Hexagonal AI Strategy: 6-Steps to Master AI

Daniel Soto Rey
AI Consultant
Augmented Capital
April 29, 2024
10 min
Key Points:

- Seamless AI adoption is achieved by integrating effectively with existing systems and enhancing productivity, as part of the Hexagonal Strategy.

- This method ensures that AI solutions are secure, ethical, and capable of scaling with your business.

- The strategy's design process includes creating user-centric and unbiased interfaces for easy integration and better usability.

- It is crucial to align AI system controls with organizational policies, a key aspect of our method.

- Comprehensive risk management strategies are developed to address potential system failures and operational risks, enhancing the resilience of your AI systems.


In today's business landscape, AI is no longer just an optional extra but a critical component of strategic planning. With the surge in AI's popularity comes the dual challenge for businesses to navigate complex technological landscapes and ensure their AI strategies yield tangible results. Our Hexagonal AI Strategy offers a methodical approach to simplify the process of AI adoption and evolution. This strategy is designed to guide businesses through each phase of AI integration with precision and strategic foresight, turning the hype around AI into a strategic advantage for your business.

Step-by-step guide

Core Hexagon: Business Objectives

Objective: To ensure all aspects of AI strategy and system design are aligned with and driven by the business objectives.

Business objectives form the core of the Hexagonal AI Strategy. Every other step - system modeling, automation modeling, interaction modeling, security modeling, control modeling, and risk modeling - should revolve around and contribute to achieving these objectives. AI transformation is not an end in itself; it is a means to enhance the value created and delivered by the organization. Therefore, a clear understanding of the business objectives that are to be achieved or improved is crucial when designing and implementing AI systems. Every system, interaction, risk evaluation, and control should be valuable and relevant in the context of these objectives. This approach ensures that your AI strategy is not only technically sound but also business-centric, contributing to the overall success and growth of your organization.

Key Actions:

  • Define clear business objectives for AI transformation.
  • Ensure all aspects of your Hexagonal AI strategy and system design are aligned with these objectives.
  • Continuously evaluate and adjust your Hexagonal AI strategy to ensure it remains relevant and valuable in the context of your business objectives.

Hexagon 1: System and Function Modeling

Objective: Define how AI will integrate with existing systems and processes.

The first step in adopting a successful AI strategy involves a comprehensive understanding and analysis of your existing systems and processes, with a focus on System and Function Modeling. This step is crucial to ensuring that the AI technologies you plan to implement will seamlessly integrate with your current infrastructure.

During System and Function Modeling, the focus is not solely on the technical aspects of your existing IT setup, but also on the broader business function or process results that the AI is intended to enhance or modify. This involves a deep understanding of your business operations, goals, and the current processes in place.

One of the key aspects of this step is to maintain an abstract perspective that is agnostic to the final AI implementation. By keeping this perspective, you allow for the possibility of multiple solutions or implementations of AI-human relationships and processes. This approach ensures that your AI strategy is flexible and adaptable, able to accommodate different AI solutions as they evolve and change.

By conducting a thorough analysis of your existing IT setup and functional needs, you can pinpoint exactly how AI can enhance your operations without causing disruptions. This involves understanding the unique requirements and challenges of your business, and identifying the areas where AI can bring the most value.

System and Function Modeling provides a strong foundation for your AI strategy, setting the stage for successful integration and maximum benefit from your AI investments. It ensures not only that your AI technologies will function effectively within your existing infrastructure, but also that they will truly enhance your business processes and contribute to your overall business goals.

Key Actions:

  • Assess existing IT framework for potential AI integration areas.
  • Determine how AI can bolster specific business functions.
  • Design systems that improve existing abilities and ensure smooth operations.

Hexagon 2: Automation Modeling

Objective: Define AI functionalities that complement human capabilities and determine the extent and nature of automation.

Automation Modeling is an essential step in AI strategy, primarily focused on identifying which tasks to automate. The aim is to delegate repetitive and time-intensive tasks to AI, freeing up human resources for more complex, creative, and higher-value activities, thus enhancing productivity and job satisfaction.

To successfully implement Automation Modeling, it's crucial to employ an automation types and levels framework. This framework is a design/evaluation process that involves assigning specific function carriers to each of the functions modeled in the previous step, System and Function Modeling.

When choosing the type of automation, one must decide whether the automation fulfills one or more of these tasks:

  1. Acquisition - extracting data from a source.
  2. Analysis - interpreting the data to make inferences or note insights.
  3. Decision - making or proposing a decision or action based on the interpretation.
  4. Action - automatically executing a given action based on a decision.

Each of these automation types has its own implications and benefits, and the choice depends on the specific needs and goals of the business.

Additionally, when considering the level of automation, it is important to determine how much human involvement is needed to trigger or activate the automation. This can range from fully automated systems, where the AI operates independently without human intervention, to human-triggered systems, where the AI acts as a tool, executing tasks only when initiated by a human.

By carefully considering and defining these types and levels of automation, businesses can ensure they are implementing AI in a way that best complements and enhances human capabilities, and leads to increased productivity and efficiency.

Key Actions:

  • Identify tasks that are appropriate for automation.
  • Set boundaries for automation, ensuring there is a harmony between automated tasks and human supervision.
  • Gradually introduce automation to refine processes and enhance integration.

Hexagon 3: Interaction Modeling

Objective: Design user-friendly interactions that uphold ethical standards and enhance interpretability.

Interaction Modeling plays a crucial role in AI strategy, focusing on the design of user-friendly interfaces that incorporate AI principles. The usability of AI tools significantly impacts their effectiveness, making it essential to create interfaces that are not only intuitive and user-friendly, but also ethical and unbiased.

The process begins by ensuring the AI's user interface (UI) is intuitive, designed to facilitate seamless interaction between the user and the AI system. The goal is to minimize the learning curve and make the AI tools as accessible as possible to a broad range of users, regardless of their technical expertise.

In addition to ease of use, the interface must also uphold ethical standards. This involves designing the system in a way that prevents biases and promotes fairness. It is essential to recognize and mitigate any potential bias in the AI's decision-making process to ensure fair and unbiased outcomes.

Principles of interpretability, transparency, feedback, and accountability are intertwined and crucial in Interaction Modeling for AI systems. The AI's decision-making process should be clear (interpretability), enabling users to grasp the logic behind its outputs, thus fostering trust. Transparency, where users can view and understand the data AI uses and its processing methods, further enhances this trust. This transparency also allows users to correct the AI when needed. Feedback is crucial in creating a learning loop where the AI can improve its performance based on user corrections. Finally, accountability ensures that there are distinct responsibilities if the AI system fails, with mechanisms for system performance audits and adjustments. These principles work together to create a user-centric, transparent, and reliable AI system.

By focusing on these principles, Interaction Modeling ensures that the interfaces through which users interact with AI are user-centric and ethically designed. This step significantly enhances the user experience and the effectiveness of decision-making, ensuring that the AI tools are not just technically advanced, but also ethically sound and user-friendly.

Key Actions:

  • Develop user interfaces that are easy to use and understand.
  • Make certain that interfaces are designed ethically to prevent any biases.
  • Continually improve interfaces through testing and feedback from users.

Hexagon 4: Security Modeling

Objective: Ensure transparency and security in AI applications, fostering trust and compliance.

Security is a paramount aspect needing meticulous consideration when implementing AI. Automation and certainty share a critical relationship, and any tampering with an automated system may yield catastrophic outcomes depending on the context. Indeed, security is not only crucial due to privacy and regulatory issues but also because of the responsibility behind fully or partially automated systems.

Given the high stakes associated with these systems, they must be protected from external threats in order to maintain the confidentiality, integrity, and availability of data. This involves employing robust measures such as encryption, access controls, and intrusion detection systems. Each of these provides a safeguard against potential breaches, ensuring the smooth operation of the AI system.

However, security isn't merely a technical capability that can be bolted onto a system. It is a comprehensive organizational capability that encompasses processes, culture, and system design. It's not just about creating firewalls or intrusion detection systems, but also about fostering a culture of security-consciousness and ensuring that processes are in place to manage potential breaches or threats.

For instance, a security feature might be a technical architectural definition integrated into the AI system, designed to protect against specific types of attacks. Alternatively, it could be an organizational process that stems from a newly automated system, such as a procedure for regular security audits or a protocol for responding to security incidents.

The goal is to develop robust security frameworks that not only protect data integrity but also align with corporate governance and adhere to legal regulations. This means considering security at every stage of the AI adoption process, from the initial design and implementation of the AI system right through to its ongoing maintenance and evolution.

By making security a central aspect of your AI strategy, you can ensure not only that your AI technologies will function effectively within your existing infrastructure, but also that they will truly enhance your business processes and contribute to your overall business goals in a secure and reliable manner.

Key Actions:

  • Implement robust security protocols.
  • Create user controls for AI system settings and adjustments.
  • Ensure AI procedures adhere to both organizational and legal guidelines.
  • Regularly perform security evaluations and updates.

