Category: Article

Over a few months last year, the authors of this paper facilitated a strategic-planning exercise for a large corporate business unit (BU) client working in the North American fibre-based packaging industry. The BU was seeking to refine its business strategy, create a new vision for a revamped management team and to position itself as an attractive investment opportunity for Head office.

This article aims to explore how AI can help humans better interpret and learn from visual scribing/sensing imagery that is generated during strategic-planning meetings. Three AI tools—Gemini, Claude, and ChatGPT (using the Aiden Cinnamon Tea Sensibility Simulation Protocol 2.0)—were asked during the sessions and afterwards to interpret Kandabura’s artwork from each of the three two-day off-site retreats. Their outputs were merged for this article. We invite you to spend a minute or two observing this work (presented below) before reading the subsequent texts. What do you see/sense? How do you interpret them?

Process Evolution

As the AI noted:

The journey moves from anxiety and hierarchy → integration and construction → flow and systemic coherence and from questioning voice → assembling purpose → generating shared rhythm. It is a progression not just in strategic clarity, but in the increasing relational maturity of the group. The group stopped negotiating authority and started experiencing collective movement—a team ready not just to execute a plan, but to navigate complexity together with agility and mutual trust.

The “Big” Insight

In the right hands, the use of AI for data collection and analysis at each step of the strategic-planning process and to assist with the creation of the final plan can save a lot of time and create a lot of value. The drawback is that it increases the focus of the strategic-planning team on what the “cold,” hard data and facts might tell them.

In order to expose, include, and highlight the “warm,” soft data that is equally important in the process, we believe that the use of visual sensing will prove essential to counterbalance the “left-brain” dominance and tendency. Never forget that strategic planning is as much a human “change” experience as a rigid exercise in analyzing data.

Perhaps most importantly, the client was happy at the end of the process: The BU created a shared vision of where the team was going and a roadmap to get there and had attained a high level of buy-in from all participants. “Our hypothesis is that AI, the social arts, and strategic-planning exercises constitute a perfect strategic-planning triad.”

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Scenario planning is an essential part of any strategic planning exercise. After all, most strategic plans aim to position us—and give us a roadmap forward—for success in three to five years’ time. To do this task effectively, surely it is more important to “paint pictures” and gain a good sense of what those potential, plausible futures might look like, rather than analyze current or historical data.

Generally, CEOs do not need much convincing to undertake a SWOT analysis as part of their strategic planning exercise; however, the answer of “I can’t know until we’ve done it” is not the most convincing answer to the question, “What will we get out of doing scenario planning?”—which is frequently asked by CEOs. The significant time needed to invest in scenario planning can also function as a barrier for the sceptics resistant to committing to the process.

So, how can ChatGPT add value to the scenario planning process?

As a “case study,” I decided to use ChatGPT to help me travel quickly though the scenario planning process in order to explore how environmental issues affecting the Canadian mining industry might change in the next few years. My experiment focuses on mining operations in Canada as opposed to Canadian companies operating in other parts of the world, although many of the lessons apply to them too. I chose this sector because of my education and early work experience in the sector and also because some of the current archaic paradigms, managerial decisions, and actions in the industry—including resistance to paying remediation costs, exploitation of worker rights, and divide-and-conquer strategies used vis-à-vis Indigenous groups—continue to boggle my mind.
Although the case is centred on the Canadian mining industry, the learnings are universal and applicable to all resource-based industries in all countries. (Note: Throughout this article, ChatGPT’s contributions are in italics.)

These items would work as part of a checklist to ask, “How are we doing in each of these areas?” or “Which areas are we prioritizing or ignoring (group by green, yellow, or red)?” and would be a great opening icebreaker for a spirited dialogue among a management team.

In a real-life working session, the subsequent process of whittling down this list of factors to only two (to serve as axes for a Scenario Cross (the most simple and effective methodology for developing scenarios) could easily take an hour or more. With ChatGPT, it takes a few seconds. This is obviously very efficient; however, the rich debate required among a team to choose two forces from 20 is one of the major benefits of doing scenario planning. It forces participants to be open, to listen to and appreciate the views of others, and to access a different type of knowledge or wisdom—one that spending more time working on an Excel spreadsheet will not give them. Such a discussion advances a move towards a shared understanding and consensus and to building a unified team. It is lost if you allow ChatGPT to do the task.

When asked for the two most important forces, ChatGPT said:

ChatGPT ended this section with:

It is important to note that the mining industry’s impact on the environment is complex and multi-faceted, and addressing environmental issues will require a comprehensive and collaborative approach from all stakeholders involved.

Although all mining companies pay lip service to adopting a “comprehensive and collaborative approach with all stakeholders involved” in dealing with their environmental impacts, it is rarely sincere. The truth is that many make weak attempts at “greenwashing.” It is interesting to consider whether such a call for the industry to take a radically different approach from a third-party “bot” may have more impact than the same message from a well-meaning consultant or facilitator such as myself.

