of implementation, so cost control is built in, not bolted on after the first surprising invoice.