I’ve spent most of my career thinking about development solutions and software as a bottleneck for scaling businesses - but now I think AI/AGI is changing that completely.

Since AI makes intelligence cheap and widely available, what do you think becomes the new bottleneck for building a great company?

Capital? Distribution? Trust? Talent? Data? Something else?

As a founder in enterprise tech, I’m curious how others think about this:

Would you hire a developer who ships 3× faster with AI, produces solid code, but has materially less understanding of the systems they’re changing?

My hesitation isn’t about AI-assisted engineers being less capable.

I just wonder if, while implementation speed is becoming cheap, system understanding and proper expertise feels like the bar is dropping more and more.

Where do you think hiring teams need to draw the line?

A few years ago, digital transformation meant moving to the cloud, replacing spreadsheets with business software, and automating repetitive tasks. Those projects helped companies become more efficient, but they didn't fundamentally change how decisions were made.

Today, the conversation is different.

Businesses aren't asking, "How do we digitize this process?" They're asking, "How do we make smarter decisions without hiring twice as many people?"

That's where AI is making the biggest difference.

Not because it's replacing employees or magically solving every business problem, but because it's helping organizations work with the data they already have. Instead of spending hours pulling reports, searching through documents, or switching between half a dozen applications, teams can get meaningful answers in seconds.

For business leaders, that's the real opportunity.

The Problem Was Never a Lack of Technology

Most organizations already have plenty of software.

There's a CRM for sales, an ERP for operations, collaboration tools for employees, dashboards for executives, and dozens of other applications running behind the scenes.

The challenge is that all these systems often operate independently.

Sales has one version of the customer.

Finance has another.

Operations has its own data.

Support teams work somewhere else entirely.

When someone needs a complete picture, they usually end up gathering information manually. It takes time, introduces errors, and slows decision-making.

AI doesn't replace those systems. It connects them in a way that makes information easier to understand and use.

Instead of hunting for answers, employees can simply ask questions and receive insights backed by enterprise data.

That's a much bigger shift than simply automating a workflow.

Digital Transformation Has Become More Human

Ironically, AI is making work feel more human.

Think about how much of the average workday disappears into repetitive tasks.

Searching for files.

Writing meeting summaries.

Preparing weekly reports.

Answering the same customer questions.

Updating spreadsheets.

None of those activities create real business value. They're necessary, but they're not why people were hired.

When AI handles those repetitive jobs, employees can focus on the work that actually requires experience, judgment, and creativity.

Sales teams spend more time building relationships.

Customer support agents solve complex issues instead of repeating scripted answers.

Managers spend less time compiling reports and more time making decisions.

That's what digital transformation should have looked like all along.

AI Works Best When It Solves Real Problems

One mistake many companies make is starting with the technology instead of the business challenge.

They hear about generative AI and immediately ask,

"Where can we use AI?"

A better question is,

"What's slowing our business down?"

Maybe customer service teams spend too much time searching internal documentation.

Maybe finance teams manually process hundreds of invoices every week.

Maybe engineers waste hours looking for technical documentation.

Those are the kinds of problems AI is good at solving.

The goal isn't to use AI everywhere.

It's to use it where it creates measurable improvements.

Success Starts With Better Data, Not Bigger Models

People often assume the latest AI model is the key to success.

In reality, most enterprise AI projects succeed or fail because of data.

If information is outdated, incomplete, or spread across disconnected systems, even the smartest AI won't deliver reliable answers.

That's why many organizations spend more time organizing and connecting their data than choosing an AI model.

Once that foundation exists, AI becomes significantly more valuable because it has the context needed to produce useful responses.

Good data isn't the exciting part of AI.

But it's almost always the most important part.

Business Leaders Should Think Beyond Cost Savings

Reducing costs is usually the first benefit companies notice.

Routine work becomes faster.

Support tickets decrease.

Reports take minutes instead of hours.

But that's only part of the story.

The bigger advantage is speed.

Organizations can respond to customers faster.

Executives can make decisions with more confidence.

Employees spend less time looking for information and more time acting on it.

In competitive markets, that ability to move quickly often matters more than cutting operational costs.

AI Adoption Is as Much About People as Technology

Buying AI software is relatively easy.

Helping people change the way they work is much harder.

Employees naturally have questions.

Will AI replace my role?

Can I trust its answers?

How much should I rely on it?

Those concerns are normal.

The organizations seeing the best results don't force AI into every process overnight.

They introduce it gradually.

They train employees.

They encourage feedback.

Most importantly, they position AI as a tool that supports people instead of replacing them.

That approach builds confidence, and confidence drives adoption.

The Companies Pulling Ahead Aren't Waiting

Across every industry, businesses are experimenting with AI in practical ways.

Some are improving customer support.

Others are accelerating software development.

Many are making internal knowledge easier to access.

None of these projects sound revolutionary on their own.

But together, they create a business that's faster, more responsive, and better equipped to adapt as markets change.

That's where the competitive advantage comes from.

Not from having the newest technology, but from using it consistently to improve everyday work.

Looking Ahead

Digital transformation has never really been about technology.

It's always been about helping businesses operate more effectively.

AI simply gives organizations a better way to achieve that goal.

The companies that succeed won't be the ones chasing every new AI trend. They'll be the ones solving real business problems, building strong data foundations, and giving their teams tools that genuinely make work easier.

Technology will continue to evolve.

Business priorities will continue to change.

But one thing is becoming increasingly clear: organizations that learn how to combine people, data, and AI effectively will be in a much stronger position for whatever comes next.