Beyond AI Adoption: Building Distributed AI Systems and Competing for AI Use Adherence

🧭 Dojo Compass

Module: Finance, Risk Management and Long-Term Resilience

Focus area: Technology, AI and Future Readiness

Many companies are introducing AI into their operations, but much of this activity remains fragmented. Individual employees, departments, and business units use different models and applications, often without the ability to share information or coordinate their activities effectively.

The next competitive frontier may be the development of distributed AI systems that connect these activities into more coherent environments. Such systems can extend beyond employees to customers, suppliers, partners, and prospective customers.

This article explores the strategic implications of that transition and introduces the concept of AI use adherence: the extent to which an organization’s AI-enabled capabilities become embedded in the ongoing activities and workflows of the people and organizations it serves.

🎯 Key Challenge

Are companies building isolated AI applications, or are they building the foundations of an integrated AI-enabled business ecosystem?

Most organizations are approaching AI through individual use cases.

An employee uses an AI assistant to draft correspondence. A marketing department uses a model to develop campaigns. A software team employs AI coding tools. Finance experiments with automated reporting, while customer service introduces a chatbot.

Each initiative may generate value. But collectively, they may produce a fragmented environment in which:

Different employees use different AI models and applications.
Business units maintain separate data sources and knowledge repositories.
Information generated in one part of the organization is not readily available elsewhere.
AI applications have limited understanding of broader business processes.
Users repeatedly provide information that the organization already possesses.
Models can access information but have limited awareness of the activities and events surrounding it.
AI-generated insights and recommendations are not consistently connected to decisions and actions.

The result is an organization with many AI applications but relatively little integrated AI capability.

This distinction is important. The value of AI is not limited to the productivity gains generated by individual users. It can also arise from connecting people, information, processes, and decisions in ways that were previously difficult or impossible.

Consider a simple example.

A salesperson uses an AI assistant to prepare a customer proposal. The assistant has access to product information but not to the customer’s service history, outstanding issues, previous commercial negotiations, or relevant information held by other departments.

The salesperson must gather this information manually, perhaps using several different systems and conversations with colleagues.

Now imagine a more coherent environment in which the AI system can access the relevant information, subject to appropriate permissions. It can recognize that the customer has an unresolved service issue, identify a previous pricing commitment, and take account of an upcoming renewal discussion.

The AI is no longer simply helping the salesperson write a better proposal. It is helping the salesperson understand the broader customer relationship and make a better business decision.

The difference is not necessarily the intelligence of the underlying model. It is the quality of the system within which the model operates.

The challenge is therefore to move beyond fragmented AI adoption and begin thinking about how AI capabilities can be organized into connected, distributed systems.

🥋 Dojo Solution

The Dojo insight is that the next stage of AI transformation should focus on connecting capabilities, not simply adding more AI tools.

A distributed AI system is not necessarily one large model or a single centralized application. It is an interconnected environment in which multiple models, agents, users, applications, and data sources can work together.

Different models may perform different tasks. Some may specialize in legal analysis, others in customer service, coding, financial analysis, or operational planning. Some may be internally developed, while others are provided by external companies.

What makes the system coherent is the ability to connect these components through appropriate data access, shared context, common processes, and coordinated interactions.

Five principles are particularly important.

1. Connect users rather than treating AI adoption as an individual activity.

An organization should increasingly view its employees as participants in a shared AI-enabled environment.

A user’s work may generate information, insights, decisions, and outputs that can be useful to others. With appropriate permissions and governance, these can become part of a broader organizational knowledge and capability base.

The objective is not to make every employee’s information visible to everyone. It is to make relevant information accessible to the people and AI applications that are authorized to use it.

2. Connect data rather than continually creating isolated knowledge repositories.

AI systems become more useful when they can access relevant information across the organization.

This does not necessarily require moving all information into one database. Distributed architectures can allow models and applications to retrieve information from different systems while respecting the permissions and controls governing each source.

The important issue is that information should be discoverable, understandable, and usable across appropriate organizational boundaries.

3. Enable models to understand dynamics, not just documents and data.

This may be one of the most important developments.

Traditional information systems tend to store relatively static information: customer records, contracts, financial statements, product specifications, and reports.

But businesses operate through continuous interactions and changes.

Customers make decisions. Employees undertake activities. Projects advance or fall behind. Risks emerge. Relationships evolve. Commitments are made and sometimes missed.

A more advanced AI environment should increasingly be able to interpret these dynamics.

For example, it should not merely know that a customer has purchased a particular product. It should be able to recognize that the customer has recently increased usage, experienced a service problem, contacted the company several times, and is approaching a renewal decision.

That broader context may be much more valuable than any individual document.

4. Connect AI capabilities to workflows and actions.

An AI system should not simply provide answers. It should increasingly help users carry out activities.

This could include preparing proposals, updating project plans, identifying operational exceptions, coordinating approvals, monitoring commitments, or initiating permitted actions in connected systems.

Appropriate human oversight, approval rights, and auditability remain essential, particularly where actions have financial, legal, operational, or customer consequences.

The objective is to reduce the distance between information, understanding, decisions, and execution.

