Future-Proof: Building a Long-Term Career in IT in the AI Era
Opening: The Big Question
Good evening, Toastmaster of the Evening, fellow
Toastmasters, and guests. Let me begin with a question: if your child, your
younger colleague, or even your younger self asked, “Is IT still a good career
for the next 20 years?” what would you say? Many of us entered IT because it
promised growth, global opportunities, and better income. But today, after
Generative AI, AI agents, and Agentic AI, that question has become more
serious. Will AI take away IT jobs, or will it create a new generation of IT
careers?
Objective and Personal Credibility
Tonight, I want to share my point of view on “Building a
Long-Term Career in IT in the AI Era.” My objective is simple: to show how IT
work was done in the past, how it is being done today, and how it may be done
in the future. More importantly, I want to reflect on what each of us must do
to build a meaningful, future-ready career in IT. I have spent more than 20
years in this field, starting as a junior programmer and growing through technical
and management roles. From that journey, I learned one lesson: in IT, the
people who survive are not always the most intelligent; they are the ones who
keep adapting.
Roadmap: Past, Present, Future
My speech has three main parts. First, I will look back at the
past, when IT careers were built through programming, delivery discipline,
offshore execution, and a clear career ladder. Second, I will explain the
present, where Gen-AI, AI agents, and Agentic AI are changing how we code,
test, document, manage, and support technology. Third, I will look ahead to the
future and share what professionals must do to remain relevant for the next 10,
20, or even 30 years. Let us start with the past.
Part 1: Past — Manual, Structured, People-Intensive IT
In the past, IT was largely manual, structured, and people
intensive. Twenty years ago, if someone learned basic programming, database
concepts, or web development, it was often enough to enter a large IT services
company. The world was digitizing. Banks, insurance companies, manufacturers,
retailers, and governments needed software systems. They needed people to
build, maintain, test, support, and improve those systems. IT was no longer a
back-office function; it became the backbone of business operations.
This created a global opportunity. Offshoring opened doors
for skilled professionals in countries such as India, the Philippines, Vietnam,
and other Asian markets. Companies gained access to talent and cost efficiency.
Employees gained better salaries, international exposure, and new career
mobility. For many families, IT became a ladder of social progress.
The career ladder was also clear. A person could start as an
intern or junior programmer, then grow into programmer, senior programmer,
lead, manager, and senior manager roles. As experience increased,
responsibility increased. Many engineers aimed to move from execution roles to
leadership roles where influence and decision-making were higher.
Frequent job changes also became common. When demand was
high and skills were scarce, professionals could change companies and receive better
opportunities. People with five to eight years of experience were especially
attractive because they had both technical knowledge and delivery experience.
For many years, this model worked well. Businesses needed
more software, IT companies needed more people, and professionals built careers
by adding skills and moving up the ladder. Work was done mainly by human
effort: coding, documentation, testing, support, and status tracking. But every
strong career model eventually faces disruption. For IT, that disruption became
visible in November 2022, when ChatGPT was launched and Generative AI entered
everyday conversation.
Part 2: Present — Gen-AI, AI Agents, and Agentic AI
This brings me to the present: how IT work is being done
today. The IT industry can now be understood in two broad periods, the pre-AI
era and the post-AI era. We are only a few years into the post-AI period, but
the speed of change is already enormous. What makes this change different is
not only the technology itself, but the fact that ordinary users can now
interact with AI through simple language.
Marker:
Explain Gen-AI Simply
ChatGPT is one of the best-known examples of Generative AI, or
Gen-AI. In simple words, Gen-AI can create new content based on our prompt.
Earlier, software systems mainly stored data, processed transactions, searched
information, or generated fixed reports. But Gen-AI can write text, summarize
documents, translate languages, create presentations, write code, and explain
complex topics. For the first time, ordinary people could interact with
advanced AI using simple natural language. Other major players followed OpenAI,
such as Gemini from Google, Copilot from Microsoft, Claude from Anthropic, and
many more. These tools made AI accessible not only to engineers, but also to
students, managers, and business users.
ChatGPT reportedly reached around 100 million monthly active
users within about two months of launch, showing one important point: when
technology becomes easy to use, its impact becomes exponential. People did not
need a manual. They simply typed a question, gave instructions, and received
help immediately.
Gen-AI also led to many tools that use these models in the
background, including coding assistants, meeting summarizers, translation
tools, design tools, customer service bots, and automated documentation tools.
In IT, Gen-AI is changing how people write code, test applications, analyze
logs, prepare reports, create user stories, and support customers. But Gen-AI
is not magic. It still needs human judgment, domain knowledge, data quality,
security controls, and responsible usage.
