Why AI Is Topping The CEO Agenda: Predictions on the Largest Value Creation Opportunity in Enterprises

Insights from the World Economic Forum 2025 in Davos and beyond

Article by Chakri Gottemukkala , Co-Founder & CEO of o9 Solutions, Inc.

At the World Economic Forum 2025 in Davos last week, I had the opportunity to participate in several meetings with business executives, government leaders, think tank experts, and innovators in cutting-edge AI. The insights into advances in OpenAI and the sneak peek into the research at the Microsoft tent in Davos provided by Satya Nadella and his team of impressive Microsoft researchers were inspiring, to say the least.

AI in the Air at Davos

While last year’s Davos seemed to have a lot of hype around AI, it was clear this year that AI is front and center of CEO agendas at every large global company in a very real way. CEOs were clear that AI will have a major and transformative impact on every process and function of their businesses.

However, while there is a degree of uncertainty on the timeframes, 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 essential step. One CEO said: “Everyone needs to get a minor in AI.” As CEO of o9, I have a direct view of the state of digital transformations in hundreds of global companies. Also, as the driving force behind o9’s Digital Brain platform—transforming 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 at enterprises.

Can AI solve the complexity and change management challenges with decision-making silos—the biggest source of value leakage in enterprises?

According to ChatGPT, the human brain makes 25,000 to 35,000 decisions daily, which may be ChatGPT downplaying its rival’s capability :-).

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 end to end operations of the business. These include supply chain decisions, product decisions, commercial decisions, financial decisions, HR decisions, short lead time decisions and longer lead time decisions, low value impact to very high value impact decisions.

But to handle the complexity and scale of large enterprises, over the years the decision-making has been distributed across functional domains like supply chain, commercial, product and finance organizations, and across processes such as long range, annual, tactical, operational to real time planning and decisioning.

o9 Solutions, Inc. was founded with the premise that the largest value leakage in enterprises is due to the fact that these functional and process silos are causing those millions of atomic decisions to be too slow, often sub-optimal and poorly synchronized relative to the needs of a business environment that is getting more complex, and more volatile than ever before.

With o9’s Digital Brain platform, over the last decade, we have been driving significant improvements in end-to-end planning. We connect all the silos on an integrated planning platform—bringing sales, supply chain, and finance functions together in planning processes. We offer more automated forecasting, touchless planning, and order generation in the operational horizon (next few days, weeks) and more robust cross-functional scenario planning for tactical (next 12-18 months) and strategic horizons (next 2-5, 10 years). This has proven to be a major value unlock for many large enterprises, realizing hundreds of millions of dollars of incremental value/year.

However, there is a constraint to realizing this large value—the inherent complexity of the problem and the associated change management challenge in large enterprises. Implementation of the solutions requires knowledgeable consulting and enterprise resources. Driving value requires expert leadership to drive the adoption of new cross-functional processes and capabilities. Sustaining the value means well-informed business processes and technology resources are needed to evolve the capabilities as business models and strategies evolve.

Can AI overcome knowledge constraints to address complexity and transform siloed decision-making in large enterprises?

The rapid advancements in Generative AI—and more recently, Agentic AI—pioneered by organizations like OpenAI and others, alongside insights from our research into adopting AI in enterprise contexts, have made me a true believer. A gamechanging solution to the scale, complexity, and change management challenges of large enterprise systems is now within reach. So, with that I will share my predictions.

While the accuracy of these predictions remains to be seen, I believe they will materialize to varying degrees over the next 3–5 years, driving significant value creation. I advise CEOs and their executive teams to stay cognizant of these possibilities and assess their strategies and initiatives accordingly.

A management platform for intelligent, integrated planning and decision-making, powered by digitized knowledge models and AI agents, will emerge. It will become the greatest source of value creation and competitive advantage for large enterprises.

The management platform will have the following key characteristics:

1. Tribal knowledge will be converted into digital knowledge at scale

Scattered data and tribal knowledge of organizations will get organized into digital knowledge models that develop exponentially. Companies will compete on the strengths of their digital knowledge models.

2. Leaner, more effective management structures will emerge, supported by AI agents with complex analysis and actioning skills

AI agents operating on digitized knowledge models will support leaner management organizations in doing complex post-game and scenario analysis, recommending decisions, and driving alignment on high-value tactical and strategic decisions. Short-term, operational decision-making will be increasingly automated, with agents driving synchronized decisions and automated execution across systems.