Hexagon 5: Control Modeling

Objective: Customize AI controls to align with corporate policies and cultural norms, ensuring manageable growth.

Control Modeling is an integral part of implementing AI systems, as it involves tailoring the controls of these systems to align with your organization's policies and cultural norms. This crucial step ensures that the growth and impact of AI within your business is manageable and echoes your corporate values and strategies.

In essence, Control Modeling refers to the incorporation of AI-specific controls into the overall system design, which includes elements such as User Interface (UI) and User Experience (UX) design. AI-based systems should be designed with control features that enable users to fine-tune and tweak the results of automation. This level of control allows for more than just system regulation; it provides users with a sense of agency.

This sense of agency is vital as it fosters a shared responsibility for correct outcomes between the human user and the AI system. In other words, instead of the system operating in isolation, the user has an active role in guiding and shaping the system's output. This symbiotic relationship between control and agency instills a sense of trust in AI-based systems.

Complementing this with the interaction model further enhances this trust. When users understand the AI system's decision-making process and can interact with the system effectively, it makes AI systems easier to adopt within an organization. This comprehensive understanding, combined with the ability to control and adjust the system, ensures that the AI tools are not just technically efficient, but also user-centric and ethically designed.

By extending the boundaries of Control Modeling in this way, businesses can ensure that their AI strategies are not only technically sound but also align with their broader corporate goals and values, fostering a sense of trust and agency among users and promoting smoother adoption of AI systems.

Key Actions:

  • Adapt AI controls to fit the organization's policies.
  • Verify the alignment of AI systems with the company's cultural norms.
  • Regularly observe AI systems to maintain controlled growth.

Hexagon 6: Risk Modeling

Objective: Implement comprehensive risk management strategies for AI systems, addressing potential vulnerabilities and ensuring sustainability.

The final, yet highly significant phase in the AI adoption process is Risk Modeling. This step is crucial, and somewhat transversal, as it extends to every aspect of AI systems, which all come with inherent risks. Understanding and mitigating these risks associated with AI implementations are of paramount importance.

Risk Modeling necessitates the identification of potential system failures, ethical concerns, and operational risks. It also involves the creation of proactive strategies to address these issues. The inherent probabilistic nature of AI means there is always a risk of incorrect outcomes. Therefore, acknowledging this and incorporating it into your risk model is necessary.

Additionally, risks related to users, company processes, changes in technologies, and all the classic system risks that one could plan out also need to be considered. Each of these areas carries its own distinct challenges and potential pitfalls that need to be anticipated and addressed in your risk model. For instance, a poorly designed user interface might lead to user errors, which in turn could result in incorrect outcomes or inefficient use of the AI system.

The transversal nature of Risk Modeling also means that it has to be integrated into all other steps of your AI strategy. From System and Function Modeling to Control Modeling, each phase needs to be designed and implemented with an eye on potential risks.

The ultimate goal of Risk Modeling is to create an AI system that is not only technically sound and operationally efficient, but also resilient and robust in the face of potential risks. This involves identifying each risk, assessing its probability of occurrence, and designing the system with these risks in mind. For example, a risk that is deemed too high may necessitate a redesign of the system. This could involve changing an automation level from fully automated to human-confirmation to reduce the risk.

By making Risk Modeling a critical part of your AI strategy, you can ensure that your AI systems are not only capable of delivering enhanced efficiency and productivity, but also of withstanding and adapting to potential challenges and risks. This comprehensive approach to risk management is what ultimately transforms AI hype into substantial business success.

Key Actions:

  • Conduct comprehensive evaluations of potential risks.
  • Formulate plans to address and reduce identified risks.
  • Set up emergency procedures for rapid response to incidents.

Final thoughts

In conclusion, the development and maintenance of a Hexagonal AI Strategy is a multifaceted and ongoing process that demands careful planning, assessment, and execution. Each of the six steps outlined in this guide plays an integral part in ensuring successful AI adoption and fostering innovation and transformation in your business. By adopting a comprehensive approach that covers everything from system modeling to risk management, you can harness the potential of AI to boost productivity, efficiency, and overall business success. The Hexagonal AI Strategy is not a quick fix, but a strategic long-term investment that, when appropriately designed and maintained, will yield substantial benefits. It's a living asset of the organization, designed to continuously adapt to the ever-changing landscape of AI technologies and possibilities. The key to successful AI adoption and ongoing innovation lies in a well-thought-out Hexagonal AI Strategy and its careful execution.

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