Tag Archive for: efficiency

why women need to learn ai

By Jessica Darmoni

“Imagine you start a new job and your boss gives you a Mac, but you are used to a PC,” said Michelle Ann Gitliz, Founder & CEO of Change Agents Technologies, Inc. a SaaS company that leverages its proprietary AI and automation platforms to transform how businesses manage compliance. “It’s a condition of your job to work with it and at first it feels unfamiliar or uncomfortable but eventually you adapt because it is part of the workplace. AI will likely follow a similar path. Many future jobs will require workers to collaborate with AI systems whether they feel ready or not.”

Artificial intelligence is no longer a futuristic concept reserved for tech companies and science fiction movies. It is rapidly becoming part of everyday life, shaping how we work, communicate, create, and make decisions. Yet for many women, AI can still feel intimidating, overly technical, or disconnected from reality. The truth is that understanding AI is not just about career advancement; it is about empowerment, safety, and maintaining agency in a world increasingly driven by technology.

Leveraging AI for Efficiency and Modernization

“Engaging with the technology allows people to understand both it’s strengths and it’s pitfalls,” says Gitlitz. “One of the most important things to understand is that AI is a tool and like every major technological shift before it, the people who benefit the most will be those who learn how to use it wisely. “

Nancy Li is a Los-Angeles based consultant helping firms leverage AI and Machine learning to scale and scope their organizations.

“Many organizations genuinely want to improve efficiency and modernize legacy systems,” she says. “The reality is that implementation is difficult. Most businesses are still trying to figure out how AI can truly augment workflows.”

She estimates that we are still several years away from full-scale adoption across industries and that AI must solve real-world problems.

In the meantime, there is an important opportunity for women to learn, experiment, and position themselves ahead of the curve instead of being left behind by it.

A Successful Use Case

According to Li, there is one major area where AI has already demonstrated measurable success: programming and coding. AI systems can now assist with software engineering, data analysis, automation, and workflow optimization. However even in those fields, humans are still essential. Someone has to guide the system, verify the output, and determine whether the result is useful, ethical, and accurate.

Li believes the future workforce may evolve into teams of “quality control managers” overseeing AI-powered systems and digital agents. That future raises a fascinating question: what does work look like when technology can perform many of the repetitive tasks people once did for a living?

The answer may depend less on technical expertise and more on judgment, creativity, discretion, and taste. AI can generate endless amounts of information, but it cannot fully replace human intuition or emotional intelligence. Knowing what you want, understanding your goals, and evaluating whether an AI-generated solution actually makes sense will become critical skills.

Understanding AI as a Matter of Safety

Women do not need to become engineers to participate in the AI economy. However, they do need enough familiarity to ask informed questions, challenge assumptions, and protect themselves.

Gitliz reminds people not to click “agree” on terms of service without reading them, or hand over personal information without considering the consequences.

“Your data is valuable. Your birthday, browsing habits, preferences, and online behavior can all be used to build detailed profiles for advertising and targeting.,” she says. “What matters is knowing the options exist, understanding the terms of use, and recognizing the impact these tools can have on your life.”

Women especially should understand how their data is collected, stored, and used. Learning the basics of AI and digital systems helps people recognize risks, identify manipulation, and make informed decisions online. You cannot safeguard yourself from technology if you do not understand how it works.

Cathy Yoon, General Counsel at Harmonic, emphasizes that the human element is still essential when leveraging AI systems. She believes that AI is good to fill in workflow gaps but that humans still need to verify outputs.

“Verification is essential because AI systems can still produce inaccurate, biased, or misleading information,” she said. “Learning how to question outputs and confirm facts will become just as important as learning how to generate them.”

Meritocracy Matters

There is also a larger cultural shift happening around merit and opportunity. Increasingly, employers and industries care less about where someone went to school and more about what they can actually do. In some emerging industries, especially digital assets and technology, AI literacy will become the new college degree and a baseline expectation rather than a specialized skill.

“This creates a unique opportunity for women from diverse backgrounds. AI has the potential to level certain playing fields because access to knowledge is more open than ever before,” says Li.

People can learn independently, build portfolios, launch businesses, automate workflows, and develop expertise outside traditional institutions.

The goal when women approach AI should be informed participation. The women who thrive in the AI era will not necessarily be the most technical. They will be the ones who stay curious, ask questions, protect their data, verify information, and learn how to use technology to supplement their strengths rather than replace them.

AI is coming whether we embrace it or not. The safest and most empowering choice is to understand it well enough to shape how it shapes us.

