DEV Community: Alex Rodov

I don't know what software will look like in five years. I have a guess about who we'll still trust.

Alex Rodov — Thu, 27 Aug 2026 22:56:36 +0000

A few honest thoughts on AI-native software, the future of enterprise technology, and the one thing I keep betting on anyway.

Alex Rodov · PMO Leadership, PMP, Microsoft MVP Alumni

I try not to make predictions about technology anymore.

Not because I don't have opinions, but because the pattern I keep noticing is that the best insights usually come from staying curious longer than everyone else.

So take this less as a forecast and more as something I've been turning over in my head.

A few months ago, a CIO told me his company had just walked away from a platform they'd spent four years building their processes around.

It wasn't broken.

A leaner, AI-native alternative could do the same job for a fraction of the cost, and could be set up in weeks instead of quarters.

He actually said something pretty cool:

"We were maintaining a museum."

I keep thinking about that sentence.

Because I think it's about to apply to a lot more of us than we'd like to admit.

Nobody Knows What's Coming

I've been reading through forecasts from people who genuinely study the future of software and artificial intelligence, and something jumps out.

The confident predictions don't agree with each other.

Some think we're heading toward software that builds itself on the fly, creating a different interface for every person instead of forcing everyone into the same product.

Others are betting on something stranger:

AI agents that stop behaving like tools and start showing up more like coworkers.

Agents with responsibilities.

Agents with context.

Agents that become part of how teams actually operate.

The categories themselves are starting to blur.

What we currently call "software" may not look much like software in five years.

And honestly, I find that uncertainty reassuring.

Because the people making the boldest predictions increasingly acknowledge how much remains unsettled.

AI adoption is accelerating, but many organizations are still hesitant to hand over meaningful autonomy.

The problem isn't necessarily the technology.

It's trust.

Can we trust the data?

Can we trust the model?

Can we trust the recommendation?

And perhaps most importantly:

Can we trust the person or organization standing behind it?

Maybe the interesting question isn't what the software will do. It's who we'll still believe when it tells us something.

The Real AI Challenge Isn't Capability. It's Trust.

This is where I think the conversation about AI software gets interesting.

We spend an enormous amount of time talking about what AI can do.

Generate.

Analyze.

Predict.

Automate.

Summarize.

Build.

But capability is becoming less scarce.

Trust isn't.

A system can produce an impressive answer in seconds.

That doesn't mean you should make a million-dollar decision based on it.

A platform can automate a workflow.

That doesn't mean the workflow should be automated.

An AI agent can identify a risk.

That doesn't mean it understands the consequences of acting on it.

As AI becomes more capable, the question shifts from:

"Can the technology do this?"

to:

"Should we trust it to do this?"

That is a much harder question.

The Human Skills That Become More Valuable

Buried in almost every serious discussion about the future of work is a version of the same quiet admission.

As technology becomes better at handling routine work, the human capabilities surrounding that technology become more valuable.

Judgment.

Adaptability.

Context.

Communication.

The ability to read a room.

The ability to read a client.

The ability to recognize when a number everyone trusts is actually wrong.

These aren't skills that disappear because AI becomes better.

They become more important because AI becomes better.

I notice this in my own conversations constantly.

People don't ask me what the platform does anymore within the first five minutes.

They ask:

"Who will I be talking to when something breaks?"

That question didn't used to come first.

I think it's becoming one of the most important questions in technology.

The Enterprise Software Model Is Changing

For decades, enterprise software competed on features.

More functionality.

More integrations.

More dashboards.

More configuration.

More customization.

That model worked when software was expensive to build and difficult to change.

AI is changing the economics.

If an AI-native platform can understand natural language, adapt workflows, generate reports, interpret data, and configure itself around the user's needs, then the old advantage of simply having more features starts to weaken.

The software becomes less important as a static product.

It becomes more important as an adaptive system.

And that creates a difficult question for established software companies:

What happens when the product you've spent ten years perfecting can be replaced by something that takes ten weeks to build?

That CIO's "museum" comment suddenly feels less like an isolated story.

It starts to look like a warning.

A Guess About the Next Five Years

So here's my honest, hold it loosely guess about where this goes.

I think the software itself is going to keep changing shape faster than any of us can plan around.

New interfaces.

New AI agents.

New ways of interacting with systems.

New categories that don't even have names yet.

I think many platforms we consider essential today will look very different in five years.

Some will evolve.

Some will disappear.

Some will remain in place simply because replacing them is harder than maintaining them.

And some will become museums.

But I don't think the appetite for genuine relationships changes at the same speed.

If anything, the less certain the technology feels, the more people seem to want a real person who understands their business, picks up the phone, and stays around long enough to actually know them.

That part of this industry feels stubbornly human.

And I say that as someone whose company is built on AI.

Technology Can Scale. Trust Has to Be Earned.

This may be the paradox of the AI era.

Technology is becoming dramatically easier to scale.

Trust isn't.

You can deploy software globally in hours.

You can generate content instantly.

You can automate decisions across thousands of workflows.

But you can't automate your way into a meaningful relationship with a customer.

You can't shortcut credibility.

And you can't build trust simply by adding an AI label to a product.

The companies that matter in the next generation of software may not necessarily be the ones with the most sophisticated models.

They may be the ones that combine sophisticated technology with something much older:

Knowing their customers.

Understanding their problems.

Being accountable for the outcome.

And still being there when things get complicated.

What I Would Bet On

If you asked me today what software will look like in 2031, I'd probably disappoint you.

I don't know.

Maybe interfaces will disappear.

Maybe AI agents will become the primary way we interact with enterprise systems.

Maybe software will generate itself around the task we're trying to accomplish.

Maybe today's platforms will look as outdated as that four year old system the CIO described.

Probably some combination of all of it.

But if you ask me what I think will still matter, I have a much stronger opinion.

Judgment will matter.

Relationships will matter.

Accountability will matter.

Trust will matter.

And the people who understand that will have an advantage regardless of what the technology looks like.

The Part I Don't Want to Predict

Maybe in five years I'll hand this essay to an AI and ask, gently, how wrong I was.

And it will answer, as they always do now, with impeccable manners and a faint, unplaceable condescension.

Or maybe I'll hand it to you instead, over coffee, and you'll tell me the truth.

I know which conversation I'd rather have.

So consider this less a conclusion than an invitation.

Technology will keep changing.

The interfaces will change.

The platforms will change.

The business models will change.

But the reason people choose to trust one company over another may prove surprisingly resistant to all of it.

That's the bet I'm making with how we build.

And more importantly, with how we show up for the people who trust us with their work.

I don't know what software will look like in five years.

I have a guess about who we'll still trust.

Alex Rodov

We delivered every project on time. So why did the room go quiet?

Alex Rodov — Thu, 13 Aug 2026 22:16:19 +0000

A short note on the VMO, and why it may be the most useful evolution in project management right now.

Alex Rodov · PMO Leadership, PMP, Microsoft MVP Alumni

I've sat through this meeting more times than I can count.

The PMO presents its quarterly review. The numbers look good:

Projects are on schedule.
Budgets are under control.
Governance is being followed.
Delivery targets are being met.

Then someone asks the question that changes the room:

"So what did this actually do for the business?"

Suddenly, the dashboard has less to say.

Because delivering a project successfully and delivering business value are two different things.

And most PMOs were built to measure the first.

A Simple Idea Called the VMO

There's a name gaining attention for the next step: the Value Management Office, or VMO.

Don't let the acronym put you off. The idea is simple.

A traditional PMO asks:

Is the project on time?
Is it on budget?
Is it on scope?
Are governance requirements being met?

A VMO asks one more question:

Is this project still worth doing?

More specifically:

Is it still creating the value we promised when we started?

That question is harder to ask.

It means being willing to stop a healthy project because the business case has changed. It means looking at a portfolio like an investment portfolio and asking what each initiative is actually returning.

A project can be perfectly managed and still be the wrong project.

A PMO earns trust by finishing projects. A VMO earns a seat at the table by knowing which projects deserved to be finished.

From Project Delivery to Business Outcomes

This is the shift at the heart of value based project management.

Traditional project reporting focuses heavily on delivery performance:

Schedule
Cost
Scope
Risks
Milestones
Resources

Those metrics matter.

But they don't always answer the executive question:

What changed because we invested in this project?

A value focused approach adds another layer.

For example:

Did the project increase revenue?
Did it reduce operational risk?
Did it improve customer experience?
Did it reduce costs?
Did it create a measurable strategic advantage?
Did the expected benefits actually materialize?

This changes the conversation from "Did we deliver?" to "Did delivery matter?"

That is where VMO thinking becomes powerful.

This Shift Is Already Happening

The move toward more strategic PMO leadership is already underway.

A decade ago, only about a third of PMOs could say they had a clear mandate. Today, that figure is over half.

Nobody necessarily announced the transition.

It happened gradually as stronger PMO leaders started asking better questions.

Instead of opening a leadership meeting with delivery statistics alone, they increasingly talk about what the work actually changed:

Revenue protected. Risk avoided. Benefits realized. Strategic objectives advanced.

That's VMO thinking.

Many organizations simply haven't put the name on the door yet.

Why the VMO Matters Now

Here's the uncomfortable part.

Measuring value has always been difficult.

It requires teams to:

Track expected benefits.
Keep business cases current.
Connect projects to strategic objectives.
Measure outcomes after delivery.
Revisit assumptions when business conditions change.

Most PMOs already wanted to do more of this.

The problem was time.

Project professionals have historically spent enormous amounts of effort collecting updates, preparing reports, reconciling information, and building executive presentations.

That is changing.

As AI takes over more of the reporting and data collection workload, it gives PMOs something they desperately need:

time to think about value.

AI doesn't create a VMO.

It simply removes one of the biggest practical excuses for not becoming one.

The AI Advantage: Less Reporting, More Judgment

This is where AI can change the role of the PMO.

Imagine a system that can continuously process project data and surface:

Schedule variance
Cost variance
Resource constraints
Emerging risks
Portfolio trends
Performance changes

The PMO doesn't have to spend Friday afternoon assembling the report.

Instead, the conversation can move toward the questions that actually matter:

Why is this happening?

What does it mean for the business?

Should we continue investing?

What value are we protecting or creating?

That is a much more strategic use of project management expertise.

The technology handles more of the information layer.

The human provides the judgment.

Where I'd Start

You don't need a new department.

You don't need a new executive title.

And you probably don't need another transformation program.

Start with your three biggest projects.

For each one, ask:

If we were deciding today, knowing what we know now, would we still say yes?

Then add one value metric next to your existing delivery metrics.

Just one.

It could be:

Revenue impact
Cost savings
Risk reduction
Customer impact
Benefit realization
Strategic contribution

Let leadership get used to hearing the PMO talk about outcomes, not just dates.

Then expand from there.

The name can come later.

The habit is the real shift.

The Future of the PMO Is About Value

The PMO doesn't need to disappear.

It needs to evolve.

The future belongs to organizations that can connect project execution, portfolio decisions, and business value in one continuous conversation.

That's what makes the VMO idea so compelling.

It isn't about creating another layer of bureaucracy.

It's about asking whether the work deserves the resources being spent on it.

And in an environment where AI can increasingly automate reporting, analysis, and administrative project work, that question becomes even more important.

Because when machines handle more of the reporting, people have more time to decide what is worth doing.

The PMOs people remember won't be the ones that delivered the most. They'll be the ones that knew what was worth delivering.

Alex Rodov

The Twenty-Year Education Behind an AI

Alex Rodov — Thu, 06 Aug 2026 22:22:19 +0000

What it takes to teach a machine the discipline of project management and why experience still matters in the age of AI.

Artificial intelligence is transforming project management faster than anyone imagined.

Reports can be generated in seconds. Risks can be detected before they become major issues. Project data can finally tell a coherent story instead of living in disconnected spreadsheets and dashboards.

But here's the question that matters:

Who teaches AI how project management actually works?

Technology alone doesn't understand portfolios, governance, stakeholder expectations, or executive decision-making.

Those lessons still come from people.

And they often take decades to learn.

Why Experience Matters More Than Ever
The Lessons You Only Learn in the Field
Teaching AI the Discipline of Project Management
From Data to Executive Decisions
Giving Project Managers Their Time Back
The Future of AI in Project Management
Final Thoughts

Why Experience Matters More Than Ever

Many AI platforms begin with algorithms.

Great project management AI begins somewhere else.

It begins with experience.

For over two decades, our team worked inside Project Management Offices (PMOs), enterprise portfolios, capital programs, and digital transformation initiatives.

Those years weren't spent learning software.

They were spent learning how projects actually succeed or fail.

When artificial intelligence reached the point where it could genuinely transform project management, one realization became obvious:

The industry didn't simply need another AI tool.

It needed AI built by people who had already lived the profession.

The Lessons You Only Learn in the Field

Some knowledge can't be found in certifications or textbooks.

It comes from experience.

You learn what a seemingly small schedule delay can do to resource allocation months later.

You learn when a Cost Performance Index (CPI) deserves attention and when it deserves skepticism.

You learn how to connect fragmented information spread across multiple systems, including:

Primavera P6 schedules
Microsoft Project plans
ERP financial systems
Risk registers
Email updates
Resource plans
Executive reports

Perhaps most importantly, you learn something every executive values:

How to explain an entire portfolio in a few clear paragraphs instead of thirty confusing slides.

That kind of judgment only develops after years of delivering complex projects.

Teaching AI the Discipline of Project Management

Building project management AI isn't simply about connecting large language models to project data.

The discipline itself must be taught.

From day one, we made one guiding decision:

The AI would learn project management not just project data.

That meant embedding proven project controls into the system.

Instead of approximating project performance, the platform applies the same methodologies experienced PMOs use every day.

When evaluating project health, AI considers the same types of information project professionals rely on:

Schedule performance
Cost performance
Resource utilization
Portfolio dependencies
Risk exposure
Financial performance
Executive priorities

The goal wasn't to replace professional judgment.

It was to scale it.

From Data to Executive Decisions

Collecting project information has never been the hardest part of project management.

Making sense of it is.

Enterprise portfolios generate enormous amounts of information every week.

Schedules.

Budgets.

Risks.

Status updates.

Forecasts.

The challenge isn't finding the data.

The challenge is turning that data into decisions.

That's where years of project management experience became one of the platform's greatest advantages.

Instead of simply displaying numbers, the system translates project information into meaningful executive insights.

Because leadership doesn't need more dashboards.

Leadership needs clarity.

Giving Project Managers Their Time Back

Project managers often spend an extraordinary amount of time doing work that isn't actually project management.

Every week they:

Collect status updates.
Consolidate spreadsheets.
Reconcile conflicting reports.
Build executive presentations.
Translate technical details into business language.

These activities are necessary.

But they're rarely where project managers create the most value.

Modern AI changes that equation.

Instead of spending Fridays assembling reports, project professionals can spend their time where they make the greatest impact:

Leading teams
Solving complex problems
Building stakeholder confidence
Managing strategic risk
Making better decisions

AI doesn't replace leadership.

It removes the administrative work surrounding it.

The Future of AI in Project Management

The future of project management isn't about replacing experienced professionals.

It's about extending their expertise.

The best AI systems won't simply automate tasks.

They'll capture decades of professional knowledge and make it available whenever project teams need it.

That means organizations can benefit from proven project management practices without losing the human judgment that makes those practices valuable in the first place.

Technology becomes an amplifier not a substitute.

And that's exactly how AI creates lasting value.

Final Thoughts

Artificial intelligence may write reports faster than any human.

It may summarize portfolios in seconds.

It may identify risks long before traditional reporting cycles.

But none of those capabilities appear by accident.

Someone has to teach the machine what matters.

For us, that education took more than twenty years.

It came from enterprise programs.

From PMOs.

From portfolio reviews.

From difficult projects.

From successful ones.

From lessons earned the slow way.

Today, those decades of experience are helping project managers spend less time managing information and more time leading projects.

Because the most valuable part of project management was never the paperwork.

It has always been the judgment behind it.

Continue the Conversation

What project management skill do you believe AI will never fully replace?

Is it stakeholder communication?

Executive judgment?

Leadership?

Or something else entirely?

Share your thoughts in the comments. I'd love to hear your perspective.

If you enjoy reading about AI, PMOs, Microsoft technologies, and the future of enterprise project management, follow for more articles exploring how human expertise and artificial intelligence are shaping the next generation of project delivery.

Stop Looking for the Right Project Management Methodology. There Isn't One.

Alex Rodov — Mon, 27 Jul 2026 22:43:36 +0000

The most successful project managers don't follow methodologies religiously. They build the right approach for the project in front of them.

For years, organizations have debated which project management methodology is best.

Should you use Agile?

Should you stick with Waterfall?

Is Kanban the answer?

Or should you embrace frameworks like SAFe?

The reality is much simpler.

The methodology wars are over, and nobody actually won.

The highest-performing teams have quietly stopped searching for the "perfect" framework. Instead, they're building processes that fit the work rather than forcing the work to fit the process.

That's becoming one of the biggest competitive advantages in modern project management.

Why the Methodology Debate Misses the Point
The Cost of Framework Dogma
The Rise of Tailored Project Management
Why Blending Methodologies Requires More Skill
How AI Is Changing Project Management Forever
How Project Leaders Should Think Today
Final Thoughts

Why the Methodology Debate Misses the Point

One pattern keeps appearing across organizations of every size.

Successful projects rarely succeed because they followed a methodology perfectly.

They succeed because experienced leaders understood which parts of different methodologies actually fit the project.

Project management was never meant to be about following a playbook.

It was always about making good decisions.

Unfortunately, somewhere along the way, many organizations began treating methodologies like belief systems instead of practical tools.

That's where problems begin.

The Cost of Framework Dogma

Frameworks create structure.

Dogma creates rigidity.

There's a difference.

Consider these common situations:

A six-week creative initiative managed with a highly detailed Waterfall schedule.
A heavily regulated infrastructure program forced into two-week Agile sprints.
Teams spending more time following ceremonies than delivering outcomes.

None of these problems are caused by Agile or Waterfall themselves.

They're caused by using the wrong approach for the work.

Every methodology has strengths.

Every methodology has limitations.

The mistake is believing one framework should solve every project.

The Rise of Tailored Project Management

The strongest project managers are no longer choosing one methodology.

They're combining them.

A modern project might use:

Waterfall for regulatory approvals and fixed milestones.
Agile for product development and customer feedback.
Kanban for operational support and continuous delivery.
Lean principles to eliminate unnecessary work throughout the project.

This blended approach isn't an official framework.

It doesn't have certifications.

Yet it's increasingly how real-world projects are managed.

Even the latest project management standards place greater emphasis on tailoring practices to the project, rather than enforcing one universal methodology.

Why Blending Methodologies Requires More Skill

Some people assume combining methodologies is easier.

The opposite is true.

Following a framework requires discipline.

Designing your own approach requires judgment.

Effective tailoring means understanding:

Project complexity
Business risk
Stakeholder expectations
Regulatory requirements
Delivery timelines
Team maturity
Organizational culture
Areas of uncertainty

Only then can a project manager decide which practices deserve a place in the project.

That's not abandoning discipline.

That's mastering it.

The framework becomes the toolbox not the destination.

How AI Is Changing Project Management Forever

For years, organizations avoided tailoring for one practical reason:

It created administrative complexity.

Different project structures meant:

Different dashboards
Different reports
Different workflows
Different governance models
More manual work

Standardization often won because it was easier to manage ot because it delivered better projects.

Artificial intelligence is changing that equation.

Modern AI-powered project management platforms can automatically adapt reporting, summarize progress, generate dashboards, and organize information regardless of how a project is structured.

That dramatically reduces the operational cost of customization.

Instead of forcing projects into predefined templates, technology can now support the way teams actually work.

The process can finally adapt to the project.

Not the other way around.

What Great Project Leaders Should Focus On

Instead of asking:

"Which methodology should we adopt?"

Ask:

"What does this project actually need?"

Every project deserves its own combination of practices.

That means learning multiple approaches well enough to understand when each one creates value.

Great project leaders typically:

Understand Agile beyond the ceremonies.
Respect where Waterfall still delivers outstanding results.
Apply Lean thinking to eliminate waste.
Adapt governance to project complexity.
Focus on outcomes instead of methodology purity.

The methodology becomes a means not the goal.

Best Practices for Modern Project Managers

If you're leading projects today, consider these principles:

Treat methodologies as tools, not rules.
Tailor your approach to project risk and complexity.
Avoid forcing every initiative into the same framework.
Use AI to automate reporting and administrative work.
Spend more time exercising judgment than maintaining templates.
Continuously evaluate whether your process is serving the work or slowing it down.

Flexibility backed by discipline almost always outperforms rigid consistency.

Final Thoughts

Project management has never been about choosing sides.

It's about solving problems.

As AI removes much of the administrative burden from planning, reporting, and governance, project managers have an opportunity to focus on what matters most:

Making better decisions.

Leading people.

Managing uncertainty.

Designing delivery approaches that fit the reality of the work.

The teams that define the next decade won't be remembered because they followed Agile perfectly or mastered Waterfall documentation.

They'll succeed because they understood when to use each and had the confidence to combine them.

The best methodology isn't Agile.

It isn't Waterfall.

It isn't Kanban.

It's the one that helps your team deliver the project successfully.

Continue the Conversation

How does your organization approach project management?

Do you follow a single methodology, or do you blend multiple frameworks based on the project?

Share your experience in the comments. I'd love to hear how your team balances structure with flexibility.

If you enjoy reading about AI, project management, Microsoft technologies, and the future of work, follow for more articles exploring how intelligent tools are reshaping the way organizations deliver projects.

The Machines Took the Busywork. What's Left Is the Real Job.

Alex Rodov — Mon, 13 Jul 2026 22:32:52 +0000

As artificial intelligence automates administrative work, leadership, judgment, and emotional intelligence are becoming the profession's most valuable skills.

projectmanagement #ai #leadership #futureofwork #productivity

For years, project management carried an odd contradiction.

The job description promised leadership.

The daily work looked more like administration.

Project managers spent countless hours:

Updating project plans
Chasing status updates
Formatting executive presentations
Reconciling spreadsheets
Producing reports

In one of the most people-centered professions, much of the work involved tasks that were anything but human.

That contradiction is finally disappearing.

Artificial intelligence is rapidly taking over the administrative side of project management.

The more interesting question isn't whether AI will automate these tasks.

It's what happens to the profession afterward.

AI Isn't Replacing Project Managers. It's Changing Their Job.

One of the biggest misconceptions surrounding AI is that project managers will become obsolete.

The evidence suggests something very different.

Project management isn't disappearing.

It's evolving.

Industry analysts increasingly agree that AI is removing administrative overhead while elevating the strategic responsibilities of project managers.

Routine work becomes automated.

Leadership becomes more valuable.

Instead of spending hours updating schedules, tomorrow's project managers will spend more time:

Managing stakeholders
Navigating uncertainty
Solving complex business problems
Guiding cross-functional teams
Anticipating and mitigating risks

In other words, the profession is moving higher up the value chain.

The Work Being Automated Was Never the Reason People Chose This Career

Few professionals entered project management because they loved updating spreadsheets.

The administrative workload simply became part of the job.

Now AI can generate reports, summarize meetings, track action items, update schedules, and surface project insights in seconds.

What's left behind is the work that has always mattered most.

The conversations.

The decisions.

The leadership.

The human side of delivery.

Human Skills Are Becoming the New Competitive Advantage

Economics offers a useful principle:

When something becomes scarce while demand increases, its value rises.

That's exactly what's happening with human leadership.

As AI automates routine execution, organizations increasingly depend on capabilities that machines struggle to replicate:

Emotional intelligence
Critical thinking
Strategic judgment
Negotiation
Influence
Trust-building
Conflict resolution

These have often been called "soft skills."

There is nothing soft about them anymore.

They are becoming the hardest skills to replace.

Leadership Has Always Been Different From Management

One phrase perfectly captures this transition:

You can manage things.

But you have to lead people.

Management has always consisted largely of processes.

Processes can be automated.

Leadership cannot.

A dashboard may identify an at-risk milestone.

It cannot calm an anxious executive.

An AI model may recommend reallocating resources.

It cannot rebuild trust after a difficult stakeholder meeting.

Technology excels at information.

Leadership excels at people.

The future requires both.

AI Literacy Is Becoming Essential

None of this means project managers can ignore AI.

Quite the opposite.

The most successful professionals will be those who know how to collaborate with intelligent systems.

That means learning how to:

Ask better questions
Evaluate AI-generated recommendations
Challenge incorrect outputs
Apply business context
Make final decisions responsibly

AI becomes a partner.

Not the decision-maker.

The technology proposes.

The project manager decides.

That may become one of the defining responsibilities of modern leadership.

The Career Ladder Is Changing

There is one important challenge that organizations cannot ignore.

For decades, many project managers learned the profession through administrative work.

Junior roles focused on:

Reporting
Documentation
Schedule maintenance
Status tracking
Coordination

Those responsibilities are increasingly being automated.

This creates an important question:

How will future project managers gain experience if the traditional apprenticeship disappears?

Organizations will need new ways to develop talent through coaching, mentoring, rotational assignments, and greater exposure to strategic work earlier in a career.

The pathway may change.

The profession's purpose does not.

Project Management Is Moving Up the Value Chain

The demand for project managers isn't shrinking.

It's shifting.

Organizations still need professionals who can:

Navigate ambiguity
Align competing priorities
Build consensus
Lead transformation
Make decisions under uncertainty

Those responsibilities become even more important as AI accelerates execution.

The faster technology moves, the more valuable human judgment becomes.

Final Thoughts

For years, administrative work acted like a tax on project management.

It consumed time without creating proportional value.

Artificial intelligence is finally lifting much of that burden.

What's left isn't a smaller profession.

It's a better one.

Project managers are being handed back the work they were always meant to do:

Leading people.

Making decisions.

Creating alignment.

Building trust.

Because the paperwork was never the profession.

It was simply the price of doing it.

Why your project team isn’t the best option for identifying project risks

Alex Rodov — Mon, 22 Jun 2026 21:06:23 +0000

Ask a project team to identify the biggest risks threatening their project.

Most will confidently produce a list.

The problem is that the most dangerous risks are often missing.

Not because the team is incompetent.

Not because they're hiding anything.

But because they're too close to the work.

After months of planning, building, coordinating, and solving problems, project teams develop something every human being develops when deeply invested in an outcome:

Blind spots.

Ironically, the people most qualified to execute a project are often the least qualified to objectively assess its risks.

That's why some organizations are beginning to experiment with a different approach.

A concept known as Rotative Risk Assessment.

The Problem With Traditional Risk Reviews

Most risk management processes share the same assumption:

The team building the project is also the best team to identify what might go wrong.

At first glance, that seems reasonable.

Who understands the project better than the people doing the work?

But familiarity comes with a cost.

The deeper teams become immersed in a project, the harder it becomes to challenge their own assumptions.

Several cognitive biases begin to appear:

Optimism bias
Confirmation bias
Familiarity bias
Escalation of commitment

None of these require bad intentions.

They're simply human nature.

Teams naturally focus on making the project succeed.

As a result, they often overlook the very threats that could derail it.

What Is Rotative Risk Assessment?

Rotative Risk Assessment flips the traditional model.

Instead of asking a project team to evaluate its own risks, an independent peer team performs the review.

Then, on a future project, the roles reverse.

The concept is surprisingly simple:

Team A reviews Team B's project
Team B reviews Team C's project
Team C reviews Team A's project

Every team participates.

Every team benefits.

And every project receives a fresh set of eyes.

Why Fresh Eyes Matter

One of the most powerful advantages of external review is objectivity.

Independent reviewers aren't emotionally attached to:

Deadlines
Budgets
Delivery commitments
Success metrics

They're free to ask uncomfortable questions.

Questions like:

What assumption is everyone taking for granted?
What dependency could fail?
What happens if this milestone slips?
Is the schedule realistic?
Are the resources sufficient?

Because they aren't immersed in daily execution, reviewers can often spot vulnerabilities that project teams stopped noticing months ago.

What Gets Reviewed?

A structured Rotative Risk Assessment typically examines:

Project Scope

Are requirements clearly defined?

Have assumptions been validated?

Is there hidden complexity?

Schedule

Are timelines realistic?

Have critical dependencies been identified?

Are contingency plans in place?

Resources

Does the project have the right people and capacity?

Are key contributors overloaded?

Dependencies

What external teams, vendors, or systems could create delays?

How resilient is the delivery plan?

Strategic Alignment

Does the project still support business priorities?

Have conditions changed since planning began?

Ownership Never Changes

One common concern is that external reviews create confusion around accountability.

In reality, the opposite is true.

Rotative Risk Assessment only transfers the responsibility for identifying risks.

It does not transfer responsibility for resolving them.

The original project team remains fully accountable for:

Decision-making
Mitigation plans
Delivery outcomes
Project execution

The review simply provides better visibility.

Leadership gains greater transparency while ownership remains intact.

Why Organizations Are Seeing Better Results

Companies adopting peer-based risk reviews frequently report several benefits.

Better Risk Identification

Independent reviewers challenge assumptions that project teams may no longer question.

Stronger Planning Discipline

Teams know their work will be reviewed by peers, encouraging more rigorous preparation.

Increased Accountability

Reviews create healthy pressure without introducing bureaucracy.

Cross-Organizational Learning

Perhaps the biggest benefit is knowledge transfer.

As teams rotate through different projects, they gain exposure to:

Different delivery approaches
Common failure patterns
New governance techniques
Risk management strategies

The organization becomes smarter with every review cycle.

Where Rotative Risk Assessment Works Best

Not every initiative requires this level of scrutiny.

The approach delivers the greatest value when project failure carries significant consequences.

Examples include:

High-visibility executive initiatives
Large capital investments
Enterprise transformation programs
Regulatory projects
Fixed-budget engagements
Time-sensitive implementations

The higher the stakes, the more valuable independent risk assessment becomes.

A New Way to Think About Project Governance

For decades, project governance has relied on a questionable assumption:

The builders of a project are also its best judges.

Rotative Risk Assessment challenges that idea.

It recognizes something fundamentally human:

People closest to a problem are often least able to see it objectively.

Sometimes the smartest risk management strategy isn't hiring more consultants or building more dashboards.

Sometimes it's simply inviting another team to take a look.

Because letting someone else identify the risks in your project doesn't weaken ownership.

It strengthens the outcome.

Stop Studying Prompts Start Using Them

Alex Rodov — Fri, 12 Jun 2026 00:53:33 +0000

Why the best AI users aren't taking prompt engineering courses they're learning by doing.

There has never been a better time to sell a course on prompting.

And there has never been a worse time to take one.

Both things are true at the same time.

Spend ten minutes on LinkedIn and you'll find countless AI experts promising the same outcome:

Learn the secret framework.
Master the right prompt formula.
Unlock the hidden language of AI.

The underlying message is simple:

If you learn how to talk to AI, you'll gain a lasting competitive advantage.

The problem?

The data increasingly suggests the opposite.

The people getting the most value from AI aren't spending months studying prompts.

They're spending minutes using them.

The Best AI Learners Barely "Study"

When you look at project managers, analysts, marketers, and leaders who have become highly effective with AI, a pattern emerges.

Most didn't complete a prompt engineering certification.

Most didn't memorize frameworks.

Instead, they:

Opened the tool
Asked a question
Reviewed the answer
Improved the prompt
Repeated the process

Their first prompt was mediocre.

Their tenth was better.

Their hundredth was excellent.

They learned through iteration, not observation.

The same way people learn leadership, project management, negotiation, or public speaking.

By doing.

The Problem With Learning AI Like Traditional Software

Traditional training works well when the subject matter is relatively stable.

AI isn't stable.

The landscape changes every few months.

Models improve.
Capabilities expand.
Interfaces evolve.

By the time many AI courses are recorded, edited, marketed, and sold, parts of the curriculum are already outdated.

What was considered an advanced prompting technique a year ago is often built directly into today's models.

The ground is moving faster than most syllabi can keep up with.

Prompt Engineering Is Already Changing

For a brief moment, prompt engineering looked like a permanent skill.

In reality, it may be more of a transitional one.

Modern AI systems increasingly:

Infer intent
Clarify ambiguity
Structure responses automatically
Ask follow-up questions
Generate prompts for users

Many of the techniques that once differentiated power users are being absorbed into the models themselves.

The better the AI becomes, the less it requires humans to micromanage the conversation.

That doesn't mean expertise disappears.

It means the expertise shifts.

What Actually Compounds Over Time

If prompt formulas are becoming automated, what remains valuable?

Judgment.

The ability to:

Ask better questions
Recognize weak answers
Challenge assumptions
Apply domain expertise
Know when the AI is wrong

Those skills don't expire with the next model release.

They transfer across every platform, every tool, and every update.

Unlike prompt templates, they appreciate over time.

AI Can Help You Write Better Prompts

Here's another reality most courses rarely discuss:

Many times, AI can write the prompt for you.

Instead of spending hours learning advanced frameworks, you can simply ask:

"Help me create the best prompt to solve this problem."

The model itself often becomes the teacher.

The value no longer comes from memorizing syntax.

It comes from understanding the problem you're trying to solve.

What This Means for Project Managers

For project managers, the takeaway is surprisingly simple.

Don't wait until you've completed the perfect course.

Don't spend months studying theory.

Start using AI on real work.

Use it to:

Draft status reports
Summarize meetings
Analyze project risks
Create stakeholder communications
Build project plans
Generate executive summaries

Every interaction builds intuition.

Every iteration improves judgment.

And every real-world use case teaches more than another hour of passive learning.

The Learning Curve Is the Work

One of the biggest misconceptions about AI is that learning happens before implementation.

In reality, implementation is the learning process.

A project manager who spends twenty minutes a day using AI on active projects will often outperform someone who spent forty hours consuming AI training content.

Because context matters.

Real problems create real learning.

And real learning sticks.

The Real Competitive Advantage

The future won't belong to people who know the most prompt frameworks.

It will belong to people who know how to think.

People who combine:

Domain expertise
Critical thinking
Business context
Human judgment
AI capabilities

That's the combination that scales.

Not prompt memorization.

Not certifications.

Not secret formulas.

Final Thoughts

The AI experts will keep selling maps.

The fastest learners will keep walking.

Every day you spend solving real problems with AI builds a skill that transfers across every future model and platform.

Because the best prompt you'll ever write isn't hidden inside a course.

It's the next one.

A little better than the last.

On a problem you actually care about solving.

AI Is Changing Work Faster Than Most Organizations Are Changing Culture

Alex Rodov — Fri, 29 May 2026 22:19:45 +0000

Every week brings new tools, smarter systems, and faster ways to execute.

AI can now summarize meetings, generate reports, automate workflows, and support decision-making at scale.

But even with all that acceleration, one thing hasn’t changed:

People still want to feel useful.

They want to feel heard.

They want to feel connected to meaningful work.

And that’s where reverse mentoring becomes incredibly relevant.

Reverse Mentoring Is Bigger Than Knowledge Transfer

Traditionally, mentorship moved in one direction.

Experience flowed from senior leaders to younger employees.

Reverse mentoring changes that dynamic.

It creates space for younger generations, emerging professionals, and digitally native employees to share perspectives that leadership teams may otherwise miss.

Not just about technology.

Communication
Expectations
Workplace culture
Adaptability
Human behavior
The future of collaboration

The best organizations aren’t just teaching anymore.

They’re listening.

Technology and People Move Differently

One of the biggest realizations from this conversation was simple:

Technology evolves exponentially.
People don’t.

Humans process change emotionally, socially, and psychologically.

Organizations often try to close that gap as quickly as possible.

But maybe the gap itself isn’t the problem.

Maybe it’s the reminder.

A reminder to slow down long enough to actually understand:

What people need
What motivates them
What creates engagement
What builds trust inside teams

Because no matter how advanced technology becomes, disconnected cultures still fail.

The Future of Work Still Depends on Human Skills

AI will continue transforming productivity.

