Open Table Insights: How can AI boost agrifood performance?

Some of the most valuable conversations at Foodtech Forum 2026 happened around the table. Through six Open Table Discussions, participants met in smaller groups to dig deeper into some of the questions shaping the future of food, from AI and circularity to scaling, investment and new business models.

One of these conversations, Harvesting Intelligence, explored how AI can improve performance across the agrifood sector, and what companies need to consider as the technology becomes increasingly integrated into their businesses. Here are some of the key insights from the discussion, led by Måns Ullerstam, CEO & General Partner at Kale United and Chair of the Board at Grönare Sverige.

Summary

The discussion explored how artificial intelligence can improve performance across the agrifood value chain, from product development and business operations to production and decision-making. Participants discussed both the opportunities AI creates for foodtech companies and the challenges related to data ownership, workforce transformation, inequality and environmental impact.

A central takeaway was that many companies are experimenting with AI but have yet to develop a clear strategy for integrating it into their business. Rather than attempting a large-scale transformation from the outset, companies should start with smaller, practical applications, learn from the results and gradually build AI capabilities that create long-term value.

1. From experimenting with AI to integrating it into the business

Many companies are currently exploring AI tools, testing different models and allowing employees to experiment in their daily work. While this is a useful first step, the discussion highlighted the need to move beyond general experimentation towards more strategic applications.

Three stages of AI adoption emerged:

1. Learning and exploration: Becoming familiar with AI tools and understanding their capabilities.
2. Improving existing processes: Using AI as a practical assistant to save time, reduce costs and improve activities such as R&D, marketing and administration.
3. Integrating AI into the business: Embedding AI into workflows, software and core processes to generate lasting improvements and build company-specific data capabilities.

The third stage offers the potential for a more sustainable competitive advantage. Companies should consider how they can accumulate and use their own data over time, developing capabilities that are difficult for competitors to replicate.

2. Data ownership and open versus closed systems

Data ownership emerged as an important strategic question. Companies need to consider how they use AI tools without losing control over valuable business information or becoming overly dependent on external technology providers.

Participants discussed the balance between open-source solutions, open systems and closed systems built around proprietary data. Open approaches can support collaboration and benefit entire communities, while closed systems can help companies protect sensitive information and develop unique capabilities.

There is no single solution for every business. The appropriate approach depends on the company’s needs, data sensitivity and strategic ambitions. A combination of open and closed solutions is likely to be relevant for many companies.

Key takeaway: Companies should develop an intentional approach to data ownership and understand how their information is stored, used and shared when adopting AI.

3. AI is transforming productivity, company growth and employment

Participants shared examples of AI enabling smaller teams to achieve significantly more than before. One company described how two people could now perform work previously requiring five. Another participant reflected that AI could substantially shorten product development timelines and reduce the number of employees needed to build a business.

These developments could make it faster and easier to start companies, accelerate innovation and free up resources for higher-value activities.

However, the discussion also raised concerns about the impact on employment, particularly junior roles. If experienced employees can use AI to perform tasks previously assigned to junior colleagues, companies may hire fewer entry-level employees. This creates a longer-term challenge: how will tomorrow’s senior experts gain the experience they need if fewer junior positions are available today?

Participants also emphasised that people and industries adapt at different speeds. While AI adoption may accelerate productivity in some sectors, others need more time to develop the necessary skills and capabilities.

The discussion therefore pointed to the importance of developing new skills, supporting adaptation and rethinking how companies train and develop future talent.

4. Start small: focus on practical, low-risk applications

A practical recommendation was to begin with simple applications rather than immediately introducing AI into the most critical or strategic parts of a business.

Potential starting points include:

• Administrative and repetitive tasks.
• HR processes.
• Marketing and communication.
• Internal knowledge management.
• Selected R&D and product development activities.

Starting with relatively low-risk tasks allows companies to experiment, learn and identify where AI creates measurable value without putting critical operations at risk.

Once an application works well, the company can build on that experience and expand into other areas.

Participants also highlighted the value of companies learning from one another and sharing experiences, helping the wider agrifood ecosystem identify useful applications and avoid repeating the same mistakes.

5. AI’s environmental footprint and its role in sustainable food systems

The environmental impact of AI was another important theme. AI can help food companies improve efficiency, optimise resource use and develop more sustainable products and production processes. Given the environmental footprint of food production, these applications could offer significant benefits.

At the same time, AI infrastructure consumes energy and other resources. Participants questioned whether the environmental costs of expanding data centres could undermine some of the sustainability gains AI is intended to deliver.

The discussion highlighted the need to consider AI’s overall environmental impact, including energy consumption, land use and water use, rather than focusing solely on the benefits of individual applications.

The key question is how AI can be directed towards reducing the environmental footprint of the food system while ensuring that the technology itself is developed and used responsibly.

6. Who benefits from AI – and what are the wider societal implications?

Beyond business performance, participants discussed the broader implications of AI for society. While the technology can increase productivity and create new opportunities, there were concerns that its benefits may accrue disproportionately to a relatively small number of companies and individuals.

The discussion also acknowledged that powerful technologies can be used for both beneficial and harmful purposes. This raises questions about responsibility, governance and how society can ensure that AI contributes to broader social and environmental progress.

Although AI offers considerable potential, its outcomes will depend on how companies, policymakers and society choose to develop and apply it.

Key takeaways

The discussion highlighted six priorities for agrifood companies exploring AI:

1. Start small and practical: Begin with simple, low-risk tasks where AI can save time and resources.
2. Move from experimentation to strategy: Progress from individual tools towards integrating AI into business processes.
3. Build on your own data: Develop company-specific data capabilities while maintaining appropriate control over sensitive information.
4. Learn and scale gradually: Share experiences with other companies, measure results and expand successful applications.
5. Prepare for workforce transformation: Invest in new skills and consider how to develop future talent as AI changes traditional entry-level roles.
6. Evaluate the full impact: Use AI to improve food system sustainability while remaining aware of its energy consumption and wider societal consequences.

Overall conclusion

AI offers significant opportunities to improve productivity, accelerate foodtech innovation and support a more sustainable and resilient food system. However, value does not come from adopting AI tools alone. Companies need to understand where the technology can solve real problems, protect and develop their data capabilities, and build the skills needed to integrate AI effectively.

The practical recommendation from the discussion was to start with something small or routine, learn from the experience and scale up gradually – while maintaining ownership and control of your data.