AB's Reflections

Tag: futureofwork

  • 20 years of blogging

    20 years of blogging

    The blog turned 20 in Jan, and as I was looking back at the older posts, I found this draft titled “The farmer and the demon” from a dozen years back that seems quite apt for the current wave of agentic AI and the job market.

    That story about the work hungry demon taken from a book:

    There once was a farmer who was granted a boon of being able to summon a hard working demon to do his bidding. One day he got tired of doing his work and chanted the magic words and lo and behold, there stood a demon in front of him.
    This is a story from Punjab, a state popular for its fertile land and prosperous farmers. This story shows that how prosperity can make people greedy and get them into serious trouble. It also shows that sometimes, when a man is in trouble, it takes a woman to rescue him. The woman helps him out not by a show of strength but by using her head.

    Once upon a time there lived a man called Sher Dil. He was a rich man with acres of farmland and so many heads of cattle he did not know what to do with them. So Sher Dil had engaged a hundred servants to work for him. But Sher Dil was at heart a miser. He hated to spend money feeding the servants and paying their wages.

    Sher Dil`s wife Gulabo was his exact opposite in temperament. She was patient and good natured and very wise. Whenever Sher Dil grumbled about expenses, she rebuked him saying that the servants worked hard for thgem throughout the year. Hence there was nothing wrong in paying them their deserved wages. On the contrary Sher Dil should be grateful for all that the great Lord has given them. But her words of wisdom had no effect whatsoever on her ill tempered, miserly husband.

    One day Sher Dil sat down to do his accounts and sure enough, he soon started grumbling about the expenses that the servants incurred. He said that he will employ only one person for all the work. However his wife protested. Sher Dil had the wicked idea of employing a demon instead of several human beings for the work. The more he thought over this idea, the more he liked it. And Sher Dil made up his mind to visit a sadhu who granted boons to people and put an end to their troubles.

    It took Sher Dil two days to reach the sadhu`s hut, situated in the middle of a forest. The sadhu sat deep in meditation. At long last he opened his eyes and asked Sher Dil what he had come for. Sher Dil bowed his head and expressed his desire. The sadhu granted his wish. He picked up a stick, closed his eyes and murmured something. Then he drove the stick into the ground. Next minute a thin stream of smoke began to emerge from the top end of the stick. Slowly the stream increased in volume till it became a small cloud, rumbling like thunder. And out of the cloud stepped a huge, blue-coloured demon as tall as a pipal tree. He looked so fierce that Sher Dil felt like running away. But somehow he stood his ground. The demon roared and asked why he has been summoned. When the sadhu explained the cause the demon roared again and enquired whether Sher Dil knew the terms of his employment. He must be kept busy throughout the day and night. The moment he finds no work he will eat up Sher Dil.

    Sher Dil burst out laughing and said there is so much of work that he won`t have a minute to spare. Then he instructed the demon to take him home. The words were hardly out of his mouth when the demon snatched him up, tucked him behind one ear and went flying across the sky. Afraid of rolling off, Sher Dil clutched the demon`s ear with both hands and peeped over it. The earth appeared like a tiny multicoloured ball. Sher Dil closed his eyes for fear. Then suddenly the demon began to descend like an egg thrown up in the air. He touched down at Sher Dil`s own doorstep. Sher Dil scrambled off and ran to see his wife. Bursting with excitement he showed his wife what he had found.

    Before his wife could say anything in reply, Sher Dil had sacked all his servants and sent off the demon to plough the land. Then he yawned, stretched himself and lay down on his bed for a nice little nap. Sher Dil was in the middle of a rosy dream when someone grabbed him by the shoulder and shook him hard. As he opened his eyes he saw the demon towering over him. He was asking for more work. Sher Dil could not believe his ears. So many acres of land, ploughed up in less than a quarter of an hour! He climbed to his roof and looked around to be sure the land had been ploughed as far as the eye could see. Then he ordered the demon get some manure and mix it with the soil, sow the seed and water the fields.

    The demon disappeared and Sher Dil went back to sleep again. But he had barely snored a couple of times when someone shook him again, harder than before and again asked for work. By this time Sher Dil was quite irritated and said him to raise a fence all around his land. The demon left but Sher Dil did not go back to sleep again. From his kitchen window he watched the demon fetch a pile of wood and some tools. His hands flew as one stick after another was cut and shaped and driven into the ground. Before Sher Dil had finished his evening cup of tea the demon was back again.

    Sher Dil sent him off to empty out their pond and fill it with fresh water from the river. Night was falling when the demon returned to say that the work had been done. In despair Sher Dil asked him to rest but the demon laughed, a horrible, croaky laugh saying that he never rested. Sher Dil picked up courage enough to say that he wanted to rest till the next morning. In the meantime he asked the demon to guard the fields so no wild animal can get in and trample down the seed.

    The demon went away but Sher Dil could not sleep a wink. Just before dawn he woke up his wife and in a tearful state shared his problem. Gulabo comforted him and asked him to leave everything at her discretion. As soon as the sun peeped over the horizon, the demon began to hammer at the door. `Work,` he yelled. Gulabo opened the door a crack and peeped out. Then she came out of the door. She asked the demon to straighten the tail of a stray dog that often came there looking for food.

    The demon bent down, caught the dog`s tail by the tip and gave it a good jerk. The tail straightened out. But the moment he let go, it curled up again. The demon caught the tail a second time and stroked it for a good five minutes. But the moment he let go, the tail curled up again. With a frown on his face the demon caught the tail for the third time, rolled it up the other way and held it in position for some time. As soon as he let the tail go it curled up again, back to its original shape.

    All this time the dog had been watching patiently as the demon fiddled around with its tail. But suddenly it lost its patience. With a bound it was up and barking at the demon. Round and round they went, the demon dodging and ducking and diving at the tail and the dog snapping and snarling and threatening to bite. This went on for hours. In the end the demon got fed up of trying to catch the dog. He admitted to himself that he cannot do the assigned work. But he was thoroughly ashamed of himself at the same time. He had no courage to face the family. So off he went to hide in the forest that he came from.

    With the demon gone, Sher Dil and his wife began to live in peace once again. Sher Dil took back all his servants and gave them a solid feed to start with. And never, never again did he complain about the amount that he had to spend on their food and wages.

  • From Clicks to Conversations: How AI Agents Are Revolutionizing Business

    From Clicks to Conversations: How AI Agents Are Revolutionizing Business

    For the last decade, businesses have invested heavily in “Digital Transformation,” building powerful digital tools and processes to modernize their operations. While this era brought significant progress, it also created a persistent challenge. The tools we built—from CRMs to ERPs—were largely dependent on structured data: the neat, organized numbers and categories found in a spreadsheet or database. Computers excel at processing this kind of information.

    The problem is that the most valuable business intelligence isn’t structured. The context behind a business plan locked in a 100-slide presentation, the nuance of a customer relationship captured in a rep’s notes, or the true objective of a strategy discussed in a meeting—this is all unstructured data. This divide has created a major hurdle for business efficiency, as great ideas often get lost when people try to translate them into the rigid, structured systems that computers understand.

    The Old Way: The Limits of Traditional Digital Tools

    The first wave of digital tools, from customer relationship management (CRM) software to accounting platforms, were designed for humans to operate. Their critical limitation was their reliance on structured data, which forced people to act as human translators. A brilliant, nuanced strategy conceived in conversations and documents had to be manually broken down and entered into rigid forms and fields.

    This created a significant “gap between business strategy and execution,” where high-level vision was lost during implementation. The result was heavy “change management overheads,” not just because teams needed training on new software, but because of the cognitive friction involved. People are used to working with the unstructured information in their heads; these tools forced them to constantly translate their natural way of thinking into structured processes the software could understand.

    Information TypeBusiness Example
    StructuredEntries in a CRM database, financial data in an accounting platform, inventory numbers in an ERP system.
    UnstructuredA 100-slide brand plan document, a sales rep’s recorded notes describing a doctor they just met, emails discussing a new brand strategy.

    This reliance on structured systems meant that the tools, while digital, couldn’t fully grasp the human context of the work they were supposed to support. A new approach was needed—one that could understand information more like a person does.

    A Smarter Way: Introducing AI Agents

    Welcome to the era of “AI Transformation.” At the heart of this new wave are AI Agents: specialized digital team members that can augment a human workforce. Think of them as a dedicated marketing agent, a sales agent, or a data analyst agent, each designed to perform specific business functions.

    The single most important capability of AI agents is their ability to work with both structured and unstructured information. You can communicate a plan to an agent by typing a message, speaking, or providing a document—just as you would with a human colleague. This fundamental shift from clicking buttons to holding conversations unlocks three profound benefits:

    • Bridging the Strategy-to-Execution Gap: AI agents can understand the nuance of an unstructured plan—the “why” behind the “what”—and help execute it without critical information getting lost in translation.
    • Handling All Information Seamlessly: They can process natural language from documents, presentations, or conversations and transform it into the actionable, structured data that existing digital tools need to function.
    • Reducing Change Management: Because agents understand human language, the need for extensive training on rigid software interfaces is significantly reduced. People can work more naturally, supervising the agents as they handle the tedious, structured tasks.

