AI in the workforce: A shifting talent equation

As a child of the 80s, I remember staring at the perpetually blinking “12:00” on our family VCR and thinking, “There has to be a better way.” That moment stuck with me. From then on, I wanted to connect people with technology in ways that made sense to humans, not just engineers.

It wasn’t until I started my own company that I realized that making technology accessible wasn’t enough—it also had to work for the business. Today, I think of this balance as my triforce of power: people, technology, and business. When those three legs of the stool are balanced, organizations thrive.

That framing shapes how I talk with clients about Artificial Intelligence. As consultants, we’re in a unique position: we see across industries, company stages, and organizational structures. We notice patterns that any one company may miss. And one of the most important patterns I’m seeing today is how AI is reshaping the workforce—not just the jobs we do, but how organizations develop talent, allocate resources, and prepare for the future.

Technology: Faster, cheaper, smarter … different

It’s no surprise AI has captured so much attention. The pace of advancement is staggering. According to Epoch AI:

• Algorithms: Compute required to achieve the same performance in language and vision models is declining at a rate of 3x per year
• Hardware: Raw computational performance is increasing about 1.35x annually
• Data: Training datasets are growing nearly 4x each year

Put simply: every year, AI delivers significantly more for less and the pace isn’t slowing down. 

This unlocks obvious opportunities in areas like repetitive task automation and even generative content creation. However with ever-improving LLMs and machine vision, the capability goes far beyond fancy chatbots and improved scientific models. AI is transforming more traditional industrial applications through the convergence of modern, digital IT practices with previously analog and mechanical operational technology (OT) found in factories and warehouses. Add to that novel agentic business processes and advances in robotics and we are seeing a reshaping of what is possible in ways inconceivable just a few short years ago.

This isn’t just a story about bigger models or better benchmarks—it’s a story about accessibility. Capabilities that once belonged only to researchers or cloud giants are now within reach for startups, nonprofits, and team members throughout your organization.

The ripple effects are everywhere. A CTO client who hadn’t written code in years shared that they are now reviewing pull requests aided by GitHub Copilot. Engineering managers across multiple companies, once focused exclusively on team enablement, are using copilots to dive back in as individual contributors. Perhaps the most shocking thing I have witnessed is a twist on the traditional hackathon where non-developers (product managers and designers) leveraged AI to build features and even an entire product through a “vibeathon” with little help from traditional engineers.

Even outside of tech we are seeing significant changes in how technology enables who is working on what. KPMG is using AI to allow junior accountants to take on higher-level tax work sooner. Similarly, UK law firm McFarland’s is enabling new lawyers to perform research at a level that previously took years of experience.

AI isn’t just speeding up existing workflows. It’s reshaping who does the work and redefining roles, skillsets, and processes.

Business: Outcomes over hype

With hype everywhere, it’s tempting for organizations to “do AI” simply to say they’ve done it. In fact, AI washing is a thing, as the perceived value of AI is quite high. But the reality is sobering. A recent MIT study found that 95% of AI investments are not generating any return on investment. That’s billions of dollars being poured into pilots, prototypes, and tools with no measurable impact.

I’ve seen this firsthand. Companies launch ambitious AI initiatives, only to end up with shelf-agents (the modern day shelfware - referring to the shelves of unused software once purchased by organizations, never finding their place in daily operations) and frustrated teams. The common thread? They never defined the why. Every AI initiative should tie directly to a primary business objective:

• Reduce costs
• Increase revenue
• Mitigate risk

Once the objective is clear, a framework (such as the Business Value Framework) can help track activities against those goals and measure impact. Without this focus, even the most sophisticated models won’t move the needle.

This is especially critical as investment patterns shift. For years, growing infrastructure teams along with cloud and SaaS drove OpEx-heavy budgets. Now, with AI, the pendulum is swinging back to CapEx: pilots, proprietary models, and big bang enterprise solutions.

Procurement processes reflect this change. I’m seeing requests for AI platforms, copilots, and automation tools move swiftly (even outside of normal budget cycles) while headcount requests are delayed, downsized, or rejected. The message is clear: many leaders believe AI is a more scalable (and palatable) investment than investing in the workforce.

