Kevin King | AI Transformation @KevinKingAI

Give me a business for 60 minutes. I don’t want to hear about their AI strategy. I want: • Their workflows • Their bottlenecks • Their labor costs • Their revenue leaks • Their customer journey • Their data Then I’ll map: Where AI should actually be deployed.

Most companies are asking: “Where can we use AI?” Wrong question. Ask: “Where are we currently paying humans to repeatedly make decisions, move information, or respond to predictable events?” That’s where I’d start looking.

AI agents aren’t the product. The process they replace is. Nobody wakes up wanting an AI agent. They want: → fewer missed calls → fewer hours of admin → faster follow-up → more booked appointments Build the system around the outcome. Not the technology.

I’m taking on two companies for an AI Transformation Blueprint. Best fit: • growing company • meaningful operational complexity • leadership mandate to use AI • uncertainty about the highest-value starting point DM “BLUEPRINT” with your industry and team size.

The next wave of AI isn’t another chatbot. It’s AI quietly taking over the work nobody wants to do. • Intake. • Follow-up. • Scheduling. • Reporting. • Data entry. • Lead qualification. I’m building these systems now. I’ll show you exactly how they work.

AI agents aren’t the opportunity. Replacing boring business processes with AI is. The people selling “AI agents” are selling the technology. The people making money will sell the outcome.

Before automating a workflow, map: Trigger → Context → Judgment → Action → Approval → Measurement Skip one of these and the automation will eventually confuse speed with correctness.

An AI agent should earn the right to act. First it observes. Then it recommends. Then it acts with approval. Only then should limited actions become autonomous. Autonomy should be graduated based on evidence—not granted because the demo looked impressive.

Most AI projects fail before implementation. They fail when the company chooses: • a vague objective • an isolated use case • no accountable owner • no success metric • no adoption plan The expensive mistake is often not bad engineering. It is building the wrong thing.

Five signs your company is ready for a company brain: 1. Answers depend on who you ask 2. Decisions are buried in meetings 3. Employees recreate existing work 4. Leadership receives conflicting numbers 5. Important context disappears when someone leaves

AI ROI is not only hours saved. It is also: • the deal rescued before it dies • the renewal caught before churn • the mistake found before it spreads • the risk surfaced before it becomes a crisis • the decision made while it still matters Measure outcomes, not clicks.

I’m looking for companies where leadership has to chase people for answers. That is often the starting point for: • a company brain • executive intelligence • proactive operational alerts • cross-functional coordination DM “SIGNAL” and tell me where information gets stuck.

Executives do not need another dashboard. They need a concise decision brief: • What changed? • Why does it matter? • What happens if we wait? • What are the available options? • What action is recommended? AI should shorten the distance between signal and decision.

One question I ask during AI opportunity discovery: “What problem does leadership repeatedly learn about later than they should?” That answer often reveals the need for a proactive intelligence system. What would your answer be?

Tell me your: 1. Industry 2. Employee count 3. Most frustrating recurring process I’ll tell you the first AI opportunity I would investigate.

Quick AI readiness test: Can your company clearly answer: • Which AI use case comes first? • What outcome will it improve? • Who owns it? • What data can it access? • When must a human approve the action? If not, you are not ready to scale AI yet.

Most companies have the information they need. The problem is that it is scattered across: - Email - Meetings - CRMs - Documents - Spreadsheets - People’s heads The opportunity is not another search bar. It is organizational memory.

I’m opening two AI Transformation Blueprint slots. We map: • highest-ROI use cases • current and future workflows • data requirements • governance boundaries • implementation sequence DM “BLUEPRINT” if your company knows it needs AI but does not know what to build first.

The first 3 places I look for AI opportunities: 1. Revenue slipping through cracks 2. Decisions arriving too late 3. Employees hunting for information These are usually more valuable than automating another administrative task.

The first 3 places I look for AI opportunities: 1. Revenue slipping through cracks 2. Decisions arriving too late 3. Employees hunting for information These are usually more valuable than automating another administrative task.