Ask any engineer what’s new
this year and you’ll hear the same answer: AI. Code that writes itself,
dashboards that predict incidents, chatbots that solve problems before a ticket
even opens, the solutions are endless.
In my career spanning around
three decades, I’ve steered tech teams, global delivery centers, and client
programs through wave after wave of technology-driven change. And as technology
continues to advance, the possibilities for AI applications will only continue
to grow, which in turn means we need to redefine the way we work, be agile,
adapt to the situation, and work with AI. In short, develop a learner's and
leader's mindset.
Based on my experience and
learnings, below are the five simples but proven, reliable and practical ways
to keep your tech team resilient and ready.
I.
LEAD WITH PURPOSE, NOT JUST
PROCESS
We see new tools alter the
‘how’ almost every week. That’s why the ‘why’ needs to stay strong. Start every
transformation conversation by pairing it to a bigger promise, whether it is
faster time-to-value for clients, sharper risk control, or safer releases.
Then, keep that promise alive via open stand-ups with a quick reminder of the
mission and close retrospectives by mapping each win back to it.
When people can trace a
straight line from today’s task to tomorrow’s impact, change will feel more
like progress and less like churn. In one of my roles, we adopted AI-based test
automation at scale. The initial disruption was high, and teams were worried
about losing their roles. But by continuously linking the change to our purpose
of “delivering smarter, faster, and safer for our clients”, resistance gave way
to ownership.
II.
MAKE LEARNING PART OF THE DAILY
RHYTHM
AI capabilities are advancing
rapidly, often outpacing traditional annual training cycles. To keep up,
leaders must embed learning into everyday work. Encourage curious teammates to
experiment with GenAI or ML tools and share their findings during reviews, what
worked, what didn’t, and why.
This fosters a culture where
trying, tweaking, and teaching are part of the job, not special assignments. An
experiment by a non-AI developer in my team, learning and using AI and
automation scripts, helped reduce documentation-to-code time by 70%.
III.
DESIGN TRUST INTO EVERY AI
DECISION
Accuracy is often impressive;
however, it is always trust that reassures. Whether you’re serving regulators,
customers, or your own engineers, make it easy to explain how the algorithm got
from input to outcome.
While working on a
workflow-automation project for a client, we made each AI decision node
explainable in plain language, bias checks visible, and logic paths exposed.
That clarity cooled most resistance before it surfaced, as it allowed
stakeholders to see not just that the model worked but how it worked.
IV. REDEFINE
ROLES TO UNLOCK HUMAN POTENTIAL
Automation pays off when it
clears the grunt work and lets people aim higher. Map out the repetitive steps
a script can handle, then sketch the new shape of the job: analysts become AI
co-pilots interpreting insights; engineers focus on edge-case creativity and
architectural resilience; support teams’ shift from ticket triage to proactive
experience design.
Tie the transition to a short
skill sprint or micro-credential so the growth path feels real, not
theoretical. The message is clear: bots handle the busywork, while humans focus
on meaningful contributions without the need to replace one another.
V. STAY
CALM WHEN THE GROUND SHIFTS
Even the best roadmaps
encounter turbulence, model drift, compliance changes, or unexpected outages.
During a high-stakes AI rollout, simultaneous tech glitches and
change-management pushback threatened momentum. I remember one instance when,
during a high-stakes AI rollout for an important client, simultaneous tech
glitches and change-management pushback threatened momentum.
What steadied the project
wasn’t a heroic pivot, but calm and frequent communication and decisions tied
to long-term value. In volatile moments, steady transparent updates turn chaos
into a manageable variable.
THE BOTTOM LINE
The tools will continue to
evolve. What won’t change is the human need for leaders who can translate that
evolution into vision. The next AI upgrade is perhaps just around the corner,
and that’s the exciting part. The way forward isn’t to tame the pace, but to
find creative ways to harness it. Trading fear for curiosity, blueprints for
prototypes, and “What if?” for “Why not?” can go a long way in creating a
workforce that’s future AI-ready. In the age of AI, leadership isn’t about
having all the answers, it’s about asking the right questions and empowering
others to explore them.
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