That part is inevitable.

But leadership, empathy, trust, judgment, emotional intelligence, and meaningful collaboration are becoming more valuable, not less.

Ironically, the more automated work becomes, the more human connection stands out.

The companies that thrive won’t simply adopt the best tools.

They’ll build environments where people still feel:

Seen
Valued
Included
Heard
Connected to purpose

That’s the real competitive advantage.
**
Final Thoughts**

Grateful for this conversation with Linda Dulye and the perspective she shared.

In a world obsessed with speed, automation, and optimization, conversations like these are a reminder that people still matter most.

If you watch the discussion, I’d genuinely love to hear your thoughts.

🎥 https://youtu.be/BY2NhYrquz4?si=7VYba7tqoqiRS7N7

The Idea Factory Has No Off Switch: Why Human Judgment Matters More in the AI Era

Alex Rodov — Thu, 21 May 2026 22:15:36 +0000

AI has removed the speed limit on creativity. Now the real differentiator is judgment.

A few years ago, creativity had a natural speed limit.

Ideas needed time.

You protected quiet mornings, whiteboards, notebooks, long walks, and late-night thinking sessions because that’s where breakthroughs happened. Producing meaningful work required friction, patience, and uncertainty.

Then AI arrived.

And suddenly, the bottleneck disappeared.

Now you can:

Generate hundreds of ideas before lunch
Draft business plans between meetings
Create visuals, reports, code, and strategies in minutes
Prototype in hours what once took entire quarters

For the first time in history, creative output can move almost at the speed of thought.

And that changes everything.

The Real AI Revolution Isn’t Speed It’s Abundance

Most conversations about AI focus on automation.

But the deeper shift is abundance.

Ideas are no longer scarce.

Execution is no longer the primary barrier.

Content generation is becoming effectively infinite.

That sounds empowering and it is but it also creates a new challenge:

If everyone can generate endlessly, what actually becomes valuable?

Ironically, the answer isn’t the machine.

It’s the human using it.

The New Scarcity Is Judgment

We are entering a world where almost anyone can create.

The differentiator is no longer access to tools.

It’s:

Judgment
Taste
Clarity
Originality
Decision-making
The courage to commit

AI can generate one hundred directions.

But someone still has to decide:

Which one actually matters?

That responsibility didn’t disappear.

It became more important.

When Infinite Possibility Becomes Noise

There’s a fascinating paradox at the center of the AI era:

The easier it becomes to create, the harder it becomes to matter.

Because abundance creates noise.

And noise creates fatigue.

The modern challenge is no longer:
“How do I produce more?”

It’s:
“How do I create something meaningful in a world flooded with output?”

This is where human judgment becomes the real competitive advantage.

The Most Valuable Skill Is Knowing When to Stop

AI rewards speed.

But meaningful work still requires restraint.

The people who thrive in this environment won’t necessarily be the ones generating the most.

They’ll be the ones who know:

Which ideas deserve attention
Which projects are worth pursuing
Which signals matter
Which opportunities to ignore

In other words:

Taste becomes strategy.

And focus becomes power.

Creativity Isn’t Dead It’s Evolving

Some fear AI will replace creativity.

The reality is more nuanced.

AI expands creative possibility.

But it also raises the standard for what stands out.

Machines can remix patterns endlessly.

Humans still provide:

Perspective
Emotion
Context
Intuition
Meaning
Vision

AI can accelerate creation.

But humans still define purpose.

The Rise of the Curator-Leader

In the past, leaders were primarily builders and operators.

Now, they increasingly become curators of possibility.

Their role is no longer just to produce.

It’s to:

Filter intelligently
Choose intentionally
Prioritize relentlessly
Guide creative direction

The future belongs to people who can navigate abundance without drowning in it.

AI Doesn’t Remove Responsibility

One of the biggest misconceptions about AI is that it somehow removes human accountability.

It doesn’t.

If anything, it amplifies it.

Because when tools become infinitely capable, human decisions carry even greater weight.

The machine can offer endless outputs.

But humans still decide:

What gets published
What gets built
What gets funded
What gets believed
What deserves attention

That responsibility cannot be outsourced.

Why This Changes Business, Leadership, and Innovation

This shift affects more than creators.

It changes how companies operate.

The organizations that succeed in the AI era won’t simply adopt the most tools.

They’ll build cultures capable of:

Better decision-making
Faster prioritization
Higher creative standards
Clearer strategic thinking

The winners won’t necessarily create more.

They’ll create more meaningfully.

The New Creative Workflow

The workflow of the future looks different.

AI handles:

Generation
Drafting
Iteration
Automation
Exploration

Humans handle:

Direction
Judgment
Emotional intelligence
Strategic choices
Final accountability

This is not human versus machine.

It’s human amplified by machine.

The Real Risk Isn’t AI It’s Thoughtlessness

The danger of AI isn’t just misinformation or automation.

It’s the temptation to stop thinking deeply.

When creation becomes instant, intentionality becomes rare.

And rarity creates value.

The future belongs to people who remain thoughtful in an age optimized for speed.

Final Thoughts

The AI gold rush is real.

But the gold isn’t the technology itself.

It’s the human capacity to:

Choose wisely
Create intentionally
Finish what matters
Bring meaning into a world overflowing with output

Because in the end:

The people who win won’t be the ones generating the most ideas.

They’ll be the ones who know when to stop.

Project Leaders, AI, and the Courage to Be Human

Alex Rodov — Thu, 30 Apr 2026 19:53:24 +0000

ai #projectmanagement #leadership #productivity #futureofwork

Artificial intelligence is transforming how projects are managed.

But here’s the uncomfortable truth:

The biggest challenge in project management today isn’t technology.

It’s how humans lead under pressure.

In the age of AI, the most valuable skill might not be technical at all.

It might be the courage to be human.

The Hidden Pressure Project Managers Carry

Many project managers operate under a silent expectation:

“I should have everything under control.”

That belief creates a dangerous pattern:

Avoiding asking for help
Hiding uncertainty
Delaying difficult conversations
Pretending everything is fine

This doesn’t make projects stronger.

It makes them fragile.

Because when issues stay hidden, they don’t disappear.

They compound.

When Project Managers Feel Isolated

Project leaders often sit in a unique position:

Responsible for outcomes
Expected to lead confidently
Surrounded by uncertainty

And yet, many feel like they’re leading alone.

In some organizations, saying:

“I don’t know”

“We’re stuck”

feels risky.

But ironically:

Speaking up early is often the most responsible leadership move.

It creates space to:

Adjust scope
Get support
Rethink strategy

Silence, on the other hand, leads to late-stage chaos.

The Cost of Replacing Instead of Supporting

When projects struggle, some companies default to:

“Let’s replace the project manager.”

It looks decisive.

But it’s expensive.

Replacing a PM often means:

Losing institutional knowledge
Breaking team trust
Resetting momentum
Reinforcing a fear-based culture

A better alternative?

Invest in the leader, not just the outcome.

Coaching, support, and development turn challenges into growth opportunities instead of setbacks.

Mindset Is the Real Leadership Lever

At the core of effective project leadership is mindset.

Limiting beliefs sound like:

“If I ask for help, I’ll look weak.”
“I need to have all the answers.”
“Uncertainty means failure.”

