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Humans + AI: Value from Artificial Intelligence

A framework to support strategic discussions by boards and executive teams about the role of AI in their strategy and success. See more on strategy facilitation.

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Design to complement humans

Explainability and accountability

Equality and inclusion

Safety and security

Bias auditing

Human oversight structures

Societal goal alignment


The potential of AI will only be tapped if clear strategies are implemented. This requires understanding the potential and establishing a specific set of activities to seize the opportunity.


Executive understanding

A critical first step to develop and implement an effective AI strategy is for the board and top executives to understand the potential of these technologies for their organization and industry. This can be done through a variety of approaches including custom workshops and mentoring.

Evolutionary roadmaps

All substantial organizations need to have an AI roadmap that prioritizes possibilities for the potential role of AI in the organization’s future and specifies immediate steps to move in that direction as well as possible paths forwards. However the roadmap must be designed to evolve rapidly as the landscape progresses and new possibilities and priorities emerge.

Support employee value

AI strategy needs to have at its core the intent to support employees’ ability to create value. In the future almost all value will be created by human-machine collaboration, so AI implementation should always focus on how the technologies can support superior human performance in their evolved roles.

Value-focused pilots

The applications of AI are being continually discovered and developed, and are often specific to a particular organization. Well-designed pilots focus on the highest-value and easiest domains, where the outcomes and lessons learned can be best scaled into other areas.

Process reconception

Most organizations’ business processes are legacies evolved from ancient paper-based structures that have been digitized. Any solid strategy needs to look not just at how AI can be used to automate elements of the current process, but re-envisage what processes will look like with more advanced technologies, to potentially shift to a completely different structure.

Rebalancing customer communication

Customer communication is already done by both humans and automated systems. While the balance will continue to shift to automation and AI, including human-like audio and video conversations, considered strategic choices need to be made on the role of human and automated communication across segments, and how these should develop over time.

Business model redefinition

The rapid development of AI is impacting many facets of existing business models, including the nature of value creation, relevant core competences, the role of talent, and customer experience. To survive and thrive organizations will need to proactively redefine their future business models and how they will evolve from their current one.


In a world increasingly shaped by the rapidly-developing capabilities of AI, every organization must focus on the continuous development of their competences to tap its potential. All of the organizational competences for creating value from AI are fully aligned with the broader competences required for organizational success in coming years.



Development, Internal, External

In a world in which AI talent is highly scarce, organizations must start with how they can develop or access the right people. This includes building a team of INTERNAL staff with relevant capabilities, both through hiring and more often through capability DEVELOPMENT of existing team members. However, organizations will almost always draw on EXTERNAL talent for specific tasks, either by developing a talent pool or using platforms. In all cases working on interesting projects and enhancing capabilities will be essential to attract the best talent.


Strategy, Aggregation, Integrity

AI feeds on data, often voraciously. A well-developed data STRATEGY is essential, including data sources, tagging, storage, backup, and clear governance structures including privacy. The judicious AGGREGATION of data from multiple sources, both internal and external, is central to value creation but can require sophisticated systems for integration. Since AI systems’ value depends on the quality of their inputs, systems for ensuring data INTEGRITY are critical, including data cleansing.


Selection, Standards, Frameworks

The development of AI competences is greatly facilitated by the widespread availability of AI and machine learning platforms and frameworks, both commercial and open source. A clear STRATEGY for use of platforms includes the SELECTION of the machine learning and other platforms that will be used for development and a choice of STANDARDS for implementing systems.


Human-centered, Experimentation, Trust

Organizations will make little headway with AI unless they have a culture that supports it. A HUMAN-CENTERED approach to AI will not only attract talent but also vastly enable staff accepting and embracing the new systems. A culture of EXPERIMENTATION in which failures are understood to be the necessary path to success is necessary in a domain which is being developed real-time. Ultimately TRUST in leadership and colleagues is vital in supporting the scope of change.


Engagement, Partnerships, Contribution

Participating positively in the AI ecosystem greatly supports building competences. ENGAGEMENT with and CONTRIBUTION to AI communities, including those developing open source and ethical frameworks and working through transformation challenges, accelerates learning and progress. Establishing specific PARTNERSHIPS especially within industries can support new business models and opportunities. Companies should also engage with their ecosystem of customers, suppliers and partners in creating collaborative value with AI.



Pattern matching

Fraud detection, Security scanning, Biometrics, Diagnosis, Visual recognition, Speed recognition


Workflow, Robotics, Vehicle operation, Task, Process,  Machine Operation


Learning, Logistics, Predictive maintenance, Marketing, Personalization, Process efficiency

Decision making

Approvals, Data insights, Decision support, Autonomous, Trading, Recommendations


Agents, Thought interfaces, Emotional engagement, Translation, Writing, Conversational interfaces



Defined pattern recognition

Emergent pattern recognition

Natural language processing

Predictive analytics

Multi-parameter optimization

Spatial processing

Generative design