5. Extend the system beyond the organization.

Perhaps the greatest opportunity is to extend AI-enabled environments to customers, suppliers, partners, and prospective customers.

A company that connects its internal AI capabilities with the workflows of its customers may become more than a supplier. It may become an integral part of how those customers operate.

This creates the possibility of a broader competitive relationship in which the value of the business increases as its AI-enabled services become more deeply integrated into the activities of the people and organizations it serves.

🏗️ Putting It into Practice

Building a distributed AI system is a strategic undertaking. It does not require a company to abandon existing AI applications or immediately implement a comprehensive technology architecture.

The objective is to move progressively from isolated use cases towards a connected environment.

Step 1: Map the existing AI landscape

Begin by identifying how AI is currently being used throughout the organization.

Do not limit the exercise to officially approved applications. Employees may already be using AI tools independently, and departments may have developed applications that management does not fully understand.

Map:

Which models and AI applications are being used.
Which departments and employees use them.
What business activities they support.
Which data sources they access.
Whether they generate information or outputs that could be useful elsewhere.
What integrations already exist.
Where duplication, information silos, or security risks may arise.

The objective is to understand the existing environment before designing a more coherent one.

This exercise may reveal that the company has already developed valuable AI capabilities but has not organized them into a system that allows the wider organization to benefit.

Step 2: Establish a shared AI architecture and governance framework

A distributed system needs a common foundation.

This does not mean that every employee must use the same model or that every application must be built on one platform. It means establishing the principles and infrastructure that allow different components to interact.

Important elements include:

Identity and access management: Users and AI agents should have clearly defined identities and permissions.
Data governance: Information should be classified according to sensitivity, ownership, permitted use, and access rights.
Integration: Applications should be able to retrieve relevant information from authorized systems.
Model management: The organization should understand which models are being used, for what purposes, and under what conditions.
Security and auditability: Access, recommendations, and actions should be appropriately logged and monitored.
Interoperability: Systems should use suitable interfaces and standards so that components can be replaced or expanded without rebuilding the entire environment.

A distributed architecture can combine internal systems, external AI services, specialized models, and locally deployed applications.

The objective is to create coherence without unnecessarily imposing technological uniformity.

Step 3: Build a shared context layer

Connecting applications is not enough. AI systems also need access to relevant context.

Consider the difference between giving a model access to a folder of customer documents and allowing it to understand the customer’s relationship with the company.

The second requires connecting information from multiple sources, potentially including:

Customer records and transaction histories.
Contracts and commercial commitments.
Communications and service interactions.
Current projects and outstanding issues.
Product usage and performance information.
Relevant financial and operational data.

The system must be able to identify relationships between these sources and determine which information is relevant to a particular user or activity.

Technologies such as retrieval-augmented generation, knowledge graphs, event streams, and workflow integrations can contribute to this capability.

Importantly, the objective is not simply to accumulate more data. It is to make the right information available in the right context, to the right user or AI application, at the right time.

Step 4: Move from static information to dynamic understanding

This is where distributed AI systems may create a particularly significant advantage.

A company should begin identifying important business dynamics that AI systems could monitor and interpret.

For example:

Business area	Static information	Dynamic understanding
Sales	Customer profile and purchase history	Changes in buying behavior, engagement, and commercial opportunities
Customer service	Support tickets and service records	Emerging dissatisfaction, recurring problems, and unresolved commitments
Finance	Financial statements and budgets	Changes in cash flow, spending patterns, and financial exposure
Operations	Production records and procedures	Emerging bottlenecks, performance deviations, and resource constraints
Human resources	Employee records and organizational charts	Workload pressures, capability gaps, and coordination challenges

The distinction is between knowing what has happened and understanding what is happening.

For example, a customer service system might contain hundreds of individual support tickets. A more advanced AI system could recognize that several customers are experiencing related problems following a recent product release.

It could identify the emerging pattern, alert the appropriate team, and help coordinate a response.

This requires more than access to documents. It requires the ability to interpret events, relationships, changes, and sequences of activity.

Step 5: Connect AI to business workflows

Once relevant information and context are available, identify workflows in which AI can help coordinate activities and actions.

Start with processes that involve multiple people, departments, or systems.

Examples include:

Preparing a customer proposal using relevant commercial, technical, and service information.
Coordinating a sales opportunity from initial contact through approval and contracting.
Monitoring project commitments and identifying delays or resource conflicts.
Managing customer complaints by connecting service, product, and operational teams.
Supporting procurement by comparing requirements, suppliers, contracts, and delivery performance.

The objective is to make AI part of the actual process rather than an application that employees must remember to consult separately.

Begin with recommendations and human-approved actions. As reliability, controls, and organizational confidence improve, selected activities may be automated within clearly defined boundaries.

Step 6: Extend the environment to customers and partners

After establishing an appropriate internal foundation, explore how the system could extend beyond the company’s employees.

This may create opportunities for much deeper commercial relationships.

Consider three examples:

Agricultural inputs: A supplier could provide farmers with an AI-enabled environment that combines crop information, weather conditions, agronomic recommendations, product knowledge, and purchasing support. With the farmer’s authorization, the system could help plan input requirements, monitor crop conditions, and identify emerging risks.