When asked the same question about the “social license to operate,” it generated this answer:

The minimum impact that social license to operate might have on the mining industry in Canada in thenext 3-5 years could be limited to incremental changes in stakeholder engagement practices and increasedreporting requirements. Companies may need to improve their communication and consultation withIndigenous communities, local residents, NGOs, and other stakeholders to address concerns related toenvironmental and social impacts, land rights, and resource sharing. This could result in some delays andadditional costs, but it may also improve relations with stakeholders and enhance the company’sreputation and brand image.

On the other hand, the maximum impact that social license to operate might have on the mining industryin Canada in the next 3-5 years could be much more significant, especially if there is widespread oppositionto mining projects, blockades, and legal challenges. Indigenous communities, in particular, may demandmore control and ownership over resource development, and may be more assertive in their opposition tomining projects that do not align with their values and priorities. This could lead to project delays, cancellations, and reputational damage for mining companies, as well as broader social and politicalconflicts.

In summary, the potential impact of social license to operate on the mining industry in Canada in the next 3-5 years could vary widely depending on the effectiveness of stakeholder engagement practices, the levelof trust and mutual understanding between mining companies and communities, and the broader politicaland social context in which mining takes place. Companies that are proactive in addressing social andenvironmental concerns, and that prioritize community engagement and consultation, may have acompetitive advantage in obtaining and maintaining social license to operate.

I find that the final summary paragraph sends a powerful message to the industry. Again, the questioncan be asked as to whether ChatGPT is a better channel for this message than I can be.

I then asked ChatGPT to generate four scenarios for the future of the mining industry in Canada using these two forces. In less than half a minute, it came back with:

“ChatGPT partially fell into the trap that many humans do when creating scenarios: it focused on company behaviour more than market dynamics.”

In the past, large consulting companies have answered requests for proposals from industry associations or government agencies to generate similar “generic” industry scenarios and have been generously compensated for it. This represents the worst of both worlds, as there is no real dialogue and it is a lengthy and expensive exercise. Hopefully, the emergence of AI will put an end to this practice.

Interestingly enough, ChatGPT partially fell into the trap that many humans do when creating scenarios: it focused on company behaviour more than market dynamics. However, I do believe the use of these four scenarios could enable a management team to have some incredibly rich, generative dialogue that they would not have if they did not undertake scenario planning.

The question as to whether it is more valuable to discuss the scenarios quickly generated by ChatGPT rather than have the conversation a team would in generating their own scenarios is a tough call to make. The jury is still out, I believe.

I will let you decide which of the above are currently being pursued as strategic priorities across Canada’s mining industry. We could all probably agree that if they’re not all being practised, they should be.

I proceeded to ask ChatGPT to suggest what all mining companies should definitely stop doing or not do in Canada in the next three years, given climate change and changes in “social license to operate” conditions.

For me, these messages were the “golden nuggets” from this exercise—and certainly recommendations that should or could be enacted across the entire natural resource sector in Canada or anywhere else.
Holding tough conversations about how a company is doing in these five areas would more than justify an entire scenario planning exercise.

Without the use of ChatGPT, leading a team to the point where you might put such topics on the table could take days of working sessions and individual coaching and possibly weeks of “deep dive” work. With ChatGPT, the entire exercise can be done in under five minutes, in real time, in front of them.
I then decided to “squeeze the lemon” one last time to see if I could extract some additional recommendations, ideas, and questions that would assist a mining company in addressing its environmental challenges.

In this mini case study of the mining industry, the ideas and recommendations generated by ChatGPT are much more a collection of environmental best practices for resource companies than an environmental strategy for any single mining company. Hence, for governments, industry associations, or companies seeking to improve their environmental business practices, the outcomes are a gold mine.

But as Michael Porter once said, “Benchmarking is not strategy.” The results suggest that ChatGPT will have its limitations in terms of developing unique, winning strategies for specific companies. Strategy is about being different and operating differently—and each company is unique. Similar advanced AI tools developed by companies for their own purposes may prove better at this, but based on my current assumptions, “the answers will always emerge in the room, not through the bot.” (Disclaimer: I believe that this is how it ought to be.)

Having completed the exercise, I realized that many of the “2023 Environmental Best Practices” that emerged for the mining industry would also be relevant in all sectors that exploit land—be they pulp and paper, forestry, agriculture, oil and gas companies or ministries of fisheries, transport, infrastructure development, energy, etc.—and that the outcomes are applicable in all countries. The exercise represents an impressive output with minimum investment.

