AI First Business Transformations (Opening Keynote @ aim10x digital 2025)

Hello, everyone. Welcome to AIM 10x Digital. As you know, AIM 10x is an innovation mantra. Every process, every function, every activity can be made 10x better through innovation. It's also a play on AI-powered management and its power to drive 10x innovation in management capabilities. I was at the World Economic Forum 2025 in Davos earlier this year, where I had the opportunity to participate in a number of meetings with business executives, government leaders, think tank experts, and innovators in cutting-edge AI. One thing was clear: AI was in the air. CEOs were very clear that it's going to have a major and transformative impact on every process and function of their businesses. But while there's a degree of uncertainty on the time frames, they indicated that upskilling every person across their company on the usage of AI for every process, function, and task is going to be an important step. One CEO even said, everyone needs to get a minor in AI.

As CEO of o9 Solutions, having a direct view into the state of digital transformations in hundreds of global companies and as the driving force behind o9's Digital Brain platform to transform end-to-end planning and decision-making, I thought I would make some predictions on how AI is going to shape the future of management in enterprises. First, what's the problem to solve? Let me frame it this way. Can AI solve the complexity and change management challenge of what we see as the root cause of the largest value leakage in enterprises: the silos of decision-making? Let me elaborate a bit. According to ChatGPT, the human brain makes 25,000 to 35,000 decisions on a daily basis. That may be ChatGPT downplaying its rival's capability, quite frankly. In contrast, a typical large enterprise selling large product portfolios in multiple markets to different customer segments and operating a global multi-tier supply chain to meet the needs of those customers is typically making hundreds of millions of atomic decisions every day across the enterprise. These decisions include supply chain decisions, product decisions, commercial decisions, financial decisions, HR decisions. They include short lead-time decisions and longer lead-time decisions. They include low-value-impact decisions to high-value-impact decisions. But to handle the complexity and scale of large enterprises, over the years, the decision-making of all these hundreds of millions of decisions has been distributed across functional domains like supply chain, commercial, product, and finance organizations, and across processes such as long-range planning, annual planning, tactical planning, operational planning, to real-time decisioning processes.

o9 was founded with the premise that the largest value leakage in enterprises is that dysfunctional and process silos are causing decisions to be too slow, suboptimal, and poorly synchronized relative to the needs of a business environment that's getting more complex and more volatile than ever before. With o9's Digital Brain platform, over the last decade, we've been driving significant improvements in this, connecting all the silos on an integrated planning platform, bringing sales, supply chain, and finance functions together in planning processes with more automated forecasting, touchless planning, order generation in the operational horizon for the next few days and weeks, and more robust cross-functional scenario planning for the tactical horizon (the next 12 to 18 months), and even strategic horizons (the next 2 to 5 to 10 years). And this has proven to be a major value unlock for many large enterprises, realizing hundreds of millions of dollars of incremental value per year. However, there's a constraint to realizing this large value: the complexity of the problem and the associated change management challenge in large enterprises. Implementation of these solutions requires knowledgeable consulting and enterprise resources. Driving value requires knowledgeable leadership, knowledgeable planning organizations to drive adoption of new cross-functional processes and capabilities. And sustaining the value means knowledgeable business process and technology resources are needed to evolve the capabilities as business models and strategies evolve. Can AI help solve this knowledge constraint to tackle the problem of complexity and change management in transforming siloed decision-making in large enterprises? The rapid advances in not just generative AI, but what is being termed as agentic AI that we are seeing from the likes of OpenAI and others, and what our own research at o9 is showing as to the possibilities to adopt AI into the enterprise context, has made me much more of a believer. A game-changing solution to the scale, complexity, and change management problem of large enterprise management systems is actually at hand.

So with that, I'll share my predictions. How far these predictions will come true remains to be seen, but I believe they're going to happen to varying degrees in the next three to five years with tremendous value creation impact. I'm advising CEOs and their executive teams to be cognizant of these possibilities and evaluate their strategies and initiatives accordingly. A game-changing management platform for intelligent integrated planning and decision-making, supported by digital knowledge models and AI agents, will emerge, becoming the biggest source of value creation and competitive edge in large enterprises. This management system will have the following three key characteristics. First is what I call tribal to digital knowledge conversion at scale. With o9's Digital Brain platform, over the last decade, we've been driving significant value by connecting all the planning and decisioning silos onto one platform. But there's a significant problem to be solved that can unlock more value: the problem of tribal knowledge. Data is showing that there's a lot of variability in outcomes achieved based on the expertise of the planners and the managers in the decision-making loop. Even when equipped with the same information and reports from the systems, expertise and knowledge to perform the analysis and the ability to tell the story and convince organizations to make important decisions is not consistent. And for organizations that do not have a platform like o9's Digital Brain platform, the problems are worse.

