The strategist's dilemma
According to Forbes, the amount of data created in the last two years is more than 90 percent of the world's total data combined. Furthermore, IDC Global DataSphere forecasted in May 2020 that more than 59 zettabytes of data would be created, captured, copied and consumed in the world by the end of the year. A zettabyte is one sextillion (1,000,000,000,000,000,000,000) bytes, which by all accounts is a lot of data. The ability to process and assess important insights from this data will be the competitive edge in the future. While, in many cases, organizations have embraced the use of data, they are only scratching the surface of its potential. Many organizational decision-makers are unaware of analytical advances that allow them to make predictive rather than descriptive use of their collected data.
For business leaders and strategy professionals alike, making sense of all this data and information at a time of heightened complexity and uncertainty caused by COVID-19 presents an evident and immense practical problem to remain competitive. New solutions are needed as traditional tools to explore the external environment, like scenario planning, reach their limitation of usefulness.
Scenario planning is a structured way for businesses to explore and think about the future. Leaders develop a set of possible scenarios that explain different stories about how the future might unfold. Scenario planning involves many alternative constructs such as thinking, forecasting, analysis, learning, strategic flexibility, mental models, options and more. Scenarios are tools to research our understanding of the external world, understand drivers and dynamics more purposefully, and think through 'possible realities.'
Any good scenario and resulting strategy have a foundation built on data, which comes at a cost; the ability to properly assess the external market research data and internal company data, with the limiting factor being human resource understanding, capacity and capability. There are limited examples focused on the connection of scenario planning to Artificial Intelligence to solve this data gathering, processing and analysis problem.
Strategy professionals are left performing very 'manual' research on the web and through other sources to get what they need in today's context. These experts typically source information through competitors' annual reports, look for external risks and threats and assess social media to identify the early signs of something emerging that needs attention. They also combine data related to economics in the market to formulate trends and monitor political dynamics to understand policy implications. Further to these data sourcing challenges, limited routine tools and templates exist. The processing and analysis process lack automation. With a rapidly evolving environment, the playing field is constantly changing and with a lack of automation, the challenge is compounded.
The business opportunity
AI is a tool perfectly positioned towards understanding uncertainty. AI has the potential to transform these traditional methods with broader applications to strategy management. Could new AI technologies be our solution to the strategy professional's data gathering, processing and analysis problem within the scenario planning process?
Some view strategy-related processes as too 'fuzzy,' more art than science, executives' purview, and therefore not appropriate for AI and other technology applications. Since strategic processes require experience, judgement and intuition, qualitative human-driven data gathering methods must prevail. The consequence of not addressing this problem is an ever-increasing feeling of being overwhelmed by the increasing volume of data and information that does not get looked at. External risks and threats get missed, insights overlooked, and strategies that get tested against inadequate scenarios that don't fully consider what's fundamental and essential in the complex and uncertain world.
Those who embrace this relatively new technology to uncover new insights in the external data create a true competitive advantage.
A new collective intelligence model
The complete end-to-end scenario planning process does not lend itself to complete AI adoption. However, specific steps requiring data-heavy analysis could benefit from AI's power.
Figure 1 – Collective Intelligence Model for Scenario Planning (Mortlock, 2020)
There is also an obvious logic for the steps proposed as applicable to be AI-driven or hybrid. These steps are operational, repeatable and routine in nature involving specific input sources, logic to identify risks, threats, trends and uncertainties that can be programmed. Within these steps, AI's routines to gather and assess the data can be programmed with consistency. The selected steps don't involve judgment, decisions, reasoning or other human elements.
The labour-intensive and manual activities within scenario planning that involve large amounts of data are great candidates for AI, ML and NLP solutions. AI tools augment the existing workforce, driving better and faster decision-making, leveraging the vast amounts of data and analysis required to glean game-changing insights.
Where to from here?
Uncertainty and complexity are always present, particularly now with COVID-19. Several steps are involved in producing a robust scenario plan as part of a broader strategic planning process—engagement amongst stakeholders, negotiation, communication, research, dialogue with leaders, tinkering as gaps are identified, developing strategies etc. The scenario planning process is further complicated by considering scenarios that involve an ever-changing external environment. The challenge facing both the strategic planner and broader leaders is to drive value from such a process at the speed, cost, quality, and capability required.
Despite these challenges, AI scenario planning will likely play a more critical role in the future as a new frontier in optimizing the processing power of data and information for strategic purposes takes shape. As organizations overcome perceived usefulness and ease of use barriers, AI in scenario planning is likely to increase, helping make sense of growing data and information. In the future, we will likely see changing attitudes and intentions involving a greater emphasis on AI. This evolution in thinking will further the value that scenario planning can bring by expanding the limitations of humans in the face of external uncertainty and internal complexity.
In the short term, AI can augment human decision-making, particularly given human understanding, capability and capacity limits, but not replace it. AI is still limited in explaining the reasoning process—why a decision was made a certain way related to the construction and application of scenarios as an example. Besides, humans learn over time, through experience and experimentation, to make better decisions. Gradually, AI systems are learning to do the same. When thinking about the potential application, it's essential to clearly understand the extent to which AI can replace, rather than enhance, the process. It's vital to consider the subtle shifts in decision-making, both big and strategic, small and tactical. When it comes to activities and decisions involved in scenario planning, for now, AI is at its best when playing an overall supporting role, with 'driving' emphasis on certain parts of the process that are data-heavy and 'hybrid' emphasis on other parts of the process that require a mix of human and AI capabilities.
In the long term, AI scenario planning will likely play a more critical role in optimizing data and information processing power. AI technologies will potentially create new and more insightful scenario-related models to deal with external forces. Furthermore, AI in the future has the potential to support strategic decisions in real-time, generate actual scenarios without human input, and offer more unique and better internal and external applications to predict.
In summary, the good news is that humans won't be replaced in the scenario planning process any time soon. Still, fusion skills will emerge where AI and humans work together in a more integrated (hybrid) way, enhancing the enterprise's overall capability. As Andrew Hill pointed out in a 2019 Financial Times article, "the human strategist is still required, precisely to decide when and why to deviate from the strategy framework, whether that frame is built by a committee of people or a highly intelligent machine."
Authored by Lance Mortlock (Senior EY Strategy Partner & Haskayne School of Business Visiting Professor).