Why women should build their front row; AI automation and its impact on jobs

Building a “front row” means creating a trusted group of people who support a woman’s career. They can encourage her, assure her during difficult moments, and help her move towards new opportunities. The idea matters because workplace barriers are rarely solved by individual effort alone.

Kripa Venkatachalam discusses this idea at informal forums at Cadence. Her front row may include family, colleagues, mentors, managers, and professional contacts. These people can offer advice, make useful introductions, recognise her work, or encourage her to accept a bigger role. Organisations and managers also have a responsibility to look harder and encourage women forward.

The current reality is that women may still feel pressure to manage every responsibility themselves. Kripa urges them to seek support and make their family, workplace, and society work for them. She also views careers as a jungle gym rather than a straight ladder, allowing people to pursue growth through varied roles.

Mentors can provide advice, perspective, and encouragement when women face difficult career choices. Family support can help women manage responsibilities and make time for demanding opportunities. Workplace policies and supportive managers can reduce barriers by recognising women’s contributions and actively encouraging them. Professional networks can provide contacts, information, and people willing to advocate for advancement.

Kripa Venkatachalam’s “front row” brings these forms of support together. At Cadence, she speaks about building a group of people who cheer for you. Her advice is practical: seek help, use your network, and do not assume that every challenge must be handled alone. She says women should make their family, workplace, and society work for them.

These supports matter because career growth depends on more than talent or effort. Organisations and managers must look closer and encourage women forward. Kripa’s own career, spanning roles at several companies, also reflects a flexible “jungle gym” approach rather than a single upward ladder.

AI automation means using artificial intelligence to carry out tasks that previously required people. It can change how work is performed, which skills employers need, and how many workers are needed for particular tasks. Its importance comes from its broad reach across both office-based work and smaller businesses.

At TechSparks 2026, K Yatish Rajawat said AI is already affecting white-collar work across sectors. The article also says MSMEs, which Rajawat calls India’s largest job engine, are losing jobs to automation. These examples show that AI’s effects are not limited to one industry or only to large technology companies.

The immediate reality is workplace change and possible job loss. The article does not specify particular occupations or technologies. It does point to reskilling as part of the next wave of employment, alongside entrepreneurship and data laws. Workers and businesses therefore face pressure to adapt as AI investments continue and automation spreads.

Microsoft’s voluntary exit programme is a workforce adjustment option for eligible employees in India, the United States, the United Kingdom, and Canada. Around 60,000 employees globally are eligible. The programme matters because Microsoft is reshaping its workforce while making heavy investments in AI.

The package offers one month of base salary for each completed year of service. It also includes health insurance and career transition services. Microsoft has about 23,000 employees in India, but the article does not say how many Indian employees qualify. Eligibility is therefore broader than the company’s Indian headcount alone.

The programme shows how AI investment can coincide with workforce changes at a major technology company. It is voluntary, so eligible employees can choose whether to leave under the stated terms. The article does not provide the expected number of departures or explain which roles are most affected.

When AI and automation perform tasks people previously handled, businesses may need fewer workers for those tasks. Workers can face job loss or pressure to learn new skills. The effect matters because employment is tied to how businesses invest, grow, and organise work. Automation can therefore alter both individual careers and the wider job market.

The article gives two direct examples. K Yatish Rajawat says AI is already affecting white-collar work across sectors. He also says MSMEs are losing jobs to automation. These businesses are especially important because he calls them India’s largest job engine. The same technology that can improve business processes can therefore reduce employment.

The article points to reskilling and entrepreneurship as part of the response. It does not quantify total job losses or identify every affected occupation. However, its warning is clear: investment and automation no longer guarantee job creation. Future employment will depend on how workers, MSMEs, and new entrepreneurs adapt.

Small and medium-sized businesses matter because they generate a large share of employment in India. K Yatish Rajawat describes MSMEs as the country’s largest job engine. Their importance goes beyond individual firms: when they expand, they can create opportunities for many workers, while weakness or automation can affect employment broadly.

Rajawat says MSMEs are also losing jobs to automation. This creates a difficult balance. Technology may reshape business operations, but fewer roles can mean weaker job creation. He links employment to entrepreneurship as well, arguing that if entrepreneurship stalls, job creation stalls too. New businesses need access to consumer data held by dominant ecommerce and social media platforms.

The current challenge is therefore not only helping existing MSMEs adapt. India also needs conditions that let new entrepreneurs succeed and workers reskill. The article identifies MSMEs, data laws, entrepreneurship, and reskilling as forces that will shape the next wave of employment.

Investment does not automatically create as many jobs as it once did. Rajawat says the old formula, in which Rs 100 crore of investment generated about 10,000 jobs, no longer holds. One reason is that technology and automation can perform tasks that previously required people. Capital can raise capacity without producing equivalent employment.

The article also points to policy design. Production-linked incentive schemes are tied to capital rather than employment, according to Rajawat. At the same time, AI is affecting white-collar work and MSMEs are losing jobs to automation. This means factories, technology, and investment may support output while creating fewer roles.

Reskilling can help workers move into changing or emerging work, while entrepreneurship can create new businesses and jobs. Rajawat warns that platform consolidation may block entrepreneurs from accessing consumer data. The forward outcome therefore depends on combining investment with reskilling, supportive data laws, and conditions that let new businesses succeed.