I've been in technology long enough to see all kinds of transformations, including the .com era, 2008 financial crisis and the cloud. Organizations I’ve led have navigated the shift to remote work, and every other next big thing. But none of it compares to AI. The change over the past few years is undeniably dramatic. The conversations started with “We’re exploring AI.” Now, it’s “We need to restructure around AI.”
But this isn’t just talk. The numbers back it up. In the U.S. alone, AI is driving massive change in the way organizations are structured. To accommodate this, more than 50,000 layoffs occurred recently. Dozens of CEOs went on record tying their workforce reductions directly to AI. This trend of AI-related restructuring affects all industries and job types.
As technology company leaders, we’re especially familiar with this. Amazon eliminated 16,000 jobs with a single email. Microsoft cut over 15,000 positions. Block's CEO cut his company nearly in half. In that case, the CEO was clear about why: AI has changed what it means to build and run a company.
And this isn’t just limited to the United States. In Europe, Ford is eliminating 4,000 jobs by 2027. Volkswagen Group eliminated over 50,000 positions too. This impact is real, and the investment in AI is massive. But as companies bet big on AI, the returns are still catching up.
The state of AI in business
The capital flowing into AI is unlike anything we’ve seen before. Gartner projects global AI spending reached $1.5 trillion in 2025. Forecasts show it topping $2 trillion by 2026. Big Tech capital expenditures also more than doubled in the last two years, reaching an estimated $427 billion.
AI is the biggest bet we’re making, but the results haven't kept pace. We’re spending aggressively and spinning up pilots across teams, departments, regions and more. But then they’re hitting a wall when the results don't materialize as fast as expected. Yes, AI can speed up growth, but only if organizations are truly prepared.
According to Deloitte's latest State of AI in the Enterprise, two-thirds of organizations report productivity gains from AI, which is encouraging. But only 34% are truly reimagining their business around it. And while 74% of organizations hope to drive revenue growth through AI, only 20% are actually doing it today.
I realize it’s easy to get excited about all the possibilities with AI. But it’s harder to create the infrastructure to get results that lead to growth. If companies want to succeed with a transformation like this, we have to look to the not-so-distant past.
We saw the same pattern with cloud computing. Companies that invested in strong foundations early, focusing on clean infrastructure and reliable data, were the ones that eventually pulled ahead. The rest spent years playing catch-up. AI is following the same trajectory, only faster.
Understanding the biggest business transformation of our lifetime
I've led organizations through several major technology shifts, and I genuinely believe AI is the most consequential one. MIT released a study late last year showing that AI can already perform tasks equivalent to 11.7% of the U.S. labor market. That’s roughly $1.2 trillion in wages in finance, healthcare, and professional services. What we see in tech layoffs is only the tip of the iceberg.
For public companies, the pressure is especially intense. Shareholders expect results on a quarterly timeline, and boards are asking hard questions about AI strategy. But this applies to every organization: the question isn't whether to adopt AI. It's whether you have the foundation in place to make it work.
AI foundations and your data
You’re not alone in this. This is a challenge I'm solving inside my organization too. Every AI initiative depends on data. Every single one. Your AI models, your machine learning, your predictive analytics. They're all only as good as the data they consume.
If your customer records are dispersed across five systems or your supplier information hasn't been validated in years, your AI investments will underperform. That's not a maybe. That's a guarantee. And here's the piece that gets missed: it's not enough to have data. It has to be clean and your people have to trust it.
When a sales leader pulls up a customer record and the address is wrong, they're not going to trust any data or decisions that use it. Same thing on the operations side. If your supplier data hasn't been touched in two years, no one’s going to let an automated procurement workflow run on it without a manual check. The data is there, sitting in the system, but people have been burned enough times that they’d rather work around it.
Trust starts with clean, governed, unified data. That's exactly what we built Black Tiger’s Data Hub to deliver. The platform cleanses, deduplicates, standardizes, and distributes your most critical data. That includes customer records, product information, supplier data, and more. One source of truth is available to everyone who needs it.
Navigating the transformation with you
I want to be straightforward about something. As the CEO of Black Tiger, I’m facing the same decisions every CEO is facing right now. Where can AI genuinely accelerate what we do? Where do we need to invest in our people? How do we make sure the moves we’re making today set us up for the next five years?
The answer we keep coming back to at Black Tiger is data. We know that clean, accurate, trusted data is the prerequisite to AI transformation that sticks. It’s not the only thing you need, but without good data, everything else falls short.
That’s our philosophy at Black Tiger and I believe in it as the foundation for our future. Having the right Data Hub supports governance and unlocks insights. It also provides the trusted foundation for analytics, compliance, AI, and digital transformation.
Looking ahead, companies that manage data well will lead this transformation. And the ones who start now will be ahead. Committing to getting your data right is how you build that advantage and adapt for what’s next.
