Mikko S. Niemelä

Tag: business

  • The Era of Execution: Thriving in the Age of AI-Powered Individual Productivity

    In my recent piece, “The Flow and Pace of Knowledge Work in the AI Era,” I explored how synthetic intelligence is fundamentally transforming work patterns and challenging traditional workflow paradigms. That article sparked numerous questions from readers about practical implementation: “What specific skills should I develop?” “How do I position myself in this new reality?” “What happens to management roles?”

    This piece addresses those questions by examining what I call the “Era of Execution” – where individuals armed with AI can achieve what previously required entire departments. Throughout history, shifts in technology have transformed how we work, but the current transformation is uniquely disruptive in its speed and scope.

    The Shift from Teams to Exponential Individuals

    In the previous decade (2010-2020), businesses operated through larger teams and centralized project management. Progress required coordination across many specialists and approvals through multiple management layers. Productivity improvements were incremental, and hierarchical structures ensured control but often at the cost of speed.

    Today’s reality looks starkly different:

    • Small teams are producing outsized results by harnessing AI. Multiple AI startups have reached $50-200 million in annual revenue with teams of just 10-50 people – accomplishing what once required hundreds or thousands of employees.
    • Individual “full-stack” productivity has emerged, where knowledge workers manage their own AI suite of tools for coding, writing, research, design, and analytics. This effectively turns them into one-person teams with 10-100× output compared to their 2010s counterparts.
    • Hierarchies have flattened because technology now handles much of the routine coordination. Decisions can be made quickly by frontline contributors using real-time data, without waiting for multi-level approvals.

    The question isn’t whether this shift is happening – it’s whether you’re prepared for it.

    The New Skill Paradigm

    Hard Skills: From Specialization to AI-Enhanced Mastery

    In the 2010s, hard skills centered on proficiency in specific domains and tools. Software engineers were valued for expertise in particular programming languages, analysts for mastery of specific BI tools, and so on.

    Now, AI literacy has become a core skill across all knowledge work. Professionals who can effectively use AI assistants dramatically outperform those who cannot. More than half of hiring managers now say they wouldn’t hire a candidate without AI literacy.

    The hard skill profile has broadened – employers seek T-shaped individuals with depth in one area but familiarity with many tools and the capacity to continually learn new technologies. LinkedIn data shows people have a 40% broader skillset on their profiles in 2023 compared to 2018, underscoring how rapidly skill expectations are expanding.

    Soft Skills: From Coordination to Executional Agency

    As AI handles more routine coordination, companies are looking for people who excel at what only humans can do: creative thinking, exercising judgment, and collaborating in less structured, more proactive ways.

    A defining soft skill of this new era is what I call “executional agency” – the capacity to take an objective and run with it independently. With fewer managers in the loop, individuals must define tasks, set priorities, and drive projects to completion on their own.

    In this environment, adaptability is paramount. The modern workplace changes quickly – tools update, priorities pivot, and even whole roles appear or vanish. Where workers in the previous decade might have had relatively stable job descriptions, today’s employees might see their role redefined yearly as new technologies emerge.

    The Diminishing Middle Management Layer

    One of the most profound consequences of this shift is the reduced need for traditional middle management. AI systems are increasingly handling the information processing and coordination that once justified these roles:

    • Routine tasks like scheduling, progress tracking, and performance reporting can be automated by project management AI and OKR tracking tools.
    • Decisions are increasingly made by those closest to the work, guided by data. AI can feed real-time insights to frontline staff who can act immediately, instead of waiting for a manager’s approval.
    • Gartner predicts that by 2026, 20% of large organizations will have used AI to flatten their hierarchy, eliminating at least 50% of current middle management roles.

    This doesn’t mean managers vanish entirely – but their role changes dramatically. The remaining managers focus on high-level strategy, coaching, and exception handling rather than day-to-day coordination. They must interpret analytics and AI outputs to guide their teams, requiring them to become savvy users of AI themselves. Importantly, the “team” they manage may increasingly consist of a pool of AI agents alongside human talent – requiring new skills in orchestrating both human and artificial intelligence toward common goals.

    The Challenge of Skill Obsolescence

    Perhaps the most daunting aspect of the Era of Execution is the accelerating cycle of skill obsolescence – what some experts call “skill inflation.” Much like monetary inflation devalues currency, skill inflation devalues established competencies over time.

    Research by Deloitte finds the “half-life” of a professional skill is now roughly 5 years, and shrinking further in high-tech fields. This means about half of what you learned five years ago might no longer be relevant.

    What’s particularly striking is how dramatically this rate of obsolescence has accelerated. By my estimation, skill inflation was perhaps 5% or less annually from 2000-2010, meaning professionals could comfortably go years without major upskilling. This increased to roughly 5-10% annually from 2010-2020, as digital transformation gained momentum.

    But since 2020, we’ve entered a period of hyperinflation for skills. I believe we saw approximately 50% skill inflation between 2020-2023 as remote work, cloud technologies, and early AI tools reshaped roles. Since 2023, with the emergence of generative AI, we may be approaching 100% annual skill inflation in many knowledge work domains.

    While these figures aren’t formally measured like monetary inflation, they reflect a profound truth: the pace at which skills lose relevance has completely transformed from gradual to exponential. What once decayed over a decade now becomes outdated in a year or less.

    This creates a productivity paradox: employees who become 10× more productive don’t end up doing 1/10th the work; they are given 10× more responsibilities or tougher problems. As AI makes routine deliverables faster to produce, the premium shifts to outcomes requiring human judgment, unique imagination, and complex problem-solving.

    How to Thrive in the Era of Execution

    For those willing to adapt, the Era of Execution presents unprecedented opportunities. Here’s how to position yourself:

    1. Use AI to Create Your Personal Learning Environment

    Develop a disciplined approach to self-education using AI. Set specific learning goals, then leverage AI to create customized curricula, generate practice exercises, and test your understanding through quizzes and simulations tailored to your learning style.

    This democratization of education makes it possible to learn almost anything rapidly – but beware of fluency bias, the false sense of mastery that comes from merely browsing information. Real learning requires structured practice, deliberate application, and rigorous self-testing. The ability to design your own educational pathways with AI will separate those who truly develop new capabilities from those who merely skim the surface.

    2. Become the CEO of Your AI Stack

    Develop proficiency with multiple AI tools in your domain. Learn to orchestrate these tools – knowing which to deploy for a given challenge and how to integrate their outputs.

    An analyst who can use AI for data preparation, analysis, visualization, and reporting will outperform one who only uses traditional tools for each step. Experiment constantly with new AI capabilities to stay ahead of the curve.

    2. Master Self-Management

    With fewer managers overseeing your work, excellence in self-management becomes critical. Develop skills in:

    • Setting and prioritizing your own goals
    • Creating realistic timelines and deadlines
    • Maintaining motivation and focus without external structure
    • Evaluating and iterating on your own work

    The most valuable employees won’t be those waiting for instructions, but those who can drive projects forward independently.

    3. Cultivate Adaptability

    The ability to quickly learn new tools and adjust to changing circumstances is now essential. Allocate regular time for learning – many professionals now set aside several hours each week specifically for upskilling.

    Focus not just on learning specific tools but on meta-learning: understanding how to rapidly acquire new skills when needed. This creates a compound effect where your learning efficiency improves over time.

    4. Prioritize Uniquely Human Skills

    As AI capabilities expand, focus on developing the skills machines struggle with:

    • Creative problem-solving and insight
    • Strategic thinking and decision-making
    • Interpersonal intelligence and emotional awareness
    • Ethical reasoning and judgment

    The professionals who thrive will be those who blend technical fluency with these distinctly human capacities.

    5. Build Your Credibility Through Execution

    In this new era, your worth isn’t determined by your position in a hierarchy but by your ability to execute. Build credibility by consistently delivering results, even without formal authority.

    Document your impact using clear metrics. Can you show that you’ve delivered work that previously required multiple people? Have you automated processes that used to be manual? These concrete examples will separate you from those merely claiming to be adaptable.

    The Future Belongs to Execution

    For university students, startup employees, and seasoned professionals alike, the key is to embrace this new reality: in the Era of Execution, a single talented, AI-augmented person can achieve what once took an army. Organizations are already restructuring around this truth, eliminating roles that don’t create direct value while empowering those who can execute independently.

    The winners in this environment won’t be those with the most impressive titles or largest teams, but those who can harness AI to deliver outstanding results with minimal oversight. By developing executional agency, continuously refreshing your skills, and mastering the art of human-AI collaboration, you’ll not only survive this transition – you’ll thrive in it.

    The Era of Execution is here. The question is: are you ready to execute?

  • The Flow and Pace of Knowledge Work in the AI Era

    Throughout history, major technological revolutions have fundamentally transformed how we work. We’re currently witnessing another such transformation, as synthetic intelligence reshapes knowledge work at its core. This shift isn’t merely about adopting new tools—it requires reimagining our entire workflow paradigms.

    History offers instructive parallels. Early automobiles were called “horseless carriages” because people initially applied horse-and-carriage thinking to this revolutionary technology. It took time to realize that cars demanded entirely new infrastructure, fueling processes, and traffic rules. Similarly, the transition from print to web required completely rethinking content workflows. Organizations that attempted to apply print-based paradigms in digital environments quickly encountered inefficiencies and limitations. The 20th century’s shift from manual craft to factory mass production rendered many artisan processes obsolete, as assembly lines created entirely new ways of organizing work. Each technological leap has demanded a reimagining of workflows, and synthetic intelligence is no exception.

    Consider what happened when we moved from paper to digital communication. Paper-based workflows collapsed under the volume and speed of digital word processing and email. In the paper era, limited throughput was expected—memos were typed, copied, and physically routed, with filing cabinets for storage. Simply digitizing these same steps proved inadequate when word processors massively increased output and email flooded inboxes. A process that functioned perfectly well for a dozen paper memos simply couldn’t manage hundreds of emails daily. Early attempts to treat email like physical mail—reading everything sequentially and archiving meticulously—led to overwhelming information overload.

    Today, we’re witnessing a similar breakdown as organizations try to rely solely on email workflows in an era when AI can generate or process countless documents overnight. This creates massive bottlenecks when the entire chain still depends on slow, sequential human approvals. The mismatch is unmistakable: AI operates at machine speed while humans review at human speed.

    This speed differential presents one of the most significant challenges in human-AI collaboration. Sequential, step-by-step workflows become bottlenecked when an AI generates outputs far more quickly than people can evaluate them. Content moderation offers a clear example—AI can review thousands of posts per minute, but human moderators manage only a fraction of that volume. Similar bottlenecks emerge when writers use AI to generate analyses in seconds, only for humans to spend days reviewing the output. Organizations facing this issue are experimenting with parallelized reviews, random sampling instead of checking everything, and trust metrics that allow some AI outputs to skip manual gates entirely. The central lesson is that simply dropping AI into a traditional linear process typically causes gridlock because humans become the rate-limiting step.

    Unlike mechanical automation that simply replaces physical labor, synthetic intelligence in knowledge work creates a partnership model—an iterative loop of generation, feedback, and refinement. Research describes this as the “missing middle,” where humans excel at leadership and judgment while AI provides speed, data processing, and pattern detection. The workflow becomes collaborative and non-linear: an AI might produce draft output that a human immediately refines, feeding back prompts to improve the AI’s next iteration. This differs markedly from traditional handoff-based processes and requires designing roles, responsibilities, and checkpoints that ensure humans and AI complement each other.

    A profound inversion is happening in content workflows. Traditionally, creating quality drafts was the most time-consuming part of knowledge work. Synthetic intelligence flips this dynamic by making content generation nearly instant, shifting the bottleneck to curation and refinement. Instead of spending most of their time writing, knowledge workers now sift through and polish an overabundance of AI-produced materials. This new paradigm demands stronger editing, selection, and integration skills to identify the best ideas while discarding low-value output. Many companies are adjusting job roles to emphasize creative judgment and brand consistency since the “first draft” is no longer scarce or expensive.

    We’re also witnessing how democratized knowledge erodes traditional hierarchies. Organizations that relied on gatekeepers to control specialized information are under pressure as AI systems give employees direct access to expert-level insights. Instead of climbing a hierarchy or waiting on specialized departments, a junior analyst can query a legal, financial, or technical AI. This flattens structures built on information asymmetry. Decision-making may no longer need to filter through a chain of command if the right answers are immediately available. As a result, some companies are reorganizing around judgment and insight—what humans still do best—rather than around privileged access to data or expertise.

    Despite these shifts, there remains a significant gap in training for human-AI collaboration. Most corporate and educational programs haven’t caught up to the demand for skills focused on prompt engineering, AI output evaluation, and effective collaboration with machine partners. Traditional training still emphasizes individual knowledge acquisition, but new workflows require human workers who can critically assess AI suggestions, guide AI with strategic prompts, and intervene when outputs deviate from organizational standards. Surveys consistently show that professionals feel unprepared for AI-driven workplaces. Without updated training, companies see staff misusing AI or ignoring its recommendations, eroding the potential benefits.

    When AI projects fail, the root cause often isn’t the technology itself but how it’s integrated into existing workflows. So-called AI “failures” typically stem from forcing new technology into outdated processes. If people don’t know how or when to use AI outputs, or if the organization doesn’t adapt quality control steps, mistakes and underperformance are inevitable. Studies of AI project failures in healthcare, HR, and finance repeatedly show the same pattern: teams bolt on AI without revising approval chains, data capture protocols, or accountability structures. Quality problems usually trace back to process misalignment rather than an inherent flaw in the AI. In effective deployments, AI tools and human roles align in a continuous feedback loop.

    The competitive landscape makes adapting to these new workflow paradigms not just beneficial but essential. Companies that master AI-enabled workflows quickly gain a significant efficiency edge. Multiple case studies confirm that early AI adopters see higher productivity and revenue growth, while firms clinging to old processes struggle to keep pace. Just as in previous technological leaps, refusing to adapt is not neutral—it means actively surrendering market share to competitors who harness AI’s speed and scale. Whether in software development, law, consulting, or customer service, evidence shows the gap between adopters and laggards widens over time. Leaders must therefore consider workflow transformation an existential priority.

    As AI handles a growing portion of analytical and generative tasks, the concept of “productive human work” shifts toward creativity, ethical reasoning, empathy, and complex problem-solving. Humans can offload repetitive knowledge tasks to machines and instead focus on higher-order thinking and strategic oversight. Companies are redesigning roles to reward the uniquely human capacities that AI cannot replicate. In practical terms, this often means devoting more time to brainstorming, innovating, and refining AI-driven outputs, rather than producing first drafts or crunching routine data. This redistribution of cognitive load requires a new mindset about how we measure and value human contributions.

    Unlike previous tools that remained relatively static, synthetic intelligence continuously evolves through new model updates and expansions of capability. Workflows must therefore be agile and modular, allowing rapid iteration as AI capabilities improve or shift. Organizations that lock into rigid processes risk suboptimal usage or obsolescence when the technology outpaces them. Adopting an agile approach to workflow design—regularly revisiting roles, checkpoints, and approval chains—proves vital to remaining effective in a world where “today’s AI” can be substantially more powerful next quarter.

    Changing established workflow habits is undeniably challenging. People naturally resist disruption to familiar routines and processes. The shift to AI-enabled work patterns can feel uncomfortable, even threatening, as it demands new skills and mindsets. However, just as previous generations adapted to typewriters, computers, and smartphones, today’s knowledge workers will adapt to AI-augmented workflows. The reward lies in liberation from mundane tasks, enabling us to focus on the truly human elements of work—creativity, judgment, empathy, and strategic thinking.

    The transition won’t be seamless, but those who embrace this evolution will find themselves at the forefront of a new era in knowledge work. The most successful organizations won’t simply deploy AI tools—they’ll reimagine their entire workflow paradigm to harmonize human and machine intelligence, creating systems that exceed the capabilities of either working alone. This is not merely about technology adoption; it’s about rethinking the very nature of productive work in the 21st century.