3. Capability innovation will become faster supported by skilled AI agents, enabling business strategies to be implemented more effectively

AI agents that can help reconfigure processes and develop and evolve new system capabilities to match evolving business strategies will be the biggest differentiator. Flexible platforms that enable such rapid evolution will be key.

With o9’s Digital Brain platform, over the last decade, we have been driving significant value by connecting all the planning and decisioning silos onto one platform. There is a problem to be solved that can unlock more value: The problem of “tribal knowledge.”

Tribal knowledge will be converted into digital knowledge at scale.

Data shows high 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 system.

Expertise and knowledge to perform the analysis and ability to tell the story and convince organizations to make crucial decisions are not consistent.

For organizations that do not have a platform like the o9 Digital Brain, the problems only worsen. 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% higher, and can the supply chain support the incremental demand? And at what incremental cost? Or on an operational level, if there is a supply chain risk developing due to a supply disruption, and you ask—which demand from which markets and customers is most impacted and to what degree? If we are to allocate, which demand is more risky and which is more reliable? Are there alternative demand-shaping actions to mitigate risks?

It takes many people, with their tribal knowledge of supply chain, sales, marketing, new product innovation, and finance, to come together and find the answers. Depending on the knowledge levels and expertise of the people, the solution may or may not arrive in time and with the right level of financial analysis and depth. The variability in outcomes due to disparity in tribal knowledge and expertise can be addressed with AI. Generative AI has proven that it can ingest and digitize the knowledge of all the writing in the world. As it is digitized knowledge, it has been proven that LLMs grow exponentially in power.

In contrast, in enterprises, as described before, most decision-making is supported by tribal knowledge. With tribal knowledge, the 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, combined with technologies like the o9 Knowledge Graph models, we see big potential to organize all scattered data. This enables the conversion of tribal knowledge into digitized knowledge, which decision-making models can then access.

Your company’s unique market domain knowledge—about products, markets, customer segments, and the sensitivity of demand forecasts to various drivers—is digitized. Supply chain domain knowledge about suppliers, manufacturing and logistics resources, capacities, and constraints is also digitized.

This knowledge constantly improves based on learnings from daily decisions and measuring expected outcomes versus actuals. Agents can then use it to drive powerful analyses and scenarios in a more prescriptive and automated fashion.

I will be writing more on this topic, but here is the call to action to all CXOs—challenge your organizations to accelerate the 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% tribal to 80% digitized knowledge in 2 years or less.

Enterprises of the future will compete against each other based on the quality of the “Digital Knowledge Models” driving their processes.

Leaner, more effective management structures will take shape, supported by AI agents with complex analysis and actioning skills.

By combining the powers of LLM-powered Generative AI with o9’s “Enterprise Knowledge Graph” models, digital AI agents can be trained to perform complex tasks that answer typical management questions. We call these the 3Ws:

1. What happened relative to plan and why?

2. What is likely to happen? What is the baseline based on current conditions?

3. 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 scenario analysis (what actions to take to improve the plan?). They can be trained to create unbiased, succinct management summaries as the company's best expert would do to improve chances of alignment and actions.

AI agents, given they are digital, can perform these tasks across broader spans of control, i.e., across functional domains and a greater number of products, markets, channels, and supply chain segments. Clearly, the roles and spans of managers and planners in leaner, flatter management structures will evolve, and this will drive a degree of understandable apprehension.

This is where CEOs have a crucial role to play in getting the organization AI-educated and embracing change.

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 a broader analysis of market risks and opportunities, devise business strategies, and take faster market-impacting actions to support said strategies.

Now, this brings us to the last and most important characteristic of the management systems of the future

Capability innovation will become faster with skilled AI agents, enabling business strategies to be implemented more effectively.

The complexity and scale of large enterprises and the silos has meant that change is challenging. 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 strategy is hampered by the changeresistant silos. In the past, companies have filled the gap by throwing more and more people and manual processes at the problem to execute the strategy. This results in silos, spreadsheets, tribal knowledge, and value leakage. In the digital and AI age, as the business environment becomes more competitive and dynamic, management's ability 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 configure and extend flexible platforms like o9 will be key.

In the long run, this ability to constantly innovate capabilities to match the needs of the business strategy will be the ultimate differentiator for leadership.

We are at a pivotal moment in the evolution of enterprise management.

To CEOs and their executive teams, here is my simple call—if you are not already thinking about it, it is high time.

Prioritize AI-powered transformation of your management system with the above characteristics. It is likely to be the #1 driver of competitiveness and value creation for enterprises in the coming future.

Best Regards,

Chakri Gottemukkala

This article was originally published at o9solutions.com

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