Throughout 2025, leadership teams across the food and drink industry have been navigating through the AI hype, defining practical pathways to unlock real business value. There continues to be a lot of excitement in boardrooms about the possibility of AI, but a lack of clarity on how to implement and where it will drive the most value. Here’s a closer look at the top eight strategic themes executives are prioritising in using AI within their organisation.
1. Operational excellence
In a climate where cost pressures are paramount, it’s no surprise that operational excellence is at the top of the list. Leaders continue to pursue opportunities to optimise resource allocation where there are routine tasks. AI can be used to streamline internal processes by automating repetitive tasks, orchestrating workflows, and identifying inefficiencies by detecting patterns through machine learning. Feedback loops can be used to surface bottlenecks and inform continuous improvement programmes. Developing applications in this area offer predictable savings and support workforce reskilling toward higher-value activities. The benefits are faster cycle times, reduced error rates and a reduced cost to serve.
2. Financial management
Leaders want earlier visibility of cash and margin risks. Predictive models can be used to improve the granularity of forecasting, plan scenarios and manage liquidity. AI use cases on the finance agenda seek to improve financial accuracy by automating reconciliations, flagging anomalies and standardising reporting. Embedding AI in routine processes reduces the burden of manual audits, whilst improving compliance by generating transparent audit trails and completing automated checks. Executives benefit from tighter governance, faster month ends, and the ability to reallocate finance capacity from repetitive tasks to strategic and performance analysis.
3. Customer experience and engagement
Customer retention, conversion, and lifetime value increases where interactions reflect real-time context and preferences. From order updates to customising the response to customer complaints, organisations are prioritising conversational interactions that scale tailored service. For customer services teams, AI is being used to automate triaging issues, predict customer health scores and enable proactive outreach. To be deployed successfully, senior executives need to measure the impact to the customer experience using clear, measurable KPIs, ensure data privacy safeguards are in-place, and aligning the channel strategy, so personalisation enhances trust and commercial outcomes. Implemented poorly and this could risk losing customer trust and impact long-term brand value.
4. Workforce empowerment
Productivity tools to assist employees are frequently discussed, as executives seek to remove low-value work and improve task accuracy applying intelligent automation. Leadership teams need to invest in reskilling and upskilling teams, whilst redesigning roles to capture productivity gains. HR functions are looking to deploy AI in automating repetitive tasks such as creating training content, job descriptions and employee on-boarding plans. In addition, to support the retention and development of talent, AI can be used to create tailored development pathways based on leadership skill matrixes and current capability assessments. These initiatives have the potential to significantly reduce labour costs within organisations.
5. Strategic planning and forecasting
The food and drink industry is particularly impacted by worldwide events and turbulent commodity markets. AI synthesises of large datasets combining internal performance with external signals can produce scenario-based forecasts and stress tests to improve strategic agility. Machine learning models can surface leading indicators of demand, competitive moves, and supply constraints so leaders can reallocate capital. Embedding probabilistic forecasts into planning cycles shortens decision time and supports creating contingency strategies. Organisations adopting AI as part of strategic planning and forecasting are more informed on risk to adapt.
6. Marketing and commercial growth
AI can be used to transform marketing by optimising the creation of creative content. Developing brand guideline libraries ensures that AI is tailored with a brand-led source of truth and customised narrative. This can significantly reduce the lead-time in developing timely seasonal or hype-driven campaigns. To support commercial operations, AI can be deployed to automate the development of pricing strategies through rapid experimentation and attribution. Machine learning led analysis can be used to explore historical datasets on the effectiveness of promotion strategies and customer segmentation. This could help to prioritise effective campaigns, increase future conversion, whilst lowering the cost per acquisition. These strategies help to support scalable brand consistency.
7. Sustainability and compliance
Sustainability and compliance use cases are high on the agenda. AI can be used to identify inefficiencies in energy, logistics, and procurement, supporting efforts to reduce emissions and minimise waste. This allows leadership teams to identify and prioritise interventions with the greatest benefit to society and the environment. In addition, using AI to automate compliance, scan reports and contracts for regulatory deviations, whilst creating coherent audit trails for inspection, reassures stakeholders and regulators alike.
8. Data-driven decision-making
Data-driven decision-making is the organising principle that allows all prior themes to deliver value. AI combines disparate datasets into timely metrics, anomaly alerts, and prescriptive recommendations that shorten the path from insight to action. Data governance, model explainability, and performance monitoring are key to ensuring decisions remain auditable and trustworthy. Embedding data literacy in an organisation, ensures that an organisation has the information it needs to continuously adapt and improve performance.
What are the practical actions that leaders can take in embarking on their AI journey?
- Prioritise investment on high-value use cases, linking pilots directly to measurable KPIs to assess ROI quickly
- Include quantitative KPIs and qualitative change metrics and scale based on performance
- Establish cross-functional ownership that align IT, data science, and business functions
- Enforce strong data governance, privacy safeguards, and model validation processes that manage AI ethics and risk
- Upskill teams to adopt tools as part of their BAU, allowing them to focus their development on higher value adding activities
- Maintain business guardrails and create communication channels for employees so AI adoption is sustainable, trusted, and delivers repeatable business value
As organisations look ahead to 2026, many are asking how to turn AI ambition into actionable plans. One way to achieve this is through a facilitated leadership AI workshop, designed to bring clarity and focus to your strategy. It’s an opportunity to explore practical applications, assess readiness, and define next steps for implementation.
Find out more and connect with our team to arrange a workshop and start shaping your AI roadmap.
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