A guide to ensure sustainable and humane adoption of the exploding AI Agentic ecosystem
In Part One of Enabling AI with Coaching as the Bridge, that you can find here
Chirs Chan and Phil Gadzinski commenced a deep review on how to ensure sustainable and humane adoption of the exploding Agentic ecosystem with the key role for the "AI Coach" emerging. In essence, an AI coach needs not only a deep understanding of AI's technical underpinnings and tools but also how to strategically integrate these into an organisation's operating model, culture, and existing governance frameworks while prioritising human-centricity and continuous adaptation.
In the first article, we started mapping the landscape and why we see the need for Coaching in AI Adoption and Transformation. We see this role as currently a novel activity that will likely grow in demand over the next 6-12 months. Some companies are doing this now.
In this Second Part, we dig into the three key areas we believe AI Coaches need to upskill in using the Lyssa Adkins and Michael Spayd Agile Coaching Competency Framework (2011, Agile Coaching Institute). They are
We believe this model provides a basis for AI Transformation coaching. AI extends and evolves the Agile Coaching Competency Framework to reflect the impact of AI on coaching practices. In essence, an AI coach needs not only a deep understanding of AI's technical underpinnings and tools but also how to strategically integrate these into an organisation's operating model, culture, and existing governance frameworks while prioritising human-centricity and continuous adaptation.
For an AI coach assisting with AI transformation, extending the Agile Coaching Competency Framework to include AI's impact on coaching practices, particularly in "Technical Mastery," requires a comprehensive understanding of various AI-related domains. This model provides a basis for AI Transformation coaching, emphasising a shift from viewing AI as merely a technological implementation to a holistic organisational change program.
The key Competencies they will likely need to bring, or learn fast, to be effective include:
AI coaches must possess personal AI literacy, enabling them to distinguish genuine AI capabilities from marketing hype and understand the organisational implications of various AI approaches. They should be able to actively use and experiment with AI tools, such as building prompts and testing agents, to grasp the implications across different levels of work, strategy, data, and decision-making. This hands-on experience is crucial for guiding others effectively.
AI transformation demands significant re-skilling, including developing robust data literacy across the organisation. Coaches need to guide organisations in compiling and implementing a comprehensive data strategy that addresses data collection, quality assurance, storage, accessibility, and strong data governance policies that balance security with usability. They should understand that data is a strategic asset. AI excels at processing large datasets for data-driven reasoning, but coaches must ensure that human judgment remains essential for interpreting AI-generated insights, considering contextual factors, and making ethical decisions.
Organisations need a technical architecture that supports AI implementation at scale, including model development environments, deployment infrastructure, monitoring capabilities, and integration patterns. Coaches should understand the importance of comprehensive MLOps pipelines for continuous monitoring of key performance indicators. They likely don't need to have depth of knowledge, but need to be ael to create an environment to ask the right questions and guide to outcomes.AI implementations often face challenges when integrating with existing technical infrastructure and business processes that weren't designed with machine learning in mind. Coaches will need to help navigate these complexities.
Coaches must emphasize that explainability, transparency, and interpretability are essential controls for any AI strategy. This involves understanding techniques that provide insights into an AI's output, enabling more informed human decisions.
To be effective in assisting with AI transformation, an AI coach needs to possess a deep understanding of "Business Mastery" as it applies to the use of AI to generate true business value. To date many PoCs and implementations have failed to provide effective ROI due to being more about the tech than the business problem to solve. This understanding includes knowledge of AI-enhanced customer experiences, AI-augmented decision-making, and adaptive investment models, extending beyond purely technical aspects to encompass strategic and cultural shifts.
An AI coach must understand how AI can fundamentally reshape and improve customer interactions and satisfaction. Coaches should guide organisations in using AI to enable personalisation and responsiveness at scale, thereby creating competitive differentiation in customer-facing processes. This involves understanding how AI can optimise workflows for maximum efficiency and turn data into actionable insights to enhance both digital and agile foundations. The recent Air Canada chatbot incident serves as a stark warning: customers expect reliability and trust in information provided by a company's AI. An AI coach must emphasise that trust is built through transparency and that what cannot be trusted cannot create value. This means guiding teams to prioritise accuracy and explainability in AI models, especially when driven by customer expectations.
Coaches should encourage a shift from abstract "AI strategy" to focusing on relentless micro-experiments centered on customer pain-points. This helps ensure that AI solutions genuinely address user needs and deliver tangible improvements to the customer journey.
