Artificial intelligence is profoundly reshaping business planning. From demand management to resource optimization and scenario simulation, it has become a key lever to enhance decision-making responsiveness and reliability. A revolution that Beyond Plans aims to make available to its clients under the best possible conditions.
During a recent webinar organized by Anaplan and NVIDIA, industry experts highlighted the major advances of AI applied to business planning. The debate confirmed Beyond Plans’ observation regarding the profound transformations applied to traditional methods. It also showcased the emergence of generative AI and advanced predictive models to support decision-makers in their strategic choices.
But how do these innovations concretely integrate into planning processes? What benefits do they bring to companies? And what challenges still need to be overcome?
AI as a Planning Accelerator: From Analysis to Optimization
Traditionally, companies rely on planning tools based on statistical models and empirical projections. While this approach is robust, it reaches its limits when faced with rapid market changes, supply chain disruptions, and shifting consumer behaviors.
AI brings a new dynamic by enabling the analysis of vast amounts of data in record time. It no longer merely aggregates and structures information: it proposes optimized scenarios and anticipates risks with increased accuracy.
A concrete example presented during the webinar involves optimizing customer allocations. Thanks to an advanced AI engine, the demand segmentation process saw its calculation time reduced from several hours to less than 30 minutes. This time saving opens up new perspectives: companies can now test multiple hypotheses in real time and refine their strategies more responsively.
Beyond mere data processing, AI can also formulate personalized recommendations based on the company’s strategic objectives. This evolution marks a decisive turning point: we are no longer talking about improved reporting but rather a true co-pilot for decision-making.
Towards Intelligent Automation: Balancing Trust and Gradual Adoption
While AI holds great promise, its adoption relies on one essential factor: user trust. A major obstacle remains the explainability of recommendations generated by these tools. Planners and decision-makers need to understand how suggestions are formulated to validate and integrate them into their processes.
One approach highlighted in the webinar and endorsed by Beyond Plans is to introduce AI gradually. Instead of fully replacing human intervention, companies should favor a human-machine collaboration where AI acts as an assistant. Users retain control over final decisions, while the algorithm accelerates and refines the analysis.
This approach helps reassure teams and facilitates the adoption of AI tools. An effective method is to run AI in parallel with existing processes to compare the results obtained with those from traditional methods. When a planner sees that AI can replicate their decisions, or even anticipate them with greater precision, it becomes easier to trust and adapt their way of working.
The webinar also emphasized the need to adapt AI models to the specificities of each company. Far from being universal solutions, these tools must align with each organization’s constraints: data structures, business rules, performance indicators… The challenge is therefore both technological and organizational.
The Future of AI in Business: Towards Increased System Autonomy?
While AI currently plays mainly a co-pilot role in planning, the rapid evolution of technologies suggests a future where it could occupy a more central position. The trend points to increasingly advanced automation, where systems will no longer just support decision-making but will be able to independently handle some actions.
This does not mean that planners will become obsolete. Their role will evolve towards more supervision and strategic analysis, relying on tools capable of processing increasingly complex variables.
However, several challenges remain. One major issue is risk management and the robustness of AI models when faced with unforeseen events. While AI can optimize processes based on past trends and weak signals, it remains imperfect when it comes to anticipating unprecedented crises.
Another key aspect concerns ethics and data security. The growing integration of AI into strategic processes raises crucial questions about information confidentiality and algorithm transparency. Companies must implement safeguards to ensure the responsible use of these tools.