High-performing leaders shift those beliefs:

Asking for help = responsible leadership
Learning = part of the job
Collaboration = strength, not weakness

When mindset changes, behavior follows:

More transparency
Better communication
Stronger team alignment

And most importantly:

Leadership becomes shared, not isolated.

From “Hero PM” to Team Ownership

Traditional project management often revolves around a single “hero PM.”

But that model doesn’t scale.

Modern leadership looks different:

Teams share context
Decisions are collaborative
Ownership is distributed

Instead of one person holding everything together, the entire team contributes to success.

That’s not just more efficient.

It’s more resilient.

Tools Don’t Fix Broken Systems

Let’s be real.

Most organizations are already using tools like:

Jira
Azure DevOps
ClickUp
Excel

But switching tools rarely solves deeper issues.

Because:

The problem is rarely the software.

The problem is how people work together.

New tools often promise:

Better productivity
More alignment
Improved visibility

But without strong communication and leadership:

Priorities stay unclear
Accountability gaps remain
Friction continues

Technology supports systems.

It doesn’t replace them.

AI Is Giving Time Back Now What?

AI is already transforming project management by automating:

Status reports
Meeting summaries
Communication drafts
Scheduling
Data analysis

This creates a new opportunity:

What do you do with the time AI gives back?

Two paths emerge:

Option 1: Do more busy work
More reports. More meetings. More tasks.

Option 2: Lead better

Have meaningful stakeholder conversations
Think strategically
Strengthen team alignment
Anticipate risks earlier

The difference is huge.

AI can increase output.

But only humans can increase impact.

The Real Role of a Project Leader in the AI Era

As AI handles more technical tasks, the role of project managers is evolving.

From:

Task tracking
Status reporting
Coordination

To:

Decision-making
Alignment
Communication
Leadership

AI doesn’t replace project managers.

It redefines their value.

Choosing Human Leadership in a Digital World

Even as technology advances, one thing remains constant:

People trust people.

AI can:

Analyze data
Suggest actions
Automate workflows

But it cannot:

Build trust
Navigate emotions
Take responsibility
Lead under uncertainty

Those are human skills.

And they’re becoming more valuable, not less.

What This Means for the Future of Project Management

The future isn’t:

AI vs Humans

It’s:

AI + Human Leadership

The best project leaders will:

Use AI to remove friction
Use time to focus on people
Lead with clarity and honesty
Build stronger, more collaborative teams

Key Takeaways

Project managers often hide uncertainty and that hurts outcomes
Replacing leaders is more expensive than developing them
Mindset is the foundation of effective leadership
Tools don’t fix broken communication or culture
AI frees time but how you use it defines your impact
Human leadership is becoming the true competitive advantage

Final Thought

Technology is evolving fast.

But leadership?

That’s still human.

The future of project management won’t be defined by better tools.

It will be defined by better leaders.

Global Innovation, Local Impact: How Trusted IT Group Connects Sarasota to the Future of AI

Alex Rodov — Fri, 24 Apr 2026 22:23:13 +0000

In today’s distributed economy, innovation is no longer confined to Silicon Valley or traditional tech hubs.

But here’s the twist:

While geography doesn’t limit innovation anymore, it still matters where leaders choose to build.

And increasingly, Sarasota, Florida is becoming part of that conversation.

The Rise of Distributed Innovation

Modern companies are no longer built around a single headquarters.

They operate across:

Multiple countries
Distributed teams
Global talent networks
Cloud-based infrastructure

This shift has created a new type of organization:

Global in execution. Local in leadership.

With operations in the United States and Europe, including a headquarters in New Jersey and a European presence in Portugal, TIG combines international scalability with localized leadership.

And now, Sarasota is entering that equation.

Why Sarasota? A Strategic Move, Not a Lifestyle Choice

The expansion of executive leadership into Sarasota is not just about remote work or relocation.

It reflects a broader trend:

High-level decision-making is becoming geographically flexible.

Cities like Sarasota are gaining attention because they offer:

Strong talent ecosystems
High quality of life
Strategic positioning for growth
Increasing relevance in global business networks

By establishing leadership presence in Sarasota, TIG is effectively:

Connecting global operations to local ecosystems
Bringing innovation closer to emerging markets
Reinforcing the city’s role in the digital economy

This is how new tech hubs are formed, not through hype, but through where leaders choose to operate.

Building the Future of Project Management with AI

One of TIG’s most notable innovations is FridayReport.ai, an AI-native platform designed to redefine how organizations manage projects, portfolios, and resources.

Unlike traditional tools, it doesn’t just track work.

It understands and adapts to it.

Key capabilities include:

Real-time reporting and executive summaries
Predictive insights for project outcomes
Resource optimization across portfolios
Automated alignment between strategy and execution

This positions TIG at the intersection of two major trends:

The evolution of Project Management Offices (PMOs)
The integration of AI into operational decision-making

In other words:

Project management is no longer just about tracking tasks.

It’s about intelligent orchestration.

From Headquarters to Networks

We’re witnessing a structural shift in how companies operate.

The old model:

Centralized headquarters
Fixed geographic identity
Localized teams

The new model:

Distributed execution
Cross-border collaboration
Network-driven operations

Companies are no longer defined by a single location.

They are defined by:

Talent
Infrastructure
Decision-making systems

As Alex Rodov, Partner in Innovation & AI, explains:

“We’re no longer limited by what technology can do. We’re limited by how well leaders understand what to ask from it.”

That insight changes everything.

Why Talent Beats Location

The competitive advantage today is not access to tools.

Everyone has access.

The real differentiator is:

The quality of people
The clarity of thinking
The speed of execution

That’s why companies like TIG are becoming talent-first organizations.

Instead of asking:

Where should we build?

They’re asking:

Where can we find the right people and create the most impact?

Expanding into regions like Sarasota is part of that strategy.

A New Definition of Corporate Presence

The traditional idea of a corporate footprint is evolving.

Today’s leading companies are:

Global in execution → Delivering across markets
Distributed in operations → Working across time zones
Strategic in leadership placement → Positioning decision-makers where they create the most value

This model creates:

Greater flexibility
Faster decision-making
Stronger resilience

And most importantly:

It allows companies to scale without being constrained by geography.

What This Means for the Future of Business

The expansion of companies like TIG into cities like Sarasota is not an isolated case.

It’s part of a larger shift:

Innovation is becoming decentralized
Leadership is becoming mobile
Value is created through networks, not locations

This changes how we think about:

Economic growth
Talent distribution
Regional competitiveness

Cities that were once considered “non-tech hubs” are now becoming strategic nodes in global systems.

Final Thoughts

The future of business is not about choosing between global and local.

It’s about combining both.

Companies that succeed will be those that:

Build globally
Operate flexibly
Lead strategically

Trusted IT Group’s expansion into Sarasota reflects exactly that.

Not just growth.

But a new way of building companies.

TL;DR

Innovation is no longer tied to geography
Leadership location still matters
Sarasota is emerging as a strategic tech hub
AI is reshaping project management and operations
The future belongs to network-driven, talent-first companies

If you’re still thinking in terms of headquarters and locations…

You might be missing the bigger picture.

AI Is Everywhere, But What Does That Actually Mean for Business?

Alex Rodov — Thu, 02 Apr 2026 19:51:02 +0000

Artificial intelligence is no longer a future concept.

It’s a present reality.

But here’s the real question:

If everyone is talking about AI… why aren’t more companies seeing real results?

