The more generative AI becomes widespread, the more often companies realize that they cannot get very far with standard solutions. If you want to achieve added value in the future, you need AI that is specifically tailored to a company’s individual context, data, processes and decision-making processes.
Personalization in AI is no longer an additional innovation, but increasingly a basic requirement. Regardless of whether they want to optimize processes, improve the customer experience or enable faster decisions – companies are increasingly relying on AI that corresponds to their reality.
Generic AI models are designed to be generally applicable – but that is precisely where their limitations lie. These models often do not take into account the individual nuances of a company, so the results are often less precise and very general, and cross-functional scalability suffers. One-size-fits-all solutions make it difficult to adapt across industries to different legal requirements, data types and complex business processes.
In challenging industries where precision, compliance, and context are critical, using generic models can lead to inefficient operations and missed opportunities. In addition, these models are difficult to integrate into corporate control, security and compliance workflows. The result: poor performance and a growing realization that standard AI is not suitable for complex business needs.
That’s why more and more companies are investing in differentiated innovation with AI solutions built from the ground up to achieve specific goals.
A clear example of this is our partnership with Accenture. With almost 1 million invoices per year and over 40,000 contracts, the manual billing process caused immense effort. Together, using SAP Business Technology Platform (SAP BTP) and generative AI, we developed a compliant, intuitive application that allows account managers to directly manage invoicing and navigate through price lists and contract terms, largely independently of teams of experts.
The results are impressive. Billing is faster and more accurate, the user experience has improved, and customer-facing teams can focus on customers rather than operational tasks. By the end of the year, billing efficiency is expected to improve by 32 percent and setup times are expected to halve. Manual work can now be automated using an intuitive platform.
Where it works: Change at the industry level
Custom AI applications are transforming industries by developing intelligence around each industry’s specific data, processes and challenges.
In manufacturing, the effects of customer-specific AI applications can be seen in the optimization of complