Career adaptability: riding the waves from mainframe to AI and the energy transition

Career adaptability: riding the waves from mainframe to AI and the energy transition

Mainframe. Desktop. Smartphone. Energy transition. AI. The next wave is already forming, and career adaptability is the skill that lets you ride it instead of being wiped out.

Across my professional journey, adapting early to technology shifts created purpose as much as progress—very much in line with Darwin’s observation that survival favors adaptability. For those of us 50+, we have lived through an innovation cycle no previous generation of Homo sapiens has experienced, and we have navigated it well by doubling down on learning and execution.

What worries me is whether younger cohorts are being prepared not just to ideate but to deliver. Rapid change reshapes behavior, careers, and expectations. The answer, again, is career adaptability: the mindset and systems that convert curiosity into repeatable results.

Milestones and inflection points in career adaptability

In the 1990s I worked at Xerox managing IBM mainframes, modems, and data compression. Sensing the next shift, I joined HP as desktop computing and the Internet went mainstream. Then came a tsunami called mobile: at Nokia and later Samsung Electronics, I moved from feature phones to smartphones, helping scale devices and data networks that redefined user behavior.

When the global spotlight turned to the energy system, I pivoted again—studying the field and joining Fronius in Austria to work on solar and power electronics as the energy transition accelerated. Most recently, AI became the layer that amplifies everything—strategy, operations, and learning—driven by advances from organizations like OpenAI.

Career adaptability and Gen Y/Z: strengths and gaps

My collaborations with Gen Y and Gen Z reveal major strengths: data fluency, rapid problem framing, and a fearless willingness to challenge status quo assumptions. Huge potential. Yet execution frequently stalls—motivation dips, objections feel personal, and expectations clash with real-world timelines. The antidote is structured practice: OKRs, feedback loops, and skin in the game that ties ideas to delivery.

A practical playbook for career adaptability:

• Curiosity: follow weak signals early—read technical roadmaps, standards bodies, and developer forums.

• Learning: build repeatable habits—certifications, side projects, and peer reviews that compound knowledge.

• Achievements: ship work. Choose roles where outcomes are measurable: shipped code, deployed systems, revenue, uptime, or cost-to-serve.

• Timing: move before the crowd. Track S-curves—mainframe to desktop, desktop to mobile, mobile to cloud/AI, fossil to low-carbon power—and jump when adjacent skills transfer.

• Resilience: treat setbacks as training data. Execution beats perfection; iteration beats opinion.

No innovation—past or future—replaces the human mix of judgment, emotional self-protection, and the willingness to get up after each fall. If you want a rule of thumb: dare, try, and keep trying. Progress often looks like seventy times seven attempts that finally click.

What comes next? Likely the fusion of AI agents, edge computing, and flexible energy systems. Whatever the label, the winners will be those who practice career adaptability daily—learning faster than the environment changes and delivering value that compounds.