Marker:
Move from Gen-AI to AI Agents
The evolution of Gen-AI led to the invention of AI agents.
An AI agent is not just a chatbot that answers one question at a time. It is a
software assistant that can understand a goal, break it into steps, use tools
or data, and complete a task with limited human guidance.
For example, instead of only asking AI to draft an email, we
can ask an AI agent to read a customer request, check system information,
prepare a response, create a task, and remind the owner to follow up. In the
workplace, AI agents are already supporting customer service, document
summarization, testing, workflow automation, and knowledge search. The future
employee will not only use AI as a search tool but work with AI agents as
digital teammates.
Marker:
Clarify Agentic AI
This evolution leads us to Agentic AI. If Gen-AI creates
content, and an AI agent completes a task, Agentic AI pursues a larger goal
more independently. It can understand the objective, plan steps, use tools,
observe results, adjust its approach, and continue until the work is completed
or human approval is needed. In simple terms, Gen-AI answers and creates; AI
agents act on specific tasks; and Agentic AI coordinates actions toward a
bigger outcome. This is powerful, but it requires governance, security, data
quality, and human oversight.
The impact on IT is massive. Tasks once handled by
entry-level engineers—basic coding, documentation, test case creation, log
analysis, and first-level support—can now be accelerated or partly automated.
Some management activities, such as reporting, meeting summaries, planning
drafts, and follow-up tracking, are also being supported by AI. This does not
mean every job will disappear, but every role will change.
Part 3: Future — Humans Working with Digital Teammates
This takes us to the future. A future IT team may look very
different from the past. A manager may lead both people and AI agents. A
developer may guide an AI coding assistant, review the output, and ensure
quality. A tester may design the testing strategy and validate AI-generated
test assets. Productivity will increase, but expectations will also increase.
We may deliver larger projects faster, with smaller teams and more automation.
The value of a professional will shift from doing repetitive work to thinking
clearly, asking better questions, validating outputs, managing risks, and
making responsible decisions.
Career Question: How Do We Stay Relevant?
If this is the direction of the industry, the important
question is: how can we build a long-term career in IT in this AI era?
Career
Action 1: Become AI-Literate
First, we must become AI-literate. AI literacy does not mean
everyone must become an AI scientist. It means we should understand what Gen-AI
can do, what AI agents can do, where Agentic AI can help, and where these tools
can fail. We must learn prompt writing, responsible usage, data privacy,
security, and validation. In the AI era, blindly trusting AI is risky, but
ignoring AI is even riskier.
Career
Action 2: Strengthen Domain Expertise
Second, we must strengthen our domain expertise. AI can
generate answers, but it does not automatically understand the full business
context. A person who understands insurance, banking, healthcare,
manufacturing, cloud operations, cybersecurity, data governance, or project
delivery will remain valuable because they can judge whether the AI output is
practical, safe, and aligned with business needs. Domain expertise plus AI
capability will be a powerful combination.
Career
Action 3: Own Outcomes, Not Just Tasks
Third, we must move from task execution to outcome
ownership. In the past, doing assigned tasks well was enough for many roles. In
the future, professionals must understand the goal, design the approach, use AI
tools effectively, validate the result, communicate clearly, and take
responsibility for outcomes. This is where human strengths—judgment, ethics,
empathy, leadership, creativity, and accountability—become more important, not
less.
Personal Reflection: My Own Reinvention
Let me connect this to my own journey. I started in the
pre-AI era, where programming and delivery discipline gave me a strong
foundation. Today, I am in the transition phase of the post-AI era. I cannot
depend only on what worked 20 years ago. I must unlearn some old ways and learn
how to use AI in project management, reporting, analysis, documentation,
testing, and decision support. For mid-level managers like me, this is
especially important. If our role is only to collect status, prepare reports,
and forward emails, AI will reduce the need for that role. But if our role is
to solve problems, manage stakeholders, guide teams, use AI responsibly, and
deliver outcomes, our value will remain strong.
Key Takeaway: Learn, Adapt, Specialize, Lead, Humanize
So, what is the career formula for the AI era? I would
summarize it in five words: learn, adapt, specialize, lead, and humanize. Learn
AI tools continuously, not as a one-time training but as a regular habit. Adapt
your work style quickly, because old methods may not always be enough.
Specialize in a domain or niche skill so that your judgment becomes valuable.
Lead humans and digital teammates together, because the future workplace will
include both. And humanize technology by applying ethics, empathy, and
responsibility. If we do these five things, AI will not simply be a threat; it
can become a career multiplier.