Let’s break down what’s actually happening behind the hype and what it means for your business strategy.

AI Has Taken Over the Language of Business

A few years ago, AI was barely mentioned in executive conversations.

Then everything changed.

Fewer than 50 S&P 500 earnings calls mentioned AI per quarter before 2022
By early 2024, that number jumped to 199 companies per quarter
Over 40% of S&P 500 companies consistently referenced AI through 2025

AI didn’t just evolve as a technology.

It became part of how companies signal relevance and innovation.

Even more interesting:

Companies mentioning AI saw ~13.9% stock growth
Those that didn’t? Around 5.7%

Translation:

Talking about AI became almost as important as using it.

Adoption Is Exploding, But Impact Isn’t

Here’s where things get interesting.

AI usage is growing fast:

88% of organizations use AI in at least one function
2/3 use it across multiple areas
61% of people have used generative AI
Weekly usage nearly doubled in one year

But impact?

Only ~33% of companies are scaling AI
Just 6% are true “high performers”

That means:

Most companies are experimenting… not transforming.

This gap has a name: the adoption gap.

And it’s where most businesses get stuck.

The “Workslop” Problem (Yes, It’s Real)

With AI everywhere, output is exploding.

But quality? Not so much.

~40% of professionals report seeing low-quality AI-generated content (“workslop”)
~54% of LinkedIn content is AI-generated or AI-assisted

We’ve entered a phase where:

More content ≠ better outcomes

AI makes it easy to produce.

It doesn’t guarantee value.

The Real Problem: Workflow, Not Technology

Here’s the biggest insight:

The companies winning with AI aren’t using better tools.

They’re using AI differently.

High-performing organizations:

Are 3x more likely to aim for transformation (not just efficiency)
Redesign workflows around AI (55% vs ~20% of others)

This is the key difference:

Most companies:

Add AI on top of existing processes

High performers:

Rebuild processes around AI

Example:

Adding a chatbot = incremental improvement
Redesigning customer support with AI = transformation

Same tech. Completely different outcome.

A Lesson from History (That Still Applies)

This isn’t new.

When electricity was introduced:

Factories that replaced steam engines → small gains
Factories that redesigned everything → massive productivity boost

AI is following the same pattern.

Technology enables transformation.

It does not create it automatically.

What Smart Companies Are Doing Right Now

The companies actually winning with AI are doing a few things differently:

1. Focus on High-Impact Use Cases

Not 50 experiments. Just 2–3 workflows where results are measurable.

2. Redesign Workflows

They don’t “add AI.” They rethink how work gets done.

3. Build Internal Capability

They train teams instead of outsourcing thinking.

4. Treat AI as Infrastructure

Not a trend. Not a feature. A foundation.

Why Most Companies Are Falling Behind

Here’s the uncomfortable truth:

People are using AI
Organizations don’t know how to capture value

Many companies are stuck in:

Tool experimentation
Pilot projects
Executive buzzwords

But not:

Real implementation
Measurable outcomes
Scalable systems

That gap is where competitive advantage is being created.

The Real Opportunity (And It’s Time-Sensitive)

The barrier to entry for AI has never been lower.

Everyone has access to:

The same tools
The same models
The same capabilities

So what separates leaders?

Execution. Learning speed. Organizational commitment.

The companies pulling ahead are:

Training their teams
Running real experiments
Iterating quickly
Measuring what works

And most importantly:

They are moving now.

The Window Is Open, But Not Forever

Here’s the reality:

Today: AI tools are widely accessible
Tomorrow: Execution gaps become structural advantages

Companies that delay will face:

Measurable gaps within 12 months
Structural gaps within 2–3 years

Because AI adoption compounds.

Every quarter of real implementation creates distance.

Final Thoughts: AI Is the Foundation, Not the Trend

AI is no longer the trend.

It’s the baseline.

The real trend is:

What companies are building on top of AI.

The winners won’t be the ones talking about AI the most.

They’ll be the ones:

Embedding it into workflows
Training their people
Measuring real outcomes
Scaling what works

And doing it faster than everyone else.

TL;DR

AI is everywhere, but real impact is rare
Most companies are experimenting, not transforming
The biggest gap is workflow redesign
High performers treat AI as infrastructure
The competitive window is open… but closing fast

If you’re still asking “Should we use AI?”

You’re already asking the wrong question.

The real question is:

Where should we rebuild how work gets done?

DEV Community: Alex Rodov

I don't know what software will look like in five years. I have a guess about who we'll still trust.

Nobody Knows What's Coming

The Real AI Challenge Isn't Capability. It's Trust.

The Human Skills That Become More Valuable

The Enterprise Software Model Is Changing

A Guess About the Next Five Years

Technology Can Scale. Trust Has to Be Earned.

What I Would Bet On

The Part I Don't Want to Predict

We delivered every project on time. So why did the room go quiet?

A Simple Idea Called the VMO

From Project Delivery to Business Outcomes

This Shift Is Already Happening

Why the VMO Matters Now

The AI Advantage: Less Reporting, More Judgment

Where I'd Start

The Future of the PMO Is About Value

The Twenty-Year Education Behind an AI

Table of Contents

Why Experience Matters More Than Ever

The Lessons You Only Learn in the Field

Teaching AI the Discipline of Project Management

From Data to Executive Decisions

Giving Project Managers Their Time Back

The Future of AI in Project Management

Final Thoughts

Continue the Conversation

Stop Looking for the Right Project Management Methodology. There Isn't One.

Table of Contents

Why the Methodology Debate Misses the Point

The Cost of Framework Dogma

The Rise of Tailored Project Management

Why Blending Methodologies Requires More Skill

How AI Is Changing Project Management Forever

What Great Project Leaders Should Focus On

Best Practices for Modern Project Managers

Final Thoughts

Continue the Conversation

The Machines Took the Busywork. What's Left Is the Real Job.

projectmanagement #ai #leadership #futureofwork #productivity

AI Isn't Replacing Project Managers. It's Changing Their Job.

The Work Being Automated Was Never the Reason People Chose This Career

Human Skills Are Becoming the New Competitive Advantage

Leadership Has Always Been Different From Management

AI Literacy Is Becoming Essential

The Career Ladder Is Changing

Project Management Is Moving Up the Value Chain

Final Thoughts

Why your project team isn’t the best option for identifying project risks

The Problem With Traditional Risk Reviews

What Is Rotative Risk Assessment?

Why Fresh Eyes Matter

What Gets Reviewed?

Project Scope

Schedule

Resources

Dependencies

Strategic Alignment

Ownership Never Changes

Why Organizations Are Seeing Better Results

Better Risk Identification

Stronger Planning Discipline

Increased Accountability

Cross-Organizational Learning

Where Rotative Risk Assessment Works Best

A New Way to Think About Project Governance

Stop Studying Prompts Start Using Them

Why the best AI users aren't taking prompt engineering courses they're learning by doing.

The Best AI Learners Barely "Study"

The Problem With Learning AI Like Traditional Software

Prompt Engineering Is Already Changing

What Actually Compounds Over Time

AI Can Help You Write Better Prompts

What This Means for Project Managers

The Learning Curve Is the Work

The Real Competitive Advantage

Final Thoughts

AI Is Changing Work Faster Than Most Organizations Are Changing Culture

The Idea Factory Has No Off Switch: Why Human Judgment Matters More in the AI Era