Sustainable success Jharna SahaThis Earth Day, the conversation worth having is less about individual behavior and more about the systems we’ve left unchanged. Jharna Saha, Co-Founder and CMO of Enlog, is working on one of the most overlooked of them: what happens to electricity once it’s inside a building. Enlog enables buildings and facilities to continuously optimize their electricity use through autonomous intelligence — reducing energy consumption by 20–25% without the heavy infrastructure overhaul that traditional retrofits require. Energy efficiency is increasingly becoming a new currency for businesses, one that delivers clear ROI, often with payback periods as short as 6–8 months purely through energy savings.

“What inspires me is building toward a world where efficiency isn’t dependent on awareness or manual control,” says Saha. “Where buildings aren’t passive consumers, they’re responsive systems. That future is technically possible right now. The gap is in how we think about this problem, not in the technology.”

We spoke with Saha about what drives her, what she’s learned building a deep tech company, and the future she’s working toward.

Start with the System, Not the Person

Saha’s path into energy didn’t begin with engineering. Her first job was in marketing, working on Earth Hour, the campaign where people switch off their lights for an hour to make a statement about energy. It was there that a contradiction became impossible to ignore.

“I remember seeing large commercial buildings fully running late at night — cooling systems, lights, everything on — in Cyber City Gurgaon. We were asking people at home to switch things off, while buildings around us consumed at a scale no individual action could offset.”

The dissonance stuck. “We expect people to behave like energy experts. Most people can’t, and realistically, they won’t. So why are we trying to change human behavior instead of fixing the system itself?” That question led to Enlog.

For anyone building a career in sustainability or deep tech, this reframe matters: the most durable solutions don’t rely on changing what people do. They change what the system does by default.

Clarity Is What Scales

The journey from that early question to a functioning company wasn’t linear. “Deep tech is not a straight path,” Saha says. “There are long gestation periods, failures, and iterations. Delivering something truly breakthrough takes that. It’s not about small deltas.”

What kept her oriented through it was a commitment to first principles thinking. “You come across many opinions along the way. But real collaboration happens with clarity and that’s how you actually scale.”

That discipline shows up especially during hard stretches. “In deep tech, cycles are long. You’re not just building a product; you’re building trust in a new way of doing things.” When momentum stalls, Saha returns to the ground truth: “What does the data say? Where is the real inefficiency? That clarity cuts through opinion and noise.”

The Two Skills That Will Define Future Leaders

Ask Saha what capabilities will matter most going forward, and she doesn’t name a technical domain. She names two qualities that are harder to develop and easier to underestimate.

“One is emotional intelligence, not just in managing people, but in navigating uncertainty without overreacting. The ability to stay clear-headed when the situation is genuinely ambiguous.”

The second is synthesis. “Leaders today don’t struggle from lack of information. They struggle from too much of it.”

The ability to take multiple signals — data, context, external shifts and quickly identify what actually matters is increasingly where leadership leverage lives. These two skills reinforce each other: emotional grounding creates the conditions for clear thinking, and clear thinking makes decisive action possible.

Let Your Team Raise Your Standard

When asked who has shaped the way she leads, Saha’s answer is her team.

“I’ve watched them go deep into problems that most people would have given up on, break down assumptions, question the obvious, come back with insights that changed how we think about the product entirely. That level of depth is rare. And when you see it consistently, it quietly raises your own standard. You stop accepting surface-level thinking from yourself.”

The environments and people you choose to work alongside don’t just affect output, they recalibrate your baseline.

Knock on More Doors — Simultaneously

The most useful advice Saha has received is also the most literal: knock on more doors.

“Whether it’s partnerships, deployments, or policy conversations, I don’t depend on one path. I keep multiple conversations alive simultaneously. Some open fast. Some take a year. But the moment you limit yourself to one or two, you’ve already slowed yourself down without realizing it.”

Career opportunity works the same way. A single application, a single mentor, a single network, these create fragility. Building in parallel, even when one path looks most promising, is what sustains momentum across the long cycles that meaningful work requires.

The Permission You’re Waiting For Isn’t Coming

Saha has spoken with over 800 students across colleges, particularly young women without access to strong networks early on. The pattern she sees most often has nothing to do with ability.

“Most of them are genuinely capable, but they’re waiting for someone to tell them it’s okay to go. That permission never comes from outside. That’s the thing I try to leave them with.”

Her other consistent message: go deep. “Don’t just execute what’s asked of you. Think about how what you’re building can scale beyond you. Ownership and scalability together is where real impact lives.”

To her younger self, she’d say the same: “You saw the problem clearly. You just needed to trust that seeing it was enough to start.”

A Different Kind of Sustainability

Saha’s vision for the next decade is specific: “I want to help build a world where managing energy becomes invisible. Where buildings understand and optimize their own consumption in real time — without waiting for someone to notice, without depending on manual intervention.” If that becomes standard, she argues, “efficiency, in that sense, becomes a primary energy source.”

As Saha puts it: “The real constraint in the next decade won’t be generation. It will be how intelligently we use what we already have.”