The coach's role in this area involves enabling effective human-AI collaboration for improved decision-making, while mitigating associated risks. An AI coach needs to articulate that AI's impact is highest when it works alongside people, enhancing decision-making and creativity (augmentation) rather than purely replacing human effort (automation). This involves understanding how AI can make teams "smarter and more efficient" and open doors to innovation. Coaches must comprehend Agentic AI, which goes beyond assistive tools to understand context, decide, execute actions, evaluate results, and iterate, often without direct human guidance. This requires guiding organisations through the shift from static cross-functional squads to mission-stable, membership-fluid teams where AI agents are integrated as active teammates. They should understand how agents can supply niche expertise, bust silos, and shorten lead-times by stepping in where human availability might cause bottlenecks.
While AI excels at processing large datasets for data-driven reasoning, human judgment remains essential for interpreting AI-generated insights, considering contextual factors, and making ethical decisions aligned with organisational values and societal norms. The coach must help leaders avoid over-dependence on AI for intuitive thinking, as this can create vulnerability if human expertise and judgment are not maintained. Coaches should assist organisations in rethinking job roles, team structures, and workflows as routine tasks are automated, freeing humans for more complex, creative, and strategic activities. This involves reimagining workflows from first principles to optimise the needed partnership between human and artificial intelligence, clearly defining where human judgment is essential, where AI provides recommendations, and where full automation is appropriate.
Executives need to develop personal AI literacy to distinguish genuine capabilities from hype and understand the organizational implications of different AI approaches. Coaches can facilitate this by encouraging leaders to actively use and experiment with AI tools, asking how AI might change the operating model. The future of leadership will involve orchestrating human-AI teams. Leadership coaching remains a critical enabler - much like the agile movement of the past decade. Again we see these parallels emerging.
An AI coach can play a role in establishing flexible and value-driven investment strategies for AI transformation. Coaches can guide the development of a value-driven roadmap with clear business outcomes, starting with smaller, high-impact opportunities to build confidence and momentum. This roadmap needs inherent flexibility, recognising that the rapid pace of AI evolution means plan lock-in could be fatal and emphasising strategic agility. The past 20 years of agile adoption have given the broader global agile coaching community the muscles to do this effectively - they just need to be repointed to this new yet similar problem.
AI transformation requires modern governance, which is a dynamic, adaptive, and value-driven approach embracing flexibility, transparency, and collaboration. You can read more about Modernising Governance here. This allows for better resource allocation and continuous adaptation, contrasting with rigid, traditional models. Coaches can leverage principles like the Five Stanchions of Modern Governance, including Data-Driven Reasoning and Sensible Transparency, to guide adaptive investment by providing real-time data for insights and informed decisions. Coaches need to help organisations develop measurement frameworks that go beyond traditional ROI, which often fail to capture AI's transformative potential. This framework should include leading indicators of adoption, intermediate outcomes, and lagging business impact metrics to maintain commitment during implementation challenges and provide evidence of progress.
Transformation is constant. AI Coaches should continue to advocate for a shift from "big-bang transformation" to relentless micro-experiments focused on customer pain-points. This could involve spinning up a portfolio of experiments with clear success metrics, communicating results, and then scaling or sunsetting initiatives based on their ROI. This approach fosters a culture of learning velocity over efficiency worship and promotes continuous upskilling. The coach's role includes guiding this experimentation and facilitating knowledge transfer to accelerate learning curves and reduce resistance. Establishing a system of record that captures and maintains visibility of teams, responsibilities, assets, and capabilities is critical for managing fluid teams and AI agents, and for confidently tracking the ROI of AI investments. This helps in making better decisions and fluidly allocating human-AI teams as needs change. Modern workflow management systems such as Planview, are even more critical - without them you don't get the data you need to make better informed decisions in a timely manner.
To be effective in "Transformation Mastery" as an AI coach, you need to deeply understand and guide organisations through the profound, systemic shifts required by AI, moving beyond mere technological adoption to encompass new operating models, ethical considerations, and evolving leadership dynamics. This involves drawing on the hard earned lessons from agile transformations of the past and applying them to the current AI era.
An AI coach needs to emphasise that achieving significant value from AI requires a fundamental change in an organisation's Operating Model, encompassing people, process, and technology, rather than just technological implementation. This is comparable to the Agile revolution, indicating a need for a deep, technology-driven perspective. We wrote about this here. The consensus among industry leaders in Australia is that the approach to enabling AI is likely to be "transformative" (Kaikaku) rather than merely "continuous improvement" (Kaizen). AI transformation requires significant shifts in people, processes, and organisational structures to realise its full value. Adoption fundamentally changes how people work and make decisions, which means a deliberate cultural evolution. This involves developing comfort with probabilistic outputs, establishing new relationships between human and machine judgment, and creating psychological safety for roles affected by automation. Coaches must guide leadership to shape cultural norms that promote appropriate trust, productive scepticism, and collaborative human-AI workflows. This mirrors the cultural shifts required for Agile to succeed.
