Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Jeremy Coleman, Vice President, Legal Research and Development, Leah
Chances are, you’ve already used AI in both your personal and professional life. No longer an elusive science fiction idea, most have accepted that AI is here to stay, and it’s prudent to learn how to best leverage it. As we move into the new year, the goal marks have shifted from proving AI’s potential to effectively embedding it into how work actually gets done across businesses.
Organizations are increasingly integrating AI directly into their workflows, decision-making processes, and overall business coordination. The key factor for success will be execution: how effectively organizations redesign their work processes, skills, and align incentives to support the adoption of AI.
Moving Beyond AI Pilots
Early AI efforts in most organizations focused on experimentation. Businesses tested assistants and automated specific tasks or isolated use cases. By 2026, that experimentation phase will largely be over, especially for larger enterprises that prioritized investing in AI on the ground floor.
Organizations that successfully move from pilots to enterprise-wide deployment will focus on industrialization. This means connecting multiple systems together across functions, unifying fragmented AI use, and integrating AI into more end-to-end processes. Over time, highly advanced AI agents will begin coordinating tasks across all departments. Optimization becomes the priority as adoption becomes more widespread.
Redesigning Knowledge Work at Scale
As AI continues to mature, it is poised to reshape the structure and economics of knowledge work itself. Rather than simply automating routine tasks, AI enables organizations to systematize work at higher levels of complexity, allowing expertise that was once delivered as bespoke output to be designed into repeatable, scalable systems. This shift allows professionals to spend less time on execution and more time on judgment, oversight, and continuous improvement, while AI supports consistency and execution.
This impact will be felt across sectors such as legal, consulting, accounting, health care, and financial services, fields where work has traditionally been framed as artisanal and expertise-driven. As AI improves its ability to handle variability and edge cases, organizations can increasingly map, monitor, and refine complex services that were previously too inconsistent to scale.
In legal, for example, teams will collaborate more closely with product, data, and technology functions to translate expert judgment into structured workflows. Similar dynamics will emerge across other professional domains as subject-matter experts work alongside technologists to embed expertise into systems, adopting operating models long used in physical goods industries.
Skills for the Next Phase of AI
As knowledge work shifts with wider spread adoption of AI, success will hinge on the ability to design, supervise, and continuously improve AI-enabled workflows. By 2026, three capabilities will be critical in determining who gets hired and how talent is developed:
- AI literacy will extend far beyond basic prompting. The most valuable professionals will be those who can design, supervise, and continuously improve AI-enabled workflows end to end. This requires understanding how to specify tasks for AI, define scope and escalation points, and integrate multiple tools into cohesive systems of work. The real value will come from holistic oversight and orchestration, not from issuing isolated commands. This means translating domain expertise into structured processes that AI can execute, monitor, and refine as part of broader enterprise workflows.
- Systems thinking will become increasingly critical as AI automates linear, rules-based, and document-heavy work across the enterprise. Professionals will need to understand not just how individual tasks operate, but how rules, processes, and decisions interact with markets, technology platforms, incentives, and human behavior. In legal and other advisory functions, this means moving beyond isolated interpretations or outputs to understanding how decisions ripple across the broader business system. A systems-oriented perspective helps organizations anticipate unintended consequences, design more resilient workflows, and apply judgment, context, and communication skills where machines increasingly handle procedural execution.
- Practical experience will become one of the most valued traits in an AI-enabled enterprise. Organizations will increasingly seek professionals who have worked inside businesses and understand operational realities such as managing budgets, navigating delivery constraints, implementing programs, and responding to customer and market pressures. This kind of firsthand experience brings practical context that cannot be gained through academic study alone. When designing and supervising AI-enabled workflows, that context helps teams anticipate edge cases, trade-offs, and execution challenges that AI cannot infer on its own. In legal and advisory roles, operational experience enables guidance that reflects how decisions play out in real business environments, rather than in theory, and allows professionals to contribute more strategically as AI takes on a greater share of procedural work.
Implications for Talent and Workforce Strategy
Organizations are already shifting how they hire and develop talent. In the legal field, for example, we are seeing a noticeable decrease in the recruitment of graduates straight from university. With this, the average age of new recruits is rising, as firms prioritize candidates with prior business experience. This expectation in law aligns with the wider market challenge across industries, and it is becoming harder for first-time job seekers to secure a foothold.
In turn, universities will be under growing pressure to produce well-rounded graduates with relevant training, so their curricula will need to be reviewed and updated more frequently to accommodate and keep pace with the speed of business. Virtual training with realistic scenarios will become essential for graduates to acquire the commercial context that they cannot get from classroom study, and bringing technology into the classroom will be key.
Employers, in parallel, will face mounting pressure to close skills gaps internally. This will require far more deliberate investment in fast, on-demand training for existing employees so that capability can be refreshed continuously, not episodically. For organizations that continue to run graduate schemes, the burden will be even heavier: traditional “learning by osmosis” models will no longer suffice. Employers will need to deploy sophisticated virtual training environments that simulate real client, commercial, and operational scenarios, allowing junior staff to develop judgement and confidence early on, rather than waiting years for exposure that may never materialize organically.
Forward-thinking employers will begin to expand their apprenticeship schemes, and some may even prioritize this as the primary talent pipeline. This shift will place additional pressure on universities to differentiate their offering.
The Next Era of Enterprise AI
By 2026, leading organizations will embed AI into the very fabric of how work is designed and managed. As pilots give way to full-fledged systems, leaders will need to ask themselves whether their organizations and workers are prepared to move beyond experimentation and redesign work for a new era of AI-enabled enterprise.
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