Financial services: A financial institution could provide business customers with an AI-enabled environment that connects relevant financial information, cash-flow analysis, financing options, and account activity. The institution becomes part of the customer’s ongoing financial management rather than simply a provider of individual banking products.

Software and technology: A software company could allow customers to interact with its products through AI agents that understand their configurations, usage patterns, documentation, and support history. The AI could help customers solve problems, improve performance, and identify additional capabilities.

In each case, the opportunity is to become integrated into the customer’s activities.

There are important boundaries. External users must have clearly defined access rights, and one customer’s data must never become accessible to another customer without authorization. Consent, confidentiality, privacy, contractual obligations, and data ownership must be addressed from the beginning.

The objective is to extend value, not to create unrestricted access to information.

Step 7: Compete for AI use adherence

This leads to a broader strategic concept.

As businesses develop AI-enabled services, they may increasingly compete for what could be called AI use adherence.

By this, I mean the extent to which users, customers, and partners consistently incorporate a company’s AI capabilities into their ongoing activities and workflows.

It is not simply the number of people who have tried an AI application, nor is it a measure of compulsory usage. It is the degree to which the system becomes genuinely useful and sufficiently integrated that people choose to rely on it as part of their work.

For example, consider two software providers.

The first provides customers with an AI chatbot that answers questions about its products. The second provides an AI-enabled environment that understands the customer’s configuration, supports its workflows, helps resolve problems, and connects relevant information to the customer’s own systems.

Both companies offer AI. But the second has the potential to become much more deeply integrated into the customer’s activities.

This creates several possible competitive advantages:

More frequent and meaningful customer engagement.
Greater switching costs arising from workflow integration and accumulated context.
Better understanding of customer needs and changing requirements.
Opportunities to develop additional services.
Stronger customer relationships and potentially greater retention.
New sources of information that can improve the company’s products and services, subject to appropriate permissions.

Companies should therefore consider not only how many customers use their AI applications, but also how deeply those applications are embedded in customer activities and how much value they create.

Useful indicators might include repeat usage, workflow integration, the proportion of relevant activities supported by AI, customer retention, time saved, measurable customer outcomes, and the number of processes in which the system provides useful assistance.

The ultimate objective is not to maximize AI usage for its own sake. It is to become an increasingly valuable part of the way customers and partners operate.

📌 Key Takeaways

Fragmented AI adoption can generate individual productivity gains without creating a coherent organizational advantage.
Distributed AI systems connect users, models, agents, applications, data, and workflows while allowing different components to retain specialized roles.
The value of AI increases when relevant information can be shared and interpreted across appropriate organizational boundaries.
Future AI systems should increasingly understand business dynamics, including events, relationships, activities, changes, and emerging patterns—not just static documents and data.
Connecting AI to workflows and permitted actions can reduce the distance between information, decisions, and execution.
A coherent AI architecture does not require one model or one database; it requires appropriate interoperability, governance, context, and access controls.
The opportunity extends beyond employees to customers, suppliers, partners, and prospective customers.
Companies may increasingly compete for AI use adherence: becoming sufficiently integrated into users’ activities that their AI capabilities are repeatedly and meaningfully used.
Customer integration can create competitive advantages, but it must be built on trust, security, privacy, and demonstrable value.
The strategic question is shifting from how many AI tools a company deploys to how effectively it connects AI capabilities into a system that creates value.

🌿 Reflection

Think about how AI is currently being used in your organization.

Are employees and departments developing isolated applications, or are they contributing to a broader system in which knowledge, data, and capabilities can be shared and coordinated?

Now extend that question beyond the organization.

What if your customers increasingly expected to interact with your business through AI-enabled environments? What if they wanted your knowledge, products, services, and expertise to be available within the workflows they already use? What if a competitor made its AI capabilities so useful and so deeply integrated that customers began to organize important parts of their operations around them?

This raises a strategic possibility.

The competitive advantage of the future may not come simply from having access to better AI models. Models will become increasingly accessible, and the differences between individual tools may become less decisive.

The advantage may instead come from the systems businesses build around those models: the data they can appropriately access, the context they can understand, the relationships they can support, and the activities they can help coordinate.

Companies that begin thinking in these terms today may be better positioned to shape the next stage of competition.

The opportunity is not simply to use AI to improve the business. It is to make the business an increasingly valuable part of the AI-enabled environment in which employees, customers, and partners operate.

⚔️ Dojo Mission

Identify one opportunity to connect fragmented AI capabilities into a more coherent system.

Spend 30 minutes examining one important business process, either internally or in your relationship with customers.

Identify the people, departments, or external parties involved.
List the AI tools, information sources, and systems they currently use.
Identify where information is fragmented, duplicated, inaccessible, or disconnected from action.
Consider what additional context or business dynamics an AI system would need to understand.
Identify one practical way to connect the relevant users, data, and AI capabilities.
Consider whether the resulting capability could eventually be extended to customers or partners.

Define one small experiment that could demonstrate the value of a more connected approach.

Your mission is to move one step beyond AI adoption and begin designing AI integration.