IT May Kill Your Company

AI Is Here. Can IT handle it?

TL;DR: Technology transfer time (the period between "this new tech exists" and "we're actually using it in production." for enterprise software has been at 18-36 months for decades. AI compresses that to 2-8 weeks, breaking decades of IT best practices around procurement, evaluation, and deployment. The human mental momentum of "this is how we've always done it" is even harder to overcome than the process challenges. Treat AI adoption like HR: interview (try things), select candidates, hire at least two (different AIs have wildly different strengths), and fire fast when they're not working.

What the Heck is Technology Transfer Time?

As mentioned in the TL;DR, Technology transfer time is the period between "this new tech exists" and "we're actually using it in production." For decades, this followed a predictable rhythm:

• Discovery Phase: 3-6 months (someone reads about it at a conference)
• Evaluation Phase: 6-12 months (PoCs, vendor meetings, PowerPoints)
• Procurement Phase: 3-6 months (legal, security reviews, budget battles)
• Implementation Phase: 6-12 months (integration, training, rollout)

Total: 18-36 months. This became muscle memory for IT organizations. Budget cycles aligned with it. Career progression assumed it. Risk management frameworks codified it.

Then AI Showed Up and Said "LOL, No"

Here's what's happening in 2026:

A developer hears about a new AI coding assistant on Monday. By Tuesday, they're using it. By Friday, they've 10x'd their productivity on a critical feature. By the following Monday, three other teams are using it too.

Technology transfer time: 2-8 weeks.

This isn't a 20% improvement. This isn't even a 10x improvement. We're talking about 50-100x compression in adoption cycles.

The Old Playbook is on Fire

Every IT best practice from the last 30 years assumed that slow 18-36 month cycle:

• Comprehensive vendor evaluation: Dead. By the time you finish your comparison matrix, five new competitors launched and two you evaluated are obsolete.
• Enterprise-wide standardization: Dead. Teams can't wait 18 months for IT to bless their tools. They'll use them anyway (hello, shadow IT on steroids).
• Formal training programs: Dead. The tool will have three major versions by the time you finish developing training materials.
• Change management: Dead. You can't "manage change" that happens faster than your change management process.

This isn't IT incompetence. The processes weren't bad—they were optimized for a world that is burning out instead of fading away.

The Human Problem is Harder

IT professionals have 20-30 years of muscle memory telling them how technology adoption works. Their intuition, their risk assessment, their planning horizons—all calibrated for that 18-36 month cycle.

You can't just tell someone "hey, ignore three decades of professional experience and move 50x faster." Their brain won't let them. It feels reckless. It feels dangerous. It feels wrong.

But the business world doesn't care about your feelings. While you're running your six-month evaluation, your AI-embracing competitors are already on their third iteration with a tool you haven't approved yet.

Treat AI Like HR, Not Like IT

Here's a mental model that I think works:

Interview (Try Things Out)

Don't do six-month evaluations. Spin up trials of 5-10 different AI tools. Give teams a week with each. See what actually works in your environment with your problems.

Select Best Candidates

After quick trials, pick the 2-3 that showed real promise. Not the ones with the best PowerPoint decks—the ones that actually delivered value.

Hire at Least Two

This is critical: don't go all-in on one AI. Different AIs have radically different strengths and weaknesses:

• Claude excels at analysis and following complex instructions
• GPT-4 dominates at broad general knowledge and creative tasks
• GitHub Copilot owns the code completion space
• Cursor integrates development workflow better than standalone tools
• Grok is great at "what's happening NOW"'"

Having multiple AIs in your stack isn't redundancy—it's portfolio management. You wouldn't hire only one type of engineer for your entire org.

Fire Fast

If an AI isn't delivering value within 2-4 weeks, kill it. Don't wait for the quarter to end. Don't write a retrospective. Don't form a committee.

And when a better option emerges (and one will, probably next month), interview it and hire it if it's better. This will feel wasteful to IT veterans. It's not—staying with inferior tools is wasteful.

The Five Choices for 2026

I'm going to be blunt: things are going to change at an insane rate this year. And as a human professional, you have exactly five choices:

1. Ignore AI at Professional Peril

You can pretend this isn't happening. You'll be unemployed or irrelevant within 24 months. This isn't a judgment—it's just math. Your competitors won't ignore it.

2. Embrace AI Obsessively for Professional Gain

Learn everything. Experiment constantly. Build your skills with multiple AI tools. Become the person who knows how to 10x team productivity. You'll be absurdly valuable.

3. Work in Very Hands-On Jobs

Plumber. Electrician. Carpenter. Massage therapist. Hairstylist. Jobs where physical presence and manual dexterity matter. AI can't (yet) replace these. They're also increasingly well-paid as everyone else floods into knowledge work.

4. Get Professional Licensing

Jobs that require state/professional licensing have regulatory moats. Bartenders need licenses. Barristers need licenses. Real estate agents need licenses. Accountants need licenses. These create speed bumps that slow AI disruption.

5. Retire and Enjoy the Show

If you're in your late 50s or 60s with solid retirement savings, this might be the play. Pour a drink, get some popcorn, and watch the most dramatic economic transformation in human history from the sidelines.

What This Means for Leaders

If you're leading IT or engineering orgs:

Stop optimizing for 18-month cycles. Your risk framework, your budgeting process, your vendor management—all of it needs to be rebuilt for 2-8 week cycles.

Empower teams to experiment. You cannot centrally control AI adoption. You can only guide it. Set guardrails (security, compliance, cost) and let teams run.

Budget for churn. You're going to adopt and abandon 3-5x more tools than you used to. That's not failure—that's adaptation.

Hire for learning velocity, not experience. Someone with 20 years of traditional IT experience but low learning velocity is now less valuable than a junior person who can master new AI tools in days.

The Uncomfortable Truth

We're not going back to 18-month technology adoption cycles. That world is gone.

The organizations that survive are the ones that can rewire their processes, their budgets, and most importantly their mental models to operate at AI speed.

Everyone else? They'll be writing case studies for MBA programs about "what happens when you optimize for a world that no longer exists."

Choose your path. But choose fast—because in 2026, standing still is moving backward at 50x speed.

Things are going to get fun, weird, and fast than anyone more than anyone is ready for. The only rational response is to learn everything you can, try everything that might work, and be ready to completely change your approach every 90 days. Welcome to the future. It's going to be wild.