Predictions are big business. The market for predictive analytics is expected to reach $12.4 Billion by 2022 as Artificial Intelligence (AI) becomes more widespread in Demand Planning organizations. But we’re not losing our jobs – don’t worry – because even though a lot of the analytical work will be automated, new roles are emerging to implement and manage AI, and interpret the findings. Here are the four types of AI in Demand Planning, and the new roles required to leverage them.
1. Limited Memory AI
Today, most Limited Memory Artificial Intelligence (AI) applications rely on an age-old premise: use vast stores of historical data together with current observations to predict future outcomes. A Demand Planner calculating the base forecast on a product is likely to use past sales to trend as a starting point. Depending on the number of products in a portfolio, this task could be executed efficiently without the use of AI applications, say for instance in a small enterprise. However, for large organisations such as multinationals, Limited Memory AI could execute this task with speed and accuracy providing live or on demand recommendations for review by the Demand Planner.
For any organization, investments in technology must provide a Return on Investment, with any decisions on AI implementation first requiring an extensive review of data stores to ensure they meet the required standard. Garbage in garbage out still applies and if you want quality data, you need a human.
Working In Reactive AI: Demand Planners with an interest in the technical aspects of the role may specialize as Demand Planning Scientists with a focus on Data Management and algorithm development, optimisation and maintenance. This function would support the initial preparation of data, selection and fine-tuning of the organization’s specific algorithms.
2. Reactive AI
Reactive AI, a second type of application, could also support Demand Planners. Scenario Planning and one-off decisions, which combines multiple information sources together, benefits launch and promotional planning where historical trending is not possible. Reliability of this proposition would depend on the extent of connectivity between the organization and it’s customers or key partners. Inputs could be drawn virtually from various stakeholders when scenarios are triggered as part of automated workflow processes.
Working In Reactive AI: Demand Planners could specialize in this area as Launch & Promo Planners. Successful collaboration with other departments has always been a differentiator of top talent in this area. Weak AI deployed in Limited Memory and Reactive applications do not offer direct substitutes for this aspect of human capability.
3. Theory Of Mind AI
Developed social characteristics of human communication and trust have proven difficult to replicate in AI. The Theory of Mind category of these applications deliver outcomes based on predictions of how people will behave within a given environment through perception of that environment and those within it. Where technological advancement of this type enables AI to read body language and interpret non-verbal cues to inform decision-making, opportunity may arise in collaborative forecasting processes.
Working With Theory Of Mind AI: AI assistants trained to work with teams may be well placed to provide a second opinion. Managerial and strategic skills would enable Demand Planners to thrive in such scenarios. Those individuals with the required capabilities may be qualified to be promoted to Demand Manager, overseeing AI decision recommendations, validating them and making the last call.
4. Self-Aware AI
AI in the fourth category is differentiated from the others based on its self-awareness capability, which draws on internal concepts of feelings and uses these additionally in the outcomes produced. This type of AI does not exist in the full capacity of that category definition, although strides are being made. To replace Demand Planners, AI would need those skills and more. Instinct, intuition, creativity to generate new ideas, social energy and charisma to influence are characteristics humans themselves are unable to quantify – far less replicate. These allow new ways of doing things and decisions to be taken outside of information that is available or could be generated. These include, for example, unprecedented events and rapid changes in consumer tastes including those for food or emerging fashion trends.
Working With Self-Aware AI: There’s is no new Demand Planner role for the type of AI (yet, anyway). That’s because in these circumstances, data does not usually exist. In professions where errors translate into costs such as lost sales and inventory builds, AI would excel in driving quality of information, automating mundane tasks, providing secondary opinions and recommendations. It could succeed under supervision, which would create a new Demand Planning role. How much time is committed to this would determine the viability of the application and the role. For those aspects of human nature, which enable social interaction, AI would fail and create a void in organizational cultures that could be detrimental to creativity, engagement and performance.
[For jobs in Demand Planning, Predictive Analytics and S&OP, check out IBF’s jobs board]