Tech executives must step into R&D roles
AI is a shift in how work happens. It’s not like upgrading from paper to email; it’s more like discovering electricity when everyone else is still burning candles. That means tech leaders can’t just be advisors anymore. They have to get hands-on.
In the past, businesses looked to IT leaders to answer technical questions. One day, they’d be asked about cloud computing; the next, about their CEO’s smartwatch. But AI is different. It doesn’t support work, it does the work. And that means organizations need real expertise to separate hype from reality. If you’re a tech executive, you have a choice: become an internal AI strategist or let someone else shape how your company moves forward.
Here’s the key difference with AI: it doesn’t follow simple “if-this-then-that” rules like traditional software. You don’t need a perfectly structured dataset or a series of predefined inputs. You can feed it a messy, one-sentence prompt, and it will generate results, sometimes good, sometimes bad, but always unpredictable. That’s powerful, but it’s also risky.
Tech leaders must take a proactive role in experimenting with AI, running pilot projects, and proving what works. It’s not about adding an “AI initiative” to your to-do list; it’s about becoming the go-to expert in your company. If you don’t build that knowledge now, someone else, whether it’s a vendor or a competitor, will shape your company’s future with AI instead.
Is AI’s 24/7 work ethic a productivity boost or management nightmare?
AI doesn’t sleep, take lunch breaks, or call in sick. It works nonstop. That sounds great, until you realize that humans still have to keep up.
Companies already struggle with an “always-on” culture. Globalization means business happens around the clock, and AI will only accelerate that. Bots will process data, generate reports, write marketing content, and analyze trends while humans are asleep. Sounds efficient, right? But here’s the catch: that output still needs to be reviewed, refined, and acted on by people. And no human can match an AI’s pace.
Leaders need to rethink their approach to work. Instead of expecting employees to compete with AI’s speed, the focus should be on intelligent oversight. Humans should handle strategic decisions, while AI manages the repetitive grind. It’s the difference between running a factory and working on the assembly line.
The challenge is structuring this balance correctly. Some industries have experience managing high-output systems, automated manufacturing plants, for example, have been refining this balance for decades. But AI isn’t just producing widgets. It’s generating content, making predictions, and influencing decisions. If AI floods teams with more information than they can process, it doesn’t make them more productive, it drowns them in data.
The solution? Leaders must decide where AI adds the most value and set limits on unnecessary output. Not everything AI generates needs to be acted on. Managing AI is about control, not acceleration.
AI sounds human, but it doesn’t think like one
“AI can write like a person. It can answer questions, generate creative ideas, and even hold a conversation. But it can’t understand context like a human does.”
That’s a problem. When AI generates an answer, it isn’t pulling from lived experience or critical thinking, it’s predicting what the next word should be based on patterns in its training data. That means AI can be confident but completely wrong. And it doesn’t care. It doesn’t have morals, instincts, or accountability. It just produces output.
This isn’t a philosophical debate, it’s a practical business risk. If an employee gives bad information, you can retrain them. If AI does it, finding the root cause is much harder. Most modern AI systems use deep learning, meaning they don’t follow clear, traceable logic. You can’t “debug” them like a spreadsheet. If AI makes a flawed decision in a high-stakes scenario, whether it’s legal, financial, or medical, leaders need to be aware that it’s nearly impossible to audit its reasoning.
The reality is that AI is powerful, but it’s not infallible. Businesses must implement “trust but verify” policies. AI-generated content should always be reviewed by a human, and key decisions should never rely entirely on AI-driven insights.
Leaders who assume AI is as reliable as traditional software will run into serious issues. AI doesn’t follow rules, it follows probabilities. That makes it flexible, but also unpredictable. The companies that succeed with AI will be the ones that understand its strengths while guarding against its weaknesses.
Managing AI teams like human teams
While AI is changing how work gets done, it’s also changing who, or rather what, does the work. Just as the spread of computers created the IT department, AI is giving rise to a new role: the bot wrangler, a person responsible for managing a fleet of AI tools.
