Artificial intelligence (AI) is no longer a distant concept; it’s embedded in everyday business operations across industries. Yet, despite AI’s proliferation, many professionals misunderstand how to leverage it effectively for career growth and organisational impact. Neal Lathia, CTO and co-founder of Gradient Labs, a conversational AI platform for financial services, has spent over a decade building production ML systems and leading AI-native teams. Based on his experience, Lathia identifies four career lessons about AI that are often misunderstood – and why getting them wrong can stall professional growth.

1. Stop Optimising For AI Metrics That Don’t Map To Real Work

A common trap when working with AI is focusing on technical metrics rather than business outcomes. “There’s an age-old problem with metrics – when you’re building an AI system, your team might look at things like accuracy, precision, and recall. But when you’ve shipped an AI system, you’ll want it to impact business outcomes: things like cost saving, revenue, or retention. The challenge has always been that it’s not clear what the relationship between these two really is,” Lathia says.

The lesson? Metrics alone aren’t enough. AI teams can get lost in numbers without considering whether the system truly drives value for the organisation. Lathia explains that evaluating AI output in context is crucial:

“We have found that the best way to navigate that space is not just rely on metrics, but also inspect the actual outputs of each AI system. Hiding behind metrics could detract from the actual customer experience that AI systems are creating. If it looks accurate enough, what are those successful and failed cases like? The best way to know is to look!” he added.

By assessing real-world outputs, teams can better align AI systems with the outcomes that matter, rather than falling prey to a false sense of progress measured by numbers alone.

2. AI Works Best As An Enabler, Not A Co-Pilot

In the public discourse, AI is often framed in extremes: it will either replace everyone or only augment those who know how to use it. Lathia urges a more nuanced perspective: some tasks are automatable, while others always need human oversight.

“At Gradient Labs, we do not believe that AI should be a co-pilot for humans. We believe that humans should manage AI agents to enable them to work at scale. When the AI agent needs input from its manager, it has a way to ask for it,” he says.

In practice, this philosophy reshapes roles. Professionals aren’t simply ceding tasks to AI; they’re enabling AI agents to operate efficiently, stepping in when judgement or oversight is required. By seeing AI as an enabler rather than a replacement, professionals can multiply their impact without losing control or accountability.

3. AI Is Automating Tasks, Not Accountability

AI can automate vast swathes of work, but it cannot absorb responsibility. Lathia notes that domain expertise is becoming more critical than ever because AI requires accurate context and knowledge to function effectively.

“Because of this, we are seeing roles change shape: domain experts, who used to be tasked with doing the work, are now tasked with ensuring that the AI systems have the right knowledge and context to automate the work well. The importance of domain expertise has never been higher!” he added.

In other words, automation does not eliminate the need for human judgment — it shifts the focus from doing tasks manually to overseeing the quality and knowledge inputs of AI systems. Professionals who understand this transition can position themselves as indispensable, bridging the gap between AI outputs and business outcomes.

4. The Biggest Career Risk Is Treating AI As Static

AI is evolving at an unprecedented pace. Technologies and models that seemed revolutionary six months ago may already be outdated. Lathia emphasizes that professionals need to stay agile to maintain relevance:

“Almost all tech departments out there are, in some capacity, evaluating how impactful AI could be for them, their output, or their organisation. Some of them decide that it has little to no value at a specific point in time. I regularly remind people: the field of AI is moving so fast that the assumptions and insights that you had from 6 months ago might now be largely outdated or supplanted by better models. Keep that in mind: working with AI means working with a moving target.”

This lesson is particularly critical for career-minded professionals. Viewing AI as static – as a fixed toolset or skill – risks obsolescence. Those who remain curious, continuously learning, and open to new applications of AI are far better positioned to thrive in rapidly evolving environments.

Aligning Career Growth With AI Reality
Taken together, Lathia’s insights reveal a clear pattern: success with AI is less about mastering the technology itself and more about understanding its role in real-world systems, maintaining human oversight, and adapting continuously.

• Focus on business impact, not just metrics. Metrics like accuracy or recall are starting points, but real-world validation matters most.
• Use AI to enable work at scale. Professionals should see AI as a tool that amplifies their ability to act, not as a replacement.
• Maintain accountability and context. Human expertise remains critical to ensure AI is applied correctly and responsibly.
• Stay adaptive in a moving landscape. The field evolves constantly, and agility is key to staying relevant.

For professionals across industries, these lessons are actionable. They provide a roadmap for leveraging AI as a career accelerator rather than a threat.

Why This Matters
The conversation around AI often skews sensational: headlines warn of mass job displacement or tout AI as the ultimate productivity hack. Lathia’s perspective is grounded in real-world AI deployment and team leadership experience since 2010. By focusing on what truly matters — measurable impact, human oversight, and continuous adaptation — professionals can align their skills with the evolving demands of AI-driven workplaces.

As organisations increasingly adopt AI, the lessons Lathia highlights will become crucial not just for technologists but for managers, executives, and business leaders. Those who ignore these principles risk investing in the wrong metrics, mismanaging AI adoption, or falling behind as the technology continues to accelerate.

Lathia’s four career lessons offer a blueprint for navigating the AI era. By focusing on outcomes over metrics, enabling AI rather than co-piloting it, preserving accountability, and embracing continuous change, professionals can thrive in environments where AI is not just a tool, but a core element of organizational performance.

AI is changing how we work, but the most successful professionals will be those who understand both the capabilities and the limitations of the technology, continuously adapting while maintaining human judgment at the center.