🤝 Join our FREE course on Feb 11th: How to Avoid AI Project Failures For Org Leaders

Most AI pilots don’t fail because of the tech.
They fail because orgs fund the wrong things, in the wrong order.

In this 30-minute Lightning Lesson on Maven, I break down:

• why AI pilots stall before ROI
• the decision mistakes that quietly kill momentum
• a simple way to prioritize AI bets that actually stick

→ Reserve your Free seat (limited)

Weekly finds

📰 AI Insight

1. Claude’s App Takeover Anthropic shoved Slack and Asana inside Claude. Your AI isn’t just a chatbot anymore; it’s basically your new, highly-efficient (and non-complaining) office manager.
   

2. Chrome Goes Agentic Google’s “Auto Browse” lets Chrome click and finish tasks for you. Finally, a browser that works as hard as you pretend to.
   

3. Google’s World Builder Google’s Genie turns text into explorable 3D worlds. Great for gaming, better for training robots to not crash into your office furniture.
   

4. Moltbot: The Viral Agent Moltbot is the viral agent that learns new skills on the fly. It’s basically the “Choose Your Own Adventure” of AI automation- guide here.
   

5. AlphaGenome Decodes Life DeepMind’s AlphaGenome is decoding the 98% of DNA scientists ignored. AI is finally reading the manual for being human.”

THE ISSUE

1. The Problem You’re Already Living With

You didn’t decide not to use AI.

You approved the tool. You greenlit the experiment. You told the team to start small and move fast.

And yet, months later, leads still wait, ops still patches workflows manually, and smart people spend time compensating for systems that were supposed to help them move faster.

Nothing is obviously broken. But nothing is meaningfully better either.

That’s the frustration most mid-market operators feel right now. Not confusion about AI’s potential, but irritation with how slow and effort-heavy it becomes once it hits real operations.

The issue isn’t belief.
It’s resourcing.

2. What Most Teams Get Wrong About AI Resourcing

Most AI advice lives at the extremes.

One side insists you must build everything in-house or you’ll never truly own the capability. The other promises modern tools make AI plug-and-play if you just pick the right platform.

Both ignore how mid-market businesses actually operate.

You’re running a real company with real constraints, limited spare capacity, and workflows that evolved under pressure, not in a clean design session. AI doesn’t land in theory. It lands in that reality.

This is why the AI consulting vs in house debate often goes nowhere. It frames AI as ideology instead of execution.

The real question is simpler:
Who is going to make this work inside your systems, your data, and your pace of decision-making?

3. Build, Buy, or Partner , What These Options Really Mean

“Build vs buy vs partner” sounds strategic. In practice, it’s a capacity decision.

Build (In-House AI)

Building internally means hiring or reallocating people to design, deploy, and maintain AI over time. You gain control, but you also take on slower momentum, leadership overhead, and ongoing maintenance.

In mid-market companies, internal AI rarely fails loudly. It stalls quietly as priorities shift back to revenue, customers, and fires that can’t wait.

Building works when AI is core IP. Otherwise, it often becomes heavier than expected.

Buy (AI Tools & Platforms)

Buying software is fast. Implementation is not.

Most tools assume clean data, stable workflows, and teams that know how to act on AI output. When those assumptions break, the tool technically works, but the business doesn’t change.

Buying isn’t wrong. But without clear ownership, it turns into shelfware with a subscription.

Partner (External AI Partner for Operations)

Partnering sits between build and buy. You keep ownership of outcomes while borrowing execution speed and pattern recognition from a team that’s already made the mistakes.

For many mid-market companies, this becomes the most practical AI implementation option because it aligns with reality: urgency, limited capacity, and pressure to see results in operations, not slide decks.

The value isn’t buzzwords. It’s fewer false starts.

4. Why This Matters Now

If AI resourcing stays unresolved, nothing explodes tomorrow.

Instead, drag accumulates.

Leads wait longer than they should. Ops teams absorb friction manually. Managers hesitate to trust reports. Decisions slow because data doesn’t quite line up.

This is where AI transformation for SMB and mid-market businesses actually shows up, not as innovation, but as friction removal.

The teams that win don’t feel futuristic. They just move faster with less effort.

5. How AI Resourcing Actually Works

Regardless of whether you build, buy, or partner, successful teams follow the same progression.

5.1 Start With One Real Constraint

Not a roadmap. One place where work piles up or decisions slow down.

AI creates leverage by removing constraints, not by adding complexity.

5.2 Get Data to “Usable,” Not Perfect

AI doesn’t need perfect data. It needs honest data.

Most failures happen when teams pretend their data is cleaner than it is. Usable beats polished every time.

5.3 Assign Clear Ownership

Someone must own outcomes, not just setup.

Who monitors performance? Who fixes drift? Who decides what improves next? Without this, AI becomes no one’s priority.

This is where many AI for business efforts stall.

5.4 Embed AI Where Decisions Already Happen

If AI lives in a separate dashboard, it gets ignored.

Successful implementations surface insight inside CRMs, ops tools, or systems teams already trust.

5.5 Measure Friction Reduction

Skip vanity metrics.

Track time saved, errors reduced, and handoffs simplified. That’s a real mid market AI strategy.