SAP Business AI can boost productivity with technology that aligns with the AI strategies of our customers—ranging from building effective agents to managing intelligent systems.

Among the many announcements at SAP Sapphire in 2025, the company unveiled new innovations, partnerships, and integrations that can deliver real-time, proactive assistance. For example, SAP’s AI copilot Joule is now available to users across SAP and non-SAP systems. SAP also expanded its agentic AI footprint across SAP Business Suite by introducing Joule Agents for multiple use cases and an evolving AI Foundation as the AI operating system designed to simplify development, enabling developers to build, deploy, and scale solutions with ease.

The impact of AI on the delivery of customer support at SAP

As announced in Q2 this year, SAP’s simplified, tiered, services-and-support engagement model will be generally available in early 2026. Here, SAP’s customer support is a centerpiece of the Foundational Success Plan, delivered via the proven SAP Enterprise Support offering included in every SAP cloud solution subscription. The Foundational Success Plan can support in-house teams by helping to onboard and run solutions, keep business continuity, and drive ongoing value. It includes customer self-service options, application lifecycle management solutions centered around SAP Cloud ALM, and preventative mission-critical support. With the plan, SAP turns on Joule for a customer’s business and supports the team ramp-up with learning journeys for SAP Business AI.

When it comes to customer support in general, agentic AI can redefine the support process by moving beyond scripted responses and basic automation. It can assess situations, make decisions, and take action—often before the customer even knows there’s an issue. SAP’s customer support harnesses agentic AI to help deliver smarter assistance, faster resolutions, and a stronger human–tech partnership.

We focus on elevating support experiences for customers and improving support delivery for engineers by employing a combination of agents and assistants. For example, we use autoresponders and smart log analyzers to help process issues, while configuration advisors, language services, and proactive notifiers can guide customers toward self-service solutions. At the same time, our support engineers rely on co-pilots to help summarize cases, recommend solutions, escalate using intelligence, assist with communications, and create a continuous feedback loop for learning. For strategic customer support, we use tools like feedback collectors to help capture customer insights and channel recommenders to help ensure that every interaction is handled in the right channel. Together, these innovations can redefine support as faster, smarter, and more human.

The impact for customers

When it comes to SAP Business AI, we build trust and create customer confidence by being relevant, reliable, and responsible. Unlike traditional AI that only suggests answers, agentic AI can reason, decide, and take action. For customers to feel confident, they expect accuracy, reliability, and transparency from the system.

As we support and guide our customers, we recognize that while agentic AI is a game-changer, it is not a magic pill. Coupled with ethical and responsible AI, real impact comes from SAP’s business expertise and a deep understanding of what our customers truly need. When knowledge is combined with AI to infuse autonomy and interoperability in our agents, we can unlock the ability to simplify processes, remove friction, and deliver experiences that feel effortless.

AI technology amplifies human insight and delivers delightful user experiences, but when it comes to business AI, it is our domain expertise that fuels SAP Business AI into a tool for creating genuinely easy, productive, and meaningful experiences for our customers.

Stefan Steinle is executive vice president and head of Customer Support & Cloud Lifecycle Management at SAP.

What I’m seeing in zero-trust deployments

The real story isn’t in the survey data — it’s in the conversations I’m having with enterprise security architects trying to implement zero trust strategies. Last month, I worked with a financial services company that had spent eighteen months evaluating ZTNA solutions. They’d built requirements documents, conducted vendor demos and mapped their application inventory. But when it came time to deploy, they hit a wall.

The problem wasn’t technology. Gartner’s Magic Quadrant shows vendors like Palo Alto Networks, Netskope and Zscaler have mature platforms. The problem was that implementing these solutions required untangling years of VPN configurations, documenting legacy application dependencies and coordinating with stretched application teams.

What struck me was hearing their CISO say, “We bought this ZTNA platform for intelligent, automated access control. Instead, we’re spending more time on manual policy creation than with our old VPN.” That’s when I realized we’re dealing with a deeper issue than technology selection.

The integration of artificial intelligence (AI) into the food industry is revolutionizing the way food is produced, processed, distributed, and consumed. AI-driven solutions offer unprecedented opportunities for improving efficiency, ensuring safety, reducing waste, and enhancing sustainability in this vital sector. This article explores how AI is transforming various facets of the food industry, from farm to table.

AI in agriculture

The food production process begins on the farm, where AI technologies are helping farmers make smarter decisions. Precision agriculture, powered by AI, uses data from sensors, drones, and satellites to monitor crop health, soil conditions, and weather patterns. Machine learning algorithms analyze this data to provide actionable insights, such as when to irrigate, fertilize, or harvest crops. This approach not only boosts yield but also minimizes the use of water, fertilizers, and pesticides, reducing environmental impact.

Robotics is another AI application making waves in agriculture. Autonomous tractors and robotic harvesters equipped with AI can perform labor-intensive tasks with precision, addressing labor shortages and reducing costs. For instance, AI-enabled robots can differentiate between ripe and unripe fruits, ensuring only the best produce is picked.

Enhancing food processing and manufacturing

AI is playing a critical role in food processing and manufacturing by optimizing operations and ensuring quality control. Advanced vision systems powered by AI can inspect food products for defects, contaminants, or inconsistencies at a speed and accuracy unmatched by human workers. This ensures that only safe and high-quality products reach consumers.

Predictive maintenance is another area where AI is proving invaluable. By monitoring machinery and analyzing operational data, AI can predict equipment failures before they occur, minimizing downtime and maintenance costs. This level of foresight is especially important in food manufacturing, where delays can lead to spoilage and significant financial losses.

In addition to improving efficiency, AI-driven automation is enhancing worker safety by taking over hazardous tasks, such as handling hot or sharp equipment. This contributes to creating a safer work environment in food processing plants.

Supply chain optimization

The food supply chain is a complex network that requires precise coordination to ensure timely delivery of perishable goods. AI-powered tools are streamlining supply chain management by improving forecasting, inventory management, and logistics.

Demand forecasting is a key application of AI in this domain. By analyzing historical sales data, market trends, and external factors like weather or holidays, AI systems can accurately predict demand for different food products. This helps retailers and suppliers avoid overstocking or understocking, reducing food waste and increasing profitability.

AI is also revolutionizing logistics through route optimization and real-time tracking. Advanced algorithms can determine the most efficient delivery routes, reducing fuel consumption and ensuring products reach their destinations as quickly as possible. Additionally, AI can monitor the condition of perishable goods during transit, ensuring they remain within safe temperature ranges.

Enhancing food safety and quality

Food safety is a top priority in the industry, and AI is proving to be a powerful ally in this area. Machine learning algorithms can analyze vast amounts of data from production lines, environmental monitoring systems, and lab tests to identify potential risks or contamination sources.

AI-powered tools are also aiding in the rapid detection of pathogens like Salmonella and E. coli. Traditional testing methods can take days, but AI-based systems can deliver results in hours, enabling quicker responses to potential outbreaks. Moreover, blockchain technology combined with AI is enhancing traceability, allowing stakeholders to track the journey of a product from farm to fork. This transparency helps build consumer trust and simplifies recalls in case of contamination.

Reducing food waste

Food waste is a significant global issue, and AI is offering innovative solutions to address this challenge. AI systems can analyze data from supermarkets, restaurants, and households to identify patterns and suggest ways to reduce waste. For instance, AI can recommend optimal stock levels for retailers, ensuring they do not overorder perishable items.

In the hospitality sector, AI-powered tools can monitor inventory and predict demand, helping chefs prepare just the right amount of food. This not only reduces waste but also cuts costs. Additionally, AI is being used to repurpose surplus food by identifying ways to incorporate it into new recipes or distribute it to those in need.

Personalized nutrition and consumer experience

AI is transforming the way consumers interact with food, offering personalized recommendations based on individual preferences, dietary restrictions, and health goals. Apps and wearable devices equipped with AI can analyze user data to suggest meal plans, track nutritional intake, and even offer cooking tips.

Retailers are also using AI to enhance the shopping experience. AI-powered chatbots and virtual assistants can guide customers in selecting products, answer queries, and provide tailored suggestions. Meanwhile, AI-driven shelf management systems ensure that popular items are always in stock, improving customer satisfaction.

Driving sustainability

Sustainability is a pressing concern for the food industry, and AI is helping companies adopt greener practices. By optimizing resource usage, reducing waste, and improving supply chain efficiency, AI is enabling the industry to lower its carbon footprint.

AI is also playing a role in developing alternative proteins, such as plant-based or lab-grown meat. Machine learning models are being used to optimize formulations, improve texture and taste, and scale production. These innovations are contributing to a more sustainable and ethical food system.

Challenges and future prospects

While the benefits of AI in the food industry are immense, challenges remain. High implementation costs, lack of technical expertise, and concerns about data privacy are some of the barriers to widespread adoption. Additionally, there is a need for robust regulations to ensure ethical use of AI and address potential biases in decision-making.

Despite these challenges, the future of AI in the food industry looks promising. As technology continues to evolve, we can expect even more sophisticated applications that further enhance efficiency, sustainability, and consumer satisfaction. Companies that embrace AI today will be well-positioned to lead the industry into a smarter, more sustainable future.

In conclusion, AI is not just a tool but a transformative force reshaping the food industry. By harnessing its potential, stakeholders can address some of the most pressing challenges in food production, safety, and sustainability, ultimately creating a better food system for everyone.