    To see how this works in practice, let’s walk through how a team of AI agents can help plan and execute a marketing campaign from start to finish.

    AI Agents in Action: Launching a Marketing Campaign

    This step-by-step walkthrough shows how AI agents can take a high-level marketing plan from a simple idea to a fully executed campaign, seamlessly connecting unstructured strategy with structured execution.

    1. The Starting Point: The Marketing Brief – The process begins when a brand manager provides a marketing brief. This brief is pure unstructured information—it could be a presentation, a document, or even the transcript of a planning conversation. It contains the high-level goals and vision for the campaign.
    2. Deconstructing the Brief: The Brand Manager Agent – A specialized “Brand Manager” agent analyzes the unstructured brief and extracts the core business context elements. It identifies key information such as:
      • Business objectives
      • Target audience definitions
      • Key messages
      • Brands in focus
      • Timelines and milestones
    3. The agent then organizes this information into structured, machine-readable “context blocks,” creating a clear, logical foundation that other systems and agents can use.
    4. Understanding the Customer: The Digital Sales Agent – Next, a “Digital Sales” agent contributes by performing customer profiling. It can take unstructured, natural language descriptions of customers (for instance, from a sales rep’s recorded notes) and map them to formal, structured customer segments and personas. This builds a richer, more accurate customer profile than a simple survey could provide.
    5. Creating the Content: The Content Writer Agent – Using the structured business context from the Brand Manager agent, a “Content Writer” agent assembles personalized content. It can reuse and repurpose existing content from a library of approved modules, accelerating content creation while ensuring brand compliance.
    6. Executing the Plan: The Next Best Action (NBA) Engine – Finally, the system brings everything together to recommend the “Next Best Action.” This engine synthesizes the campaign’s business context, the customer’s profile, the available content, and their recent engagement history to suggest the perfect next step for each customer. It recommends precisely what content to send and which channel to use, turning high-level strategy into a concrete, personalized action.

    This orchestrated workflow makes the entire process smoother, faster, and far more intelligent. It creates a virtuous cycle, where the system learns from every interaction to continuously improve the overall strategy and execution over time.

    The Future of Work is Collaborative

    The rise of AI agents marks a fundamental shift in how we work with technology. We are moving from a world where humans must adapt to operate digital tools to one where humans supervise intelligent AI agents that use those tools on our behalf.

    This new wave of AI transformation is not about replacing people, but about augmenting their human workforce without adding headcount. By handling the translation between unstructured human ideas and structured digital processes, AI agents help businesses reduce friction, cut down on turnaround times, and finally bridge the long-standing gap between their biggest strategies and their real-world execution.

  • India’s Race Between Demography and AI

    India’s Race Between Demography and AI

    Artificial Intelligence is often framed as a threat to jobs—a disruptive force poised to replace human labour at an unprecedented scale. From call centres to accounting firms, from routine coding to legal research, Generative AI and automation tools are already demonstrating capabilities that once seemed untouchable. The fear of widespread job loss is not unfounded. McKinsey, among others, estimates that nearly a quarter of global work activities could be automated by the early 2030s.

    Yet, there is another equally significant demographic trend reshaping the labour market—the aging of populations. In countries such as Japan, South Korea, Germany, and even China, the working-age population is shrinking. This is not because jobs are disappearing, but because people are. Fertility rates have fallen below replacement levels, and the proportion of elderly citizens is rising sharply. These nations face a paradox: they need more workers but have fewer people available to fill roles.

    AI and Aging: Complementary Forces in Developed Countries

    This is where AI and aging unexpectedly complement each other. In economies that are already greying, AI is less a destroyer of jobs and more a replacement for the labour that no longer exists. Japan, for example, has pioneered the use of robotics and AI-driven systems not to replace young workers, but to stand in for absent ones—care robots for the elderly, AI-assisted diagnostics for hospitals short on doctors, and factory automation for industries facing chronic staff shortages.

    In such societies, the fear of AI taking away jobs is muted by the demographic reality that many jobs would otherwise remain unfilled. AI is effectively stepping into the gap created by demographic decline. For them, the challenge is not managing unemployment but managing the technological transition in a way that sustains productivity and care standards as their populations age.

    India’s Young Advantage—and the Ticking Clock

    India, however, tells a different story. The country’s demographic structure is still overwhelmingly young. Nearly two-thirds of Indians are in the working-age bracket, and the median age is around 28—more than a decade younger than China or the U.S. This “demographic dividend” has been hailed as India’s biggest economic advantage for the next 10–15 years. But this window is finite.