For example, as a business unit owner, if you ask questions like: Why did we miss the forecast for product X in market Y last month? What commercial actions can help increase demand for product X in Q3 to be 10 percent higher? And by the way, can the supply chain support that incremental demand, and at what incremental cost? Or at a very operational level, if there's a supply chain risk developing due to a supply disruption, and you ask: Which demand from which markets and which customers is impacted, and to what degree? If you are to allocate, which demand is more risky and which one is more reliable? And are there alternative demand-shaping actions to mitigate risk? It takes many people with their tribal knowledge of supply chain, sales, marketing, new product innovation, and finance to come together to answer these questions. And depending on the knowledge levels and expertise of the people, the answer may or may not come in time and with the right level of financial analysis and depth. This variability in outcomes due to tribal knowledge and expertise disparity can be addressed with AI. Generative AI has proven that it can ingest and digest the knowledge of all the writing in the world. And because it is digitized knowledge, it has proven that LLMs and the knowledge can grow exponentially in power. In contrast, in enterprises, as described before, most decision-making is supported by tribal knowledge. And tribal knowledge has a very bad property: dots don't get connected across silos, and it dissipates as people change roles or organizations. Using the power of generative AI and agentic AI, and combining with technologies like o9 knowledge graph models, we see big potential to organize all scattered data and convert tribal knowledge into digitized knowledge that can be accessed by decision-making models. Your company's unique market domain knowledge, knowledge about products, markets, customer segments, sensitivity of demand forecasts to various drivers, supply chain domain knowledge, knowledge about suppliers, manufacturing and logistics resources, capacities, and constraints can be digitized. And this knowledge is constantly improving based on learnings from daily decisions and measuring expected outcomes versus actuals. It can then be used by the agents to drive powerful analysis and scenarios in a more prescriptive and automated fashion.

Here's a call to action to all CXOs. Challenge your organizations to accelerate digitization of expertise and tribal knowledge of key functions and processes in customer-facing, planning, supply chain, commercial, and product innovation domains. Set goals of moving from 80 percent tribal to 80 percent digitized knowledge in two years or less. Enterprises of the future will compete against each other based on the quality of the digital knowledge models driving their processes. More efficient, more effective management structures will evolve, supported by AI agents with complex analysis and actioning skills. By combining the powers of LLM-powered generative AI and agentic AI with o9's enterprise knowledge graph models, digital AI agents can be trained to perform complex tasks that answer the typical management questions that we call the three W's. In any decision domain, the typical management questions are: Looking backward to the last periods, what happened? What were the surprises related to the plan and why? And looking forward, what's likely to happen? What's the baseline forecast or plan based on current conditions? And third, what other actions can be taken to close the gap or improve the plan? AI agents can be trained to do performance post-games (what happened and why), baseline forecasting and planning (what is likely to happen), and cross-functional scenario planning (what cross-functional actions to take to improve the plan). They can be trained to create unbiased management summaries in succinct ways as the best expert in the company would do, to improve chances of alignment and action. And the AI agents, given their digital nature, can perform these tasks across broader spans of control, across functional domains, and a greater number of products, markets, channels, supply chain segments, etcetera. And in the shorter horizon, for operational decisions related to responses to customer orders, quotes around pricing and availability, inventory deployment, production scheduling, purchase orders, AI agents can help make decisions in a touchless automated fashion. And with every cycle of decisions made, the agents can learn and improve the policies that drive the next set of automation decisions, making them much better and much more intelligently.

Clearly, the roles of managers and planners in this management structure will have to evolve, and this will drive a degree of understandable apprehension. This is where CEOs and business executives have a crucial role to play in getting the organization to be AI-educated and embracing the technology as a way to innovate and drive value versus being seen with apprehension. Done right, the change can be extremely positive as it frees up managers and planners to perform much higher-value-creating tasks for the enterprise. It will enable them to pull together broader analysis of market risks and opportunities, devise business strategies, and take faster market-impacting actions to support said strategies. And that brings us to the most important characteristic of the management systems of the future. Capability innovation has to keep pace with business model changes, and that will be made easier with AI agents. The complexity and scale of large enterprises and the silos has meant that change in enterprises is very hard. Even making simple changes to processes, systems, and driving adoption across the organization is not easy. So when a business needs to execute a new innovative strategy to drive growth, the organization's ability to evolve internal capabilities to support the said strategy is hampered by the change-resistant silos. In the past, companies have filled the gap by throwing more and more people, spreadsheets, and manual processes at the problem to execute the strategy. And that's what results in silos, spreadsheets, tribal knowledge, and value leakage growth. In the digital and AI age, as the business environment becomes more competitive and dynamic, the ability for management to devise innovative strategies for maintaining competitive differentiation is a must. AI is proving to be more and more adept at tasks like coding and configuring systems to develop new capabilities. LLM-powered AI agents that can configure and extend flexible platforms like o9 are going to be key. And in the long run, this ability to constantly innovate capabilities to match the needs of the business strategy is going to be the ultimate differentiator for management.

It's clear that we are at a pivotal moment in the evolution of enterprise management. Executive teams, here's my simple call: If you're not already thinking about it, it is high time. Prioritize AI-powered transformation of your management systems. It is likely to be the number one driver of competitiveness and value creation for enterprises in the coming future.