Three ways AI is shaping growth planning in 2026

AI for competitive advantage

To achieve this AI competitive advantage, many companies are continuing to invest substantially in AI technologies. Over the next year, I see AI applications taking shape across three main areas: customer experience, transactional processes and planning/analytics.

An example of this would be using AI to enhance the customer experience, which could include personalised product recommendations tailored to each customer based on prior purchases.

While lower-risk AI applications like automating product recommendations can positively impact brand sales, there are still potential risks (e.g. hallucinations or output inaccuracies) that can occur when AI is applied to more complex transactions or planning processes where accuracy is critical.

However, the bigger long-term risk that companies face is opting not to prioritise AI and determining which strategies and applications can be used not only in their RGM processes, but across their business. The businesses that don’t digitise their operations and planning processes ultimately risk losing competitive advantage (and eventually market share) to the companies that do invest in AI technologies and progress in their digital transformation initiatives.

While it will likely take organisations a couple of years to develop a strong foundation of digital knowledge, once companies are able to transform traditionally analogue business processes (like commercial planning and revenue optimisation) by digitising them at scale, they could begin to see 30% to 50% productivity benefits. The companies that are taking a wait-and-see approach to investing in AI technologies and digital transformation are likely to remain on the sidelines, instead of becoming the key players in their industry.

Economic headwinds

Ben-Yousef: Build out robust knowledge models to capture siloed internal data & insights for planning.

Another trend that I see shaping up in 2026 is retailers and CPG companies continuing to manage potential economic headwinds and supply chain disruptions. With this in mind, companies will need to pull the right combination of pricing, promotion and assortment levers and will likely need to make strategic adjustments throughout the year. I think what business leaders are waking up to now is the huge productivity benefit from being able to apply AI and agentic AI to commercial planning and revenue management processes. 

For example, with this technology, planning teams would have greater visibility into the end-to-end supply chain and be alerted to a potential supply chain risk with enough time to simulate various scenario planning outcomes and make real-time adjustments in planning and decision-making to address short-term supply chain disruptions or changes in consumer demand. 

As agentic AI continues to make advancements, planning teams will be able to automate aspects of the planning process, which would allow them to spend more time focusing on strategic growth initiatives and commercial planning priorities. 

But it’s important to realise that before companies can leverage the full benefits of agentic AI, they first need to focus on building out robust knowledge models that will help capture siloed internal data and insights that will help transform their planning capabilities.

Build developer & data foundations

Over the next year, I think more organisations are going to allocate more resources towards digitising internal processes and developing the fundamental knowledge models that will drive forthcoming investment in agentic AI.

A critical aspect in this process is rethinking what comprises an organisation’s unique enterprise data and how that is fed into the software application development lifecycle. Traditionally, most of us think of enterprise data as data tables filled with sales and transactional information that tell us how many orders were placed or how many production runs were initiated over the past month, quarter, or year. While this is necessary internal data, much more contextual data from across the business needs to be connected and digitised to make the strategic growth and commercial planning processes a fully integrated, modernised approach that moves the business forward.

One of the first steps of building a robust knowledge model that is powered by large language models (LLMs) is to augment the LLM with the company-specific terminology and glossaries, so that the data and information used in queries are standardised across the business and properly interpreted and understood by the LLMs to reduce the chance of hallucinations.

Over time, as the model takes in additional information and new glossaries, the model will develop a more powerful knowledge base as it continually learns.

Contextual expertise 

This digitisation process can capture not only the data stored in spreadsheets but also the contextual expertise of a planning team’s workforce. An example of this would be providing the knowledge model with detailed workflows of a commercial plan, structured so that the model could extract what planning timelines and dates specific activities will take place and what activities align with formally approved objectives and budgets. 

When a knowledge model is given access to this information, it can become structured knowledge that is accessible across an organisation. This allows teams to rely on a unified, single source of truth that provides the necessary insights to improve collaboration across teams and can enhance scenario planning when navigating potential supply chain disruptions.

As companies in the retail and CPG industries look ahead to the new year, in addition to managing the potential supply chain and business uncertainties and risks that emerge, there is also plenty of opportunity to integrate technologies into the strategic planning process and build a foundation for continued digital transformation in the years to come.