Elevate Your Organization with Strategic AI Adoption

Any management approach that doesn’t include AI as a central part of the future workforce is of the past. We are rapidly entering a world of AI agents and humanoid robots playing a role at work and home.

A key insight when designing with OT: Specialization and expertise are vanishing as limited resources, due to intelligence as a service. That makes it much easier to elevate an organization! So when you are deciding on new teams requiring new expert knowledge, shed the old assumptions, “We don’t have enough experts, therefore…”. Spin up another 10,000 agents. 

Evolving rightward on the horizontal axis to broader skills mandate will become ever easier due to AI. And likewise upward on the vertical axis to a broader work mandate. So, top-right Driving archetypes will become more feasible.

Obviously, “adopt AI everywhere you can” will be the general guidance, but given the limited resources of money, time, and attention, in strategic org design with OT we recommend that you ask and answer these questions:

1. What parts of the organization are the focus of development with OT? 

Focus your AI investment in the area you are elevating, where competitive advantage may come from early adoption of new technology.

2. What archetypes are part of your target, and what are the major bottlenecks or constraints limiting their rapid adoption? 

Find the early points where AI can make an outsized impact.

3. Do the AIs need monitoring by humans?  

That implies new responsibilities and processes in your organization. We predict that monitoring will become an important responsibility for human workers.

Strategic AI Adoption: Example

For example, a pet-supply company creates e-commerce software in a Resource topology. It’s a mature, uncomplicated market. Competition has delivered a better website and app that customers love. Leadership has decided on a business objective of retaining customers, with an org goal of fast flow to parity, by targeting a Delivery topology with CAPS-3 teams.

Now let's evaluate this set-up with the three questions:

Q> What parts of the company are the focus of org development? The Product Management and R&D departments, so focus on AI investment there. 

Q> What archetypes are part of the target? What’s a dominant constraint from one to the other?

The R&D department has mostly TASKS-1 individuals, and the target is CAPS-3 teams. On investigation, it’s a lack of people skilled in front-end design and coding. Focus time, money, and management attention on AIs, especially on that big bottleneck, over a generic “adopt AI.”

Q> Do the AIs need monitoring by humans?

Yes, because people have decided on AI agents that autonomously design and code front ends, independently creating and iterating on variations that they deploy and measure. The small number of existing front-end designers and developers joining the new CAPS-3 teams will be trained in agent monitoring and take on that responsibility.

Strategic AI Adoption in Archetype Groups

A table of how AI adoption can be strategized in each of the four quadrants:

Strategic AI Adoption in the Archetype GroupsArchetype Group | Characteristics | Strategic AI Adoption | Key Benefit"Directing" – characteristics: Oversee higher scopes of work without directly creating customer value. Focus on planning, coordination, and specifications."Directing" – strategic AI adoption: Strategic assistance through tools like predictive analytics, resource optimization, and workflow automation. Simulate project outcomes, automate prioritization, and allocate resources effectively. Digital omnipresence within the work done by the "Doing" archetypes to ensure control and compliance."Directing" – key benefit: Reduces manual planning time. Provides data-driven insights for better strategic decisions."Doing" – characteristics: Task-oriented work without a view of the whole picture. Heavily reliant on directives from others."Doing" – strategic AI adoption: Operational efficiency booster for tasks requiring routine expertise with intelligent automation, task-specific agents, and virtual assistants. Optimizes repetitive and tactical work. Automated and predictive guidance within task execution."Doing" – key benefit: Enhances productivity by reducing the cognitive load. Ensures consistent execution quality."Delivering" – characteristics: Deliver value within a narrow scope but often operate in silos. Limited alignment with broader goals."Delivering" – strategic AI adoption: Collaboration and alignment enabler via real-time data integration, recommendation engines, and feedback loops. Aligns work with organizational objectives. Plus all above-mentioned applications of AI for the "Doing" archetypes."Delivering" – key benefit: Reduces silos through integration. Ensures outputs align with strategic goals and avoid misaligned value delivery."Driving" – characteristics: Capable of understanding and delivering end-to-end customer value. Operate autonomously with shared ownership and alignment."Driving" – strategic AI adoption: Strategic partner through adaptive machine learning, real-time dashboards, and customer journey analytics. Predict customer needs and optimize end-to-end processes. AI serves as a subject-matter expert assisting faster (expanding, unbounded) learning for the teams."Driving" – key benefit: Amplifies autonomy and innovation. Enhances data-informed decision-making and customer alignment. Digital marketplaces for business objectives with AI-assisted prioritization mechanisms.Org Topologies™ Version 2.0 02.2025 by Alexey Krivitsky, Craig Larman, Roland Flemm (CC BY-NC-SA)

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