How AI is reshaping the economics of knowledge work — and what mid-size organizations should do about it now.
We are in the middle of a new industrial revolution for knowledge work. The implications extend beyond technology teams, reshaping productivity models, workforce structures, and economic demand across industries. Published by NovoCircle
This shift is unfolding far faster than expected. Current technical capabilities indicate that the majority of information tasks can now be automated. Intelligent Automation is poised to increase overall information worker productivity in the range of 8–10× by 2028.
Organizations and individuals must act decisively. The choices made in the next 18–24 months — whether to scale, restructure, or reinvest — will determine competitiveness in an AI-driven economy.
According to a September 2025 report from McKinsey, “45% of information work is automatable with the technology available today.” This translates to a projected impact on 12 million jobs by 2030. However, Microsoft’s announcements at Ignite suggest these estimates may significantly understate both the scale and speed of change underway.
New tools and capabilities introduced by Microsoft indicate that as much as 80% of the tasks currently performed by information workers can be automated using existing technologies. The anticipated timeline for widespread automation is likely to shorten dramatically, with the transition unfolding over the next 18 to 24 months.
At Microsoft Ignite, the company introduced a series of incremental changes that, when considered together, have the potential to radically transform the roles of information workers:
Over the next two years, the IT (Information Technology) and information workforce will experience dramatic changes as companies adopt deep automation and artificial intelligence to drive productivity. By 2028, aggregate productivity among information workers is expected to increase by eight to ten times. Tasks that currently require twenty people will be accomplished by just two individuals.
Jobs once considered stable — human resources, finance, IT helpdesk, customer service — are now at risk and may become obsolete within the next three to five years. The 20% of workers whose roles evolve rather than disappear will see their daily tasks become highly augmented by automation rather than eliminated outright.
To successfully navigate this shift, companies must proactively adapt. There are two principal strategies:
Process-Based Transformation (Top-Down): Companies identify their key business functions and processes that can be re-engineered and automated through AI and modern tools. This approach is recommended by Enterprise Architects, Consultants, and IT Leaders. It carries significant risk — the process is costly, and failure can result in lost time and financial resources.
Individual Productivity-Based Transformation (Bottom-Up): Companies encourage every employee to identify at least one task that could be automated to save approximately four hours per week. This approach yields a cumulative 10% increase in capacity without causing workforce disruption, with low investment and risk.
Three strategies for harvesting productivity gains:
IT workers must evaluate their roles to determine whether they are genuinely IT professionals or information workers operating within the IT department. Individuals serving as developers, designers, engineers, or certain types of architects will play a pivotal role in leading the transformation. The most effective way for IT workers to embrace the transformation is by focusing on the preparation and management of organizational data — cleaning up complex data foundations, identifying existing data, pinpointing its location, and integrating it for optimal use.
Information workers who choose to embrace change and ride the wave of intelligent automation must act rapidly. Acquiring skills in building and operating AI agents to automate tasks is crucial. For those who choose not to upskill, organizational initiatives or unemployment may compel them to do so anyway. By anticipating these possibilities, individuals can take control of the timing and options for their next career move.
The information economy — where organizations and individuals create value primarily by acquiring, processing, and communicating information — is undergoing a fundamental restructuring. As AI and intelligent automation make 80% of information tasks automatable, the economic basis of information work is changing. This is not a prediction. It is a description of what is happening now, at a pace faster than previously anticipated.
The timeline has accelerated significantly. Where earlier projections suggested a gradual transition over the next decade, current technical capabilities indicate the majority of information tasks can now be automated. The most credible estimates put large-scale workforce disruption in the 18–24 month range. The choices organizations and individuals make in that window will largely determine their competitive position.
Start with clarity on what can be automated in your specific environment — not in general, but in your actual processes. The NovoCircle Define engagement provides a fixed-scope, fixed-price assessment of your automation opportunity: which processes have the strongest ROI (Return on Investment), what tools are appropriate, and what the realistic sequencing looks like. This is the foundation every serious automation program needs before investing at scale.
NovoCircle works at the intersection of intelligent automation, enterprise architecture, and AI strategy for mid-size organizations. We assess what is genuinely viable in your environment, design and build the right solution, and develop the internal capability to sustain it. Our vendor-agnostic position means our recommendations reflect your environment and goals — not which vendor pays us the most.
Every NovoCircle engagement begins with a fixed-scope assessment — locating where your practice sits, identifying the constraint on the next transition, and returning a prioritized set of recommendations. Fixed scope. Fixed price. No agenda beyond getting it right.
The central conclusions from this research, presented for practitioners and technology leaders.
The majority of automation projects that do not deliver expected ROI failed not in implementation but in the scoping and assessment phase — organizations built before they understood.
Organizations that conducted independent platform assessments before procurement achieved meaningfully higher implementation success rates than those led through vendor evaluation processes.
Over half of surveyed deployments reported meaningful underutilization at twelve months post-deployment. In nearly all cases, structured training had not been scoped as part of the project.
Organizations that prioritized foundational data and integration work before building automation or analytics layers reported significantly better sustained outcomes.
The complete paper includes methodology notes, detailed findings, practitioner recommendations, and a framework for applying these insights to your own technology investment decisions. No registration required.
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