✅ AI Pilots Are Over. What Comes Next?

AI is everywhere, but impact is rare. Most companies are stuck testing tools while a small group of operators redesigns workflows and seizes growth.

The latest McKinsey survey confirms it: only 33% of firms have scaled AI beyond the pilot phase. The rest? Still experimenting.

In this issue: OpenAI’s trillion-dollar shift, EY’s AI-fueled revenue jump, a strategy for getting past pilots, and tools to deploy today.

This Week’s Top 3 AI Money Moves

1. OpenAI’s Five-Year Roadmap Becomes Public

OpenAI revealed a $1 trillion+ spending commitment and is diversifying revenue streams beyond subscriptions. Targeting enterprise, government, hardware, and infrastructure.

Why It Matters: 

The shift signals that AI is no longer a fringe experiment.It’s becoming a fully-fledged strategic business instrument. Custom services, hardware and infrastructure become profit centers, not just experimentation labs.

Takeaway:

If you’re in an executive or founder role, start treating AI not as an IT project, but as a business model lever. Ask: How can AI create new revenue or defensible advantage, not just cut cost? ✅

2. Ernst & Young (EY) Reports 30% Growth in AI-Driven Consulting

EY disclosed that its AI-related revenues grew 30% in FY 2025, fueled by demand for enterprise AI transformations and governance frameworks.

Why It Matters:

Even traditional professional-services firms are shifting to an AI-first business model. It underscores: if your advisory, services or product business isn’t embedding AI, you’re at risk of being commoditized.

Takeaway:

If you’re building or managing a service or product business, ask: Where can AI differentiate our offering? Audit your workflows and client touchpoints for automation, personalization and scalability.

3. McKinsey Survey: Only ~33% of Firms Have Scaled AI Beyond Pilots

The 2025 State of AI survey shows 88% of firms use AI somewhere, but only about one-third have scaled it into major business functions.

Why It Matters:

There’s a gap between adoption and value capture. Having a ChatGPT licence is not the same as embedding AI into the core of operations and strategy.

Takeaway:

Use this as a red flag: if AI in your business is still “pilot-only,” you’re behind. Begin shifting from isolated wins to workflow redesign, leadership commitment and clear KPIs.

AI-ducation

From Pilots to Profit: AI That Scales

AI is moving from experimental to structural. Organizations are no longer asking “What could we try?” but “How do we integrate AI into core business processes?” 

According to McKinsey, high-performers don’t just deploy models. They redesign workflows, set governance and establish scaling practices.

Why It Matters:

Until AI is embedded in the operating model, it remains a side-function. The research shows that embedding AI into workflows—not just point tools—is the differentiator between incremental benefit and transformation.

Playbook – 3 Steps to Apply Today

✅ Step 1: Map your value chain and highlight 2–3 key workflows where AI could affect revenue, cost or speed.

✅ Step 2: Set leadership ownership and metrics. Appoint one executive (e.g., VP of AI Strategy) and define KPIs (e.g., % of workflows augmented, cost per transaction reduced).

✅ Step 3: Pilot-then-scale, but plan for scale from day one. Choose one high-impact workflow, design for integration (data, governance, change management), then use that as a model to roll out across functions.

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AI in Action

$1M in Pipeline in 90 Days

SaaS company Common Room used AI to augment its sales development workflow, and generated $1 million in qualified pipeline in just 90 days.

How They Did It?

• They mapped their lead pipeline first—clarifying where humans and AI would add most value.

• They deployed AI SDR agents to qualify inbound leads, route conversations, and initiate follow-ups.

• They monitored key conversion metrics: qualified leads, handoffs to human reps, and pipeline velocity.

• They refined scripts and dropped low-ROI segments based on weekly performance data.

TAIN’s Take

If your GTM depends on lead flow, this is the model.

Map your inbound flow, insert an AI layer (e.g., Claude, ChatGPT API, or a purpose-built SDR agent), and define one core metric (like “leads to qualified opps”). Iterate weekly.

This isn't about replacing reps. It’s about scaling faster with the same headcount.

What’s Next?

Thinking about building something deeper for founders, builders, and curious pros using AI…
A Skool community, private trainings, and real-world frameworks.

What would you actually show up for?
Hit reply with 1, 2, or 3:

1️⃣ A private Skool group with frameworks, live strategy sessions, and tools
2️⃣ A paid course or cohort to implement AI in my work or business
3️⃣ Just keep the free briefings. I’m good for now.

📩 Your answer shapes what I build next. Reply takes 2 seconds.

Thanks for reading!

That’s this week’s AI Newsroom. Built to help you move faster, work smarter, and stay ahead of the AI curve.

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Stay sharp. Move fast.
- Jason Smircich