Successful AI implementations will require redesigning workflows and decision processes to leverage AI strengths while compensating for limitations. This means rethinking how work happens and optimising for the unique partnership between human and artificial intelligence, rather than merely automating existing processes. Human-AI teams will likely shift from static cross-functional squads to mission-stable, membership-fluid teams, where AI agents are integrated as active teammates. Coaches should guide organisations in rethinking job roles, team structures, and workflows as routine tasks are automated, freeing humans for more complex, creative, and strategic activities.
An AI coach needs to understand "Agentic AI" – systems that understand context, decide, execute actions, evaluate results, and iterate, often without direct human guidance. They should guide organisations in how these agents can supply niche expertise, bust silos, and shorten lead-times by stepping in where human availability might cause bottlenecks. The coach will need to help organisations prepare for AI agents to be embedded in teams and work collaboratively with human teammates. It is critical to have a system of record that captures and maintains visibility of teams, responsibilities, assets, and capabilities, which is crucial for managing fluid teams and AI agents, and for tracking the ROI of AI investments. There is already a future where a product like TeamForm, which allows you to connect all of your teams and AI tools with your strategy and work so you can improve team performance, is made up of a mix of humans and agents. And managed by either a Human, or a Service Orchestrator,.
Coaches should advocate for embedding ethics into the AI lifecycle from the beginning, not as an afterthought. This involves developing and refining a comprehensive Ethical AI Framework that defines core ethical principles and provides actionable guidelines for their application across the AI lifecycle.
The importance of humanity as the cornerstone of organisational success increases with AI prevalence. While AI augments capabilities, it cannot replace uniquely human qualities like emotional intelligence, creativity, and adaptability. The coach needs to help leaders create space for people to develop these human skills and ensure the workforce remains relevant and valuable in an AI-augmented environment. Human judgment remains essential for interpreting AI-generated insights, considering contextual factors, and making ethical decisions.
Unlike Agile adoption, where leadership could often delegate understanding, AI Transformation requires executives to develop personal AI literacy. They need to distinguish genuine AI capabilities from marketing hype and understand the organisational implications of different AI approaches. Coaches should encourage leaders to actively use and experiment with AI tools, asking how AI might change the operating model.
Executives will need to provide active sponsorship, visibly modeling AI adoption, allocating appropriate resources, removing organisational barriers, and consistently communicating the strategic importance of the transformation. The future of leadership lies in creating effective human-AI teams where people are the orchestrators. Leaders have the ost influence over an organisation - not activating the leadership group means delay or likely failure in Ai adoption. Which then may risk organisation survival. Leaders need to understand that AI transformation requires modern governance, which is a dynamic, adaptive, and value-driven approach that breaks away from traditional, rigid models.
AI coaching is a new role , though really just a minor pivot of an existing capability, positioned as the linchpin for supporting the purposeful design and transition to new ways of working in the AI-enabled future, building on the success of Agile adoption. AI Coaches will bridge the gap between technical possibilities and business realities, facilitating the cultural and process changes necessary for sustainable AI implementation. We can foresee based on clear parallels that Investing in AI coaching significantly increases the probability of successful AI adoption by accelerating learning curves through guided experience and reducing resistance by addressing concerns and misconceptions. The AI Coach will guide the consistent application of best practices and facilitate knowledge transfer and capability building. To be effective,we can also predict there will likely be different levels of coaching. Enterprise AI Coaches work at the strategic level, focusing on roadmaps, governance, and long-term capability building, while Team-level AI Coaches operate tactically, guiding project implementation and building team capabilities. Both roles are essential for comprehensive transformation. Coaches can leverage and draw on the lessons learned from Agile adoption, particularly the importance of addressing cultural and organizational elements alongside technological implementation.
Because AI isn’t just a technology and tool - it changes how people think, work and decide. Without coaching to help address the underlying organisational systems, trying to implement AI tools will yield limited results at best. Our AI coaches at Everest ensure that AI transformations are not just about tools - but about sustainable, human, cultural and organisational shifts for ongoing AI success.
If you want to help your organisation effectively and efficiently adopt Ai - let's talk.