AI isn’t plug-and-play. These systems need oversight. They require someone who understands their strengths, limitations, and how to structure workflows to maximize efficiency while minimizing risks. Managing AI doesn’t mean setting it loose on a task. It means understanding when to use AI, how to break down complex problems into AI-manageable tasks, and who (or what) should refine the results.
The shift is fundamental. For years, businesses have optimized for individual contributors, highly skilled employees who work independently or in small teams. AI is changing that. A single employee can now have a team of AI assistants, each handling specialized tasks like research, data analysis, content generation, or even customer service. But here’s the challenge: managing AI requires a different skill set than managing people.
AI won’t get tired, complain, or demand a raise. But it also won’t think critically, understand nuance, or take responsibility when things go wrong. That means AI managers need to be highly strategic in task delegation. Instead of assuming AI will handle everything flawlessly, they need to know when to step in, refine results, and redirect AI efforts.
At the organizational level, this means companies need AI operations teams, not just IT staff. There’s no universal playbook for AI management yet, but one thing is clear: businesses that figure out how to integrate AI effectively will outpace those that simply let AI run unchecked.
The companies that succeed with AI won’t be the ones that throw the most money at it. They’ll be the ones that master the art of orchestrating AI workers the same way a great leader orchestrates a human team.
AI is replacing entry-level jobs but that’s not the real problem
Let’s talk about an uncomfortable truth: AI can already do the work of many junior employees. Whether it’s writing basic code, analyzing data, or drafting reports, AI is getting increasingly good at entry-level knowledge work. That raises a big question: If AI does the work of junior employees, how do we develop the next generation of senior leaders?
Most tech careers follow a clear path. You start as a junior developer, analyst, or engineer, learning foundational skills by handling repetitive but necessary tasks. Over time, you gain experience, move into senior roles, and eventually step into leadership. But what happens when AI takes over those foundational tasks? If companies eliminate junior roles entirely, they risk cutting off their own talent pipeline.
Think about it: Would you hire a junior JavaScript developer today if AI can write functional code on demand? Probably not. But today’s junior developers are tomorrow’s senior engineers, system architects, and CTOs. If they never get a chance to develop, businesses will find themselves with a top-heavy organization, lots of experienced leaders, but no rising talent to replace them.
The solution isn’t to force junior employees into outdated roles. It’s to redesign entry-level positions to focus on AI management, system architecture, and automation strategy. Instead of training new employees to write code manually, train them to direct AI, validate outputs, and improve system efficiency.
Here’s what forward-thinking companies will do:
- Shift junior roles toward AI oversight: Entry-level employees should focus on verifying AI-generated work, refining it, and identifying automation opportunities.
- Integrate AI into training programs: Teach new employees how to work with AI rather than being replaced by it.
- Avoid over-reliance on external talent: Companies that eliminate junior roles today will struggle in a few years when they need experienced tech leaders but haven’t trained any in-house.
This is a pivotal moment. Companies can either use AI to improve talent development or accidentally create a leadership gap that will cost them in the long run. AI is here to stay, but the smartest businesses will make sure their future leaders are too.
Final thoughts
AI is changing how businesses operate, but it doesn’t replace leadership, it demands better leadership. Companies that adapt quickly, restructure roles wisely, and train employees to work alongside AI will gain an edge. Those that rely on outdated structures will fall behind.
Tech leaders today have a choice: build the future of work, or react to it too late.
Key takeaways
- Strategic AI R&D: Tech leaders must drive internal R&D to harness AI’s dynamic capabilities. Prioritize controlled pilot projects to validate AI’s impact and position your organization as an innovation leader.
- Balancing productivity with oversight: AI delivers non-stop output, but leaders need to implement robust oversight. Establish systems to review and refine AI-generated data to prevent decision-making overload.
- New roles for AI management: Embrace emerging roles such as AI coordinators to manage and integrate digital workers effectively. This shift makes sure of optimal task delegation and risk mitigation across tech teams.
- Preserving the talent pipeline: Redesign entry-level positions to focus on AI management and strategic oversight. This approach safeguards future leadership by cultivating skills essential in an AI-augmented workplace.
