Mikko S. Niemelä

Tag: technology

  • 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.

  • Synthetic Intelligence and the Scaling Challenge

    As business leaders increasingly embrace AI solutions, there’s a critical reality we must understand about scaling these systems. Unlike traditional computing where doubling resources might double performance, synthetic intelligence follows a more challenging path: intelligence scales logarithmically with compute power.

    What does this mean in practical terms? Each additional investment in computing resources yields progressively smaller returns in capability. This isn’t just a theoretical concern—empirical studies have confirmed that modest increases in AI performance often demand approximately ten times more compute resources. Even small improvements in accuracy or capability can require vast new investments in hardware, electricity, and cooling infrastructure, explaining why training state-of-the-art models carries such significant financial and operational costs.

    This scaling challenge becomes particularly pronounced when we consider autonomous AI agents. These systems don’t just solve isolated problems—they spawn new tasks and trigger additional software interactions at each step. As these agents proliferate throughout an organization, computational demands expand dramatically, often far beyond initial forecasts. The result is what I call the “compute gap”—a widening divide between desired AI capabilities and practical resource availability.

    Organizations aren’t helpless against this reality, however. Smart deployment strategies can help bridge this gap. For instance, deploying multiple specialized models instead of relying on a single massive one allows for more efficient use of resources. When we partition tasks cleverly and coordinate specialized systems, we can stretch existing hardware investments considerably further.

    Interestingly, AI itself offers one path forward through this challenge. When applied to semiconductor design, AI accelerates advances in chip technology, which in turn enables more powerful AI systems. This recursive improvement loop pushes both hardware and software innovation forward at a rapid pace, with each generation of chips becoming more adept at running large models while enabling the next wave of AI tools to refine chip design even further.

    The shift toward multi-agent systems represents another promising direction. Moving from monolithic models to distributed teams of AI agents fundamentally changes how compute scales. Parallel tasks can be tackled simultaneously, improving total throughput and resilience. By specializing, individual agents can operate more efficiently than a single general-purpose system, especially when orchestrated effectively.

    It’s worth distinguishing between training compute and test-time compute in your AI strategy. Training typically consumes enormous bursts of computational resources, often with diminishing returns for final accuracy. However, inference—or test-time compute—can become the larger expense when AI is deployed widely across millions of interactions. Optimizing inference through specialized hardware and software is essential for managing costs and ensuring consistent performance at scale.

    Some leaders assume cloud computing eliminates these scaling constraints entirely. While provisioning more virtual machines does simplify deployment, it doesn’t erase the underlying physical resource limits. Hardware availability, data center footprints, and energy constraints still govern how far AI can practically expand. The cloud offers flexibility but doesn’t change the fundamental trade-offs dictated by logarithmic scaling.

    Energy consumption emerges as perhaps the most critical constraint in this equation. Exponentially expanding agent deployments require commensurately more power, putting real pressure on data centers and electrical grids. This isn’t just an environmental concern—it’s an economic and logistical challenge that directly impacts the bottom line. Solutions that reduce the energy-to-compute ratio become increasingly vital for sustaining AI growth.

    Market dynamics further complicate this picture. When organizations see high returns on AI investments, they naturally allocate more capital for bigger and faster models. This feedback loop is self-reinforcing: better results justify scaling up, which drives further investment. As competition intensifies, companies continue fueling compute-intensive research, pushing boundaries while simultaneously increasing demand for already-constrained resources.

    Perhaps the most overlooked aspect of the scaling challenge lies in data transfer. In multi-agent or distributed environments, moving data among nodes often becomes the main source of latency. If networks fail to keep pace with processing speeds, models remain underutilized while waiting for information. Efficient data movement—supported by investments in high-bandwidth, low-latency infrastructure—will be essential for keeping synthetic intelligence systems fully operational at scale.

    Understanding these scaling dynamics isn’t just academic—it’s crucial for making informed strategic decisions about AI adoption and deployment. As we continue integrating these technologies into our organizations, recognizing the logarithmic nature of AI improvement helps set realistic expectations and allocate resources wisely. The future belongs not necessarily to those with the most computing power, but to those who can orchestrate it most efficiently.