For businesses to realize true value from their AI investments, they must have a concrete mechanism for understanding, communicating, and measuring the impacts and outcomes.

People: The broken career ladder

One of the largest unintended consequences of AI adoption is the significant impact on our people.

Similar to the advent of other monumental technology shifts (e.g., the PC, the web), tech workers are at a crossroads. Do they jump on the bandwagon and wholesale shift their career—retraining themselves (on the majority of) how they do their jobs in the new paradigm followed by getting a new job in the new tech? Or do they hold off until the last responsible moment—personally trying to manage their full-time employment along with self-study and training in the new capabilities for when it becomes inevitable? This is a watershed moment for the tech industry. There is no simple answer and history tells us that years from now we will be far better equipped to judge the paths that people chose. 

For those not yet established in their careers, they are faced with a different challenge, that of a drastically shrinking pool of entry-level white-collar jobs.

Hiring freezes and layoffs are disproportionately impacting early-career professionals. While senior and specialized roles continue to be posted and filled, opportunities for new grads are drying up. Internship programs are being canceled. I’ve personally had to rescind a job offer at the last minute, leaving a recent graduate (and former intern) in a lurch. It was heartbreaking.

This isn’t confined to tech. In finance, directors are using LLMs to generate reports that once served as training ground for analysts. In law, entry-level research work is being automated. Traditional paths for on-the-job training and learning directly from your seniors isn’t possible in this world.

Experts are sounding the alarm. ABC News reported that AI may be “breaking the career ladder.” Lynn Wu, a Wharton professor, put it bluntly: “The biggest problem is that the career ladder is being broken.” Anu Madgavkar of McKinsey added: “You could have fewer people getting a foothold.”

Anthropic CEO Dario Amodei went further, describing the shift as a “white-collar bloodbath” in an Axios interview.

Whether you call it a bloodbath or a broken ladder, the pattern is clear: entry-level white-collar roles are disappearing, and with them, the traditional pathways into careers are being decimated.

The leadership dilemma

This creates a dilemma for organizations. If entry-level jobs are eliminated, what of our talent pipeline? How do we succession plan? Who becomes the next generation of leaders? How do we develop institutional knowledge, personal relationships, and practical experience that can’t be automated by an LLM?

The eternally optimistic entrepreneur in me sees a silver lining: today’s AI-native generation will harness these new tools to start companies, launch products, and create new industries we can’t yet conceive of. They will be forced to bypass traditional career ladders altogether and disrupt the current tech giants we naively see as “too big to fail” (growing up in the Motor City I am all too aware of the dangers of that line of thinking).

But for established organizations, the challenge is different. Without intentional strategies for developing talent, the leadership pipeline could dry up and innovative new thinking will become stagnant and inwardly facing at best. Companies risk creating a hollow middle; experienced (and deeply embedded) leaders at the top, powerful tools at the bottom, with very little in between.

That’s why I advise clients to think long-term. Invest in AI, yes, but also invest in people. Create opportunities for early-career talent to learn, experiment, and grow, even if AI could technically do the work faster. Avoid the short-term allure of doing more with less and consider how these tools could empower your people (including those you haven’t hired yet) to reach new heights.

The triforce of people, technology, and business demands balance. Lose one leg, and the whole stool tips over.

Conclusion: A call for balance

AI is the most powerful technological shift we’ve seen in decades, a true watershed moment. Its growth is exponential, its applications are seemingly endless, and its business potential is massive. But it is not neutral. AI is already reshaping the workforce in ways we can’t afford to ignore.

The opportunity is real: reduced costs, increased revenue, and mitigated risk.

But so is the cost: a decimated workforce, strategic stagnation through a race to the bottom, and hungry, innovative competition.

For organizations, the challenge is to embrace AI without hollowing out the human side of the workforce. For individuals, especially new grads, the challenge is to find innovative ways to nurture your career in an AI-first world.

As I remind my clients, it always comes back to balance. Technology may accelerate, business may demand results, but it’s people who ultimately make organizations sustainable. The triforce of your organization’s success—people, technology, and business—is only as strong as its weakest leg.

Jonathon Baugh is a Senior Client Partner at Test Double, and has experience in business value measurement, AI investments, and strategic planning.

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