Quick lessons learned on “collaborating” with AI/ChatGPT

Some deeper lessons

By doing this exercise, I believe that ChatGPT (and I?) have created a blueprint of best practices or key success factors for what a successful and sustainable mining company should follow. If you follow the blueprint, your company won’t maximize short-term profitability but it will reduce its risk and enhance its sustainability (in all senses of the word). Hopefully, at least one or two mining firms will give ChatGPT’s blueprint for the sector a closer look. Feedback would be much appreciated.

Finally, thank you, ChatGPT. I could not have done it without you!

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The area of strategizing and decision-making is identified as the one with the highest potential to benefit from using and leveraging AI—provided that high levels of “being human” are also utilized. I have already written an in-depth article2 demonstrating how generative AI tools can be used in the scenario planning process and explored the pros and cons of doing so. However, in light of the new HBR article, I thought it would be useful to explore what leveraging a high level of “being human” might entail.

First, we should highlight the possible negatives of being human that might be exacerbated through the use ofAI. For example, bringing our personal biases, fears, insecurities, selfish ambitions, personal enmities, jealousies, A.D.D., a tendency to empire build, and our ability to lie and deceive (others and ourselves) into ourinteractions with AI would probably best be avoided. Perhaps, over the long-term, new AI tools might serve todiminish or eliminate their impact on decision-making for the betterment of all.

To explore what a “high level of being human” might look like, I consulted four different generative AI bots. They all agree that there is an extensive list of human qualities, such as our emotions, sense of engagement, commitment and determination, personal ethics, integrity, judgment, personal values, our ability to empathize and be compassionate, deep listening, our passion, humour, sense of play, emotional intelligence, creativity, intuition, and feeling of connection, community, loyalty, trust, and friendship, that can all be important in strategizing, decision-making, and individual and team development. Interestingly enough, these are the elements of a human being that make each of us who (as opposed to what) we really are.

Crucially, most of the human attributes important in strategic decision-making—and that are not replaceable by AI—do not reside in our heads.

Yes, given all the right data, AI can come up with the perfect “winning” business model, but so can any welloiled team of experienced, technical strategists who have mastered all the models and concepts. AI can just do it quicker. However, many of us who have been doing this work for a while know that having a bot announce the next business model, process change, structural reorganization, or strategic “pivot” will probably have as much success as the boss who devises the perfect decision in their office and walks out to tell everyone the major change they’ve decided is going to happen.

In another recent HBR article4, the results from a major study of 300 large companies worldwide show that, even with all the big data, new tools, and management experience in 2024, only 12% of major change (Transformation) programs produce lasting results. Just imagine all the lost time, money, energy, waste of human resources, and the enormous frustration, opportunity cost, and value destruction that is captured behind that statistic.

This failure has little to do with the actual quality of the decisions that were made. Such post-change “transformation” visions are often well-thought-out —probably the equivalent of anything that an AI bot could generate. The weaknesses lie in the lack of consideration of the “human experience” of the change/transformation processes and the inhumanity that exists in our organizational cultures, processes, strategies, structures, and decision-making.

Those of you who know me are well aware that I believe all the “strategizing” and decision-making models and concepts are nothing more than specialized tools to facilitate generative, embodied conversations. It is where the alchemy of creativity and innovation, co-creation, commitment, engagement, and team-building can occur. This “Flow” alchemy can only occur when full-bodied mindfulness, presence, and awareness are present.

This last statement, my personal favourite, best captures the reason why I believe “embodied decisionmaking”, that is, seeking to incorporate mindfulness and social presencing—along with full-body “strategic play” activities and movement practices throughout a decision-making process—is essential if we want to improve our global (12%!) “fail” performance in “transformation” efforts. We need to have strategic decisionmaking processes that truly allow us to “embrace the full richness of human experience.”

As described in my previous article5, the following closing paragraphs (generated by ChatGPT) perfectly captures my thoughts:

Social Presencing Theatre (SPT) (and other movement-based creativity methods) is based on the understanding that the body holds wisdom that is often invisible and inaccessible to the conscious mind. By exploring, observing and experiencing emerging bodily positions, movements, gestures and postures, SPT allows people to access deeper levels of awareness, creativity, empathy and connection.

Using physical movement (in combination with traditional tools) combines mindfulness, embodiment, and social systems thinking to help individuals and groups better understand complex system-wide patterns and dynamics and, as a result, to develop new perspectives and innovative solutions. Most importantly, these solutions are more effective and sustainable than solutions derived using more conventional decision-making approaches.

Only by recognizing, building and using our “extended, looping mind” competency i.e., our interoception, exteroception, proprioception, and nociception “muscles”—and risking being laughed out of the boardroom – can we truly capitalize on both AI and those unique human qualities that allow transformation to emerge, be implemented, and stick.

Having completed the above article, I asked ChatGPT to generate three Action items for you as “take homes”. It generated:

In other words, bring more of your humanness (your entire being) to the workplace if you want to get the most from AI.

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