Introduction: Why agitoy is addressing this topic

With the rapid advance of generative AI, many companies share a vague concern: can our existing business continue as it is?

Much of the public discussion, however, stays at the surface — which jobs will be replaced, whether work will disappear. The questions that matter to management are different: where is business value actually created, which organizational roles will be restructured, and what kind of people will create competitive advantage? These are often not adequately worked through.

Working alongside both executives and operational teams across advertising, e-commerce, media and technology, agitoy has consistently observed that generative AI is not taking jobs away. It is restructuring the relationship between work and tasks. This article sets out that shift.

Definitions of “work” and “tasks” in this article

First, the terms as used here.

• Tasks
  • Can be proceduralized and standardized
  • Have clear inputs and outputs
  • Can be done by someone else, or by AI
  • Are natural targets for efficiency and automation

    Examples: information gathering, document preparation, implementation, design production, ticket handling
    

• Work
  • Involves purpose, intent and judgment
  • Depends on context and is hard to reproduce
  • Carries responsibility
  • Bundles tasks and turns them into outcomes

    Examples: requirements definition, prioritization, structural design, decision-making, coordination and translation between business and technology

Work gives meaning to a set of tasks and converts them into results. Tasks are only a part of work.

1. What disappears is tasks, not work

Generative AI is best at repetitive, standardized tasks:

• Information gathering and summaries
• Initial research
• Drafting code or copy
• Banner and text variations
• Generating test cases
• First-pass log analysis
• Simple bug investigation

What AI replaces are tasks that no longer need a person — not work itself: judgment, structuring, decision-making. When executives say “we introduced AI, but productivity did not improve as much as expected,” the reason is usually that only tasks were handed to AI, and the work was never redesigned.

2. Human value concentrates in work — the upstream

What generative AI still struggles with is work that requires understanding context, exercising judgment and coordinating people:

• Understanding customers and extracting insight
• Judgment grounded in market and cultural context
• Stakeholder coordination — relationships and organizational dynamics
• Structuring ambiguous requirements
• Deciding priorities
• Taking responsibility for decisions

These are the upstream activities that set the direction of a business, and for now they are hard to replace with AI. The spread of generative AI does not reduce the importance of work. It makes work more visible, and more valuable.

3. Conclusion: not elimination, but a reorganization of work and tasks

The change generative AI brings is not the disappearance of occupations. What is actually happening is a reorganization of how work and tasks are divided.

Roles that depend on tasks (more easily replaced by AI)

Roles where the volume of tasks or the number of items processed is itself the value will be replaced or compressed quickly as generative AI spreads.

E-commerce

Roles limited to ad setup, trafficking and creative swaps; reports produced without interpretation; product registration and page edits done to procedure, without understanding the reasoning or priorities behind them. Setup, aggregation and updates are readily absorbed by AI and automation tools.

Ad tech and media buying

Roles that only configure delivery, adjust bids and swap creatives; that report the numbers but cannot explain why a metric moved or propose an improvement. Where the main value is knowledge of platform specs and tool operation, AI optimization and automated delivery take over.

Media

Roles that only mass-produce articles or fill formats, with no involvement in editorial policy or context, where producing the piece is itself the goal. If no one can say why a topic is being covered, article generation and outlining sit squarely in AI's strongest territory.

Development and SES

Roles closed to ticket processing and to-spec implementation, working without understanding the background or purpose of the requirements, and uninvolved in why a spec exists or how priorities are set. Implementation-only roles are heavily exposed to AI completion and code generation.

What these have in common

They can explain what is being done, but not why, what was not chosen, or how the decision was made. The moment AI takes over the task, the value disappears from view.

Roles that carry work (increasing in value)

• Can articulate purpose and structure
• Can handle requirements definition and specification
• Can read context and coordinate across stakeholders
• Can use AI to raise productivity substantially
• Can make proposals grounded in business value

What matters to a company is not the number of people doing tasks, but the density of people who can do work.

4. The value of work is hard to see

Work — requirements definition, structuring, coordination, judgment, translation — rarely shows up as a tangible deliverable, and is easily misunderstood. Going forward, organizations will be built around people who can explain what work they are responsible for, and organizations that evaluate work and make decisions on that basis will be stronger.

Work whose value is not explained is treated as if it does not exist. This is one of the decisive dividing lines of the AI era.

5. Three perspectives for raising value in the AI era (for management and practitioners alike)

1) Start from why (the purpose of the work), not what (the task)

People who can explain intent are the ones who move decisions forward.

2) Design the division of roles between people and AI explicitly

AI handles research, comparison, drafting and routine tasks. People handle judgment, explanation, decisions and coordination. This design has a large effect on organizational productivity.

3) Articulate expertise as work

Requirements definition, structuring and translation are critical management assets in the AI era. In technology and SES in particular, the ability to move from ambiguity to articulation to implementation determines competitiveness.

A message from agitoy to executive leadership

Generative AI is not a threat that destroys existing businesses. It is an opportunity to redefine where business value is created.

agitoy works alongside executives and operational teams on clarifying business structure in the AI era, redesigning the relationship between work and tasks within the organization, strengthening upstream processes such as requirements definition and structuring, and connecting marketing with technology.

As the value of tasks falls quickly, competitiveness will be decided by the organization's ability to carry work — giving meaning, exercising judgment, building structure. We want to shape the next form of growth together with the leaders facing this change.