    Demographers estimate that by the mid-2030s, India’s working-age population will peak. After that, the proportion of elderly citizens will start rising sharply. By 2046, the elderly are projected to outnumber children under 14. In other words, India’s advantage will begin to fade just as many advanced economies have already entered the post-dividend phase. If India cannot create enough productive jobs during this critical decade, its youth bulge may turn into a liability.

    AI’s Adoption Curve

    The question is: will AI go mainstream while India’s workforce is still young? Current projections suggest that large-scale AI adoption is still 5–15 years away. Today’s Generative AI tools, while impressive, remain in an experimental phase. They lack reliability, governance frameworks, and cost efficiency at scale. Gartner’s hype cycle places most AI technologies in the “Trough of Disillusionment,” meaning that widespread productivity gains will take years to materialize.

    If this trajectory holds, AI’s mainstream integration across sectors like healthcare, education, law, and public administration may not happen until the 2030s—roughly the same time that India’s demographic dividend starts to decline. This sets up an intriguing scenario where India’s labour market transition and AI’s maturity could synchronize.

    Possible Scenarios for India

    1. The Collision Scenario:

    If AI adoption accelerates too quickly, India’s youthful workforce may find itself competing against machines for jobs before the country has built a strong industrial and service base. Sectors such as BPO, customer service, and low-skill IT roles—once the backbone of India’s outsourcing economy—could see rapid automation. Without massive reskilling efforts, unemployment among young Indians could spike even as the global economy demands fewer entry-level jobs.

    2. The Missed Opportunity Scenario:

    Alternatively, if AI adoption lags too far behind—say, beyond 2040—India could enter its aging phase without having reaped the productivity gains AI promises. By then, the country would face the dual pressures of a shrinking workforce and a delayed technological transition. This would mirror some of the struggles seen in late-industrializing economies that missed the manufacturing wave.

    3. The Synchronization Scenario:

    The most optimistic possibility is that AI and India’s demographic transition align productively. Over the next decade, India could use its young workforce to build, train, and scale AI systems, preparing the ground for when labour shortages begin. By the time the aging curve hits in the 2035–2040 period, AI could step in not as a threat, but as a productivity amplifier—automating routine tasks while humans focus on complex, creative, or empathetic roles.

    This requires a proactive strategy: early investment in AI literacy, creation of AI-enabled jobs (rather than job replacement), and building a global service economy where Indians are not just users of AI, but architects of AI solutions.

    The Decisive Decade

    India’s story in the 2030s will be defined by the intersection of two megatrends: a maturing workforce and a maturing technology. Whether this convergence leads to disruption or opportunity depends on choices made now—in education, infrastructure, governance, and industry adoption. The challenge is to ensure that when AI becomes mainstream, India’s workforce is not left behind but is ready to ride the wave. The 2020s are not just a decade of demographic advantage—they are the runway for an AI-driven, post-dividend future.

  • The Economic Reality and the Optimistic Future of Agentic Coding

    The Economic Reality and the Optimistic Future of Agentic Coding

    After a couple of months deep in the trenches of vibe coding with AI agents, I’ve learned this much: scaling from a fun, magical PoC to an enterprise-grade MVP is a completely different game.

    Why Scaling Remains Hard—And Costly

    Getting a prototype out the door? No problem.

    But taking it to something robust, secure, and maintainable? Here’s where today’s AI tools reveal their limits:

    • Maintenance becomes a slog. Once you start patching AI-generated code, hidden dependencies and context loss pile up. Keeping everything working as requirements change feels like chasing gremlins through a maze.
    • Context loss multiplies with scale. As your codebase grows, so do the risks of agents forgetting crucial design choices or breaking things when asked to “improve” features.

    And then there’s the other elephant in the room: costs.

    • The cost scaling isn’t marginal—not like the old days of cloud or Web 2.0. Powerful models chew through tokens and API credits at a rate that surprises even seasoned devs.
    • That $20/month Cursor plan with unlimited auto mode? For hobby projects, it’s a steal. For real business needs, I can see why some queries rack up millions of tokens and would quickly outgrow even the $200 ultra plan.
    • This is why we’re seeing big tech layoffs and restructuring: AI-driven productivity gains aren’t evenly distributed, and the cost curve for the biggest players keeps climbing.

    What the Data Tells Us

    That research paper—Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity—had a surprising conclusion:

    Not only did experienced developers see no time savings on real-world coding tasks with AI, but costs increased as they spent more time reviewing, correcting, and adapting agent output.

    The lesson:

    AI shifts where the work happens—it doesn’t always reduce it. For now, scaling with agents is only as good as your processes for context, review, and cost control.

    Why I Remain Optimistic

    Despite the challenges, I’m genuinely excited for what’s coming next.

    • The platforms and models are evolving at warp speed. Many of the headaches I face today—context loss, doc gaps, cost blind spots—will get solved just as software engineering best practices eventually became codified in our tools and frameworks.
    • Agentic coding will find its place. It might not fully automate developer roles, but it will reshape teams: more focus on high-leverage decisions, design, and creative problem-solving, less on boilerplate and “busy work.”

    And if you care about the craft, the opportunity is real:

    • Devs who learn to manage, review, and direct agents will be in demand.
    • Organizations that figure out how to blend agentic workflows with human expertise and robust process will win big.

    Open Questions for the Future

    • Will AI agentic coding mean smaller, nimbler teams—or simply more ambitious projects for the same headcount?
    • How will the developer role evolve when so much code is “synthesized,” not hand-crafted?
    • What new best practices, cost controls, and team rituals will we invent as agentic coding matures?

    Final thought:

    The future won’t be a return to “pure code” or a total AI handoff. It’ll be a blend—one that rewards curiosity, resilience, and the willingness to keep learning.

    Where do you see your work—and your team—in this new landscape?

  • Shadow AI, Friction Fatigue & the Flexibility Gap: 5 Lessons from the Ivanti Tech at Work 2025 Report

    Shadow AI, Friction Fatigue & the Flexibility Gap: 5 Lessons from the Ivanti Tech at Work 2025 Report

    The Ivanti Tech at Work 2025 report isn’t just a workplace tech survey — it’s a mirror to how modern organizations are struggling (and sometimes succeeding) to adapt to the realities of hybrid work, AI adoption, and employee expectations.

    Here are 5 insights that stood out — and why they matter for anyone building teams, tools, or trust in the modern workplace.

    🔗 Read the full report

    1. Shadow AI Is a Trust Problem, Not Just a Tech One

    Nearly 1 in 3 workers admit they use AI tools like ChatGPT in secret at work.

    Why the secrecy?

    According to the report:

    • 36% want a competitive edge
    • 30% fear job cuts or extra scrutiny
    • 30% say there’s no clear AI usage policy
    • 27% don’t want their abilities questioned

    This is more than a governance issue. It’s a cultural signal.
    Employees are turning to AI to be more productive — but doing so under the radar signals a trust deficit and policy vacuum.

    💡 What to do:
    Leaders need to replace silence with structure — with clear, enabling policies that promote responsible AI use, and an environment where value creation matters more than screen time.

    2. The Flexibility Paradox: High Demand, Low Supply

    83% of IT professionals and 73% of office workers value flexibility highly — but only ~25% say they actually have it.

    Even as companies trumpet hybrid work, asynchronous enablement, autonomy, and outcome-based work norms haven’t caught up. The result? Disengagement and frustration.

    💡 What to do:
    Revisit what flexibility really means. It’s not just about where people work — it’s how they work.
    That means:

    • Tools for async collaboration
    • Decision-making frameworks for remote teams
    • Leaders modeling flexible behaviors

    3. Presenteeism Is the New Fatigue

    The report highlights “digital presenteeism”: workers pretending to be active — jiggling mice, logging in early — to appear productive.

    • 48% say they dislike their job but stay
    • 37% admit to showing up without doing meaningful work

    These are signs of unclear expectations and poor workflow design — not disengagement alone.

    💡 What to do:
    Audit for friction, not just lag.
    Look at your workflows, KPIs, and culture. Are people forced to perform busyness instead of real value?

    4. The Digital Experience Gap Is Real

    While flexible work is valued, many workers find it harder to work outside the office. The report notes:

    • 44–49% say collaboration is easier in-office
    • 36–48% say manager access is better in-office
    • 16–26% say apps are easier to use from the office

    💡 What to do:
    Enable remote-first experience, not just policy:

    • Seamless access to tools and systems
    • Integrated collaboration platforms
    • AI-powered support and IT workflows

    5. Redesign for Trust, Not Just Tools

    The big takeaway?

    Workers don’t just need better AI — they need clarity on what’s allowed
    They don’t just need more flexibility — they need workflows that enable it
    They don’t just need faster tools — they need a culture that values trust over control

    Final Thoughts

    The Ivanti Tech at Work 2025 report is a diagnostic — revealing what happens when new tools are bolted onto outdated operating models.

    For leaders, the message is clear:

    We need to evolve not just our tech stack, but our trust stack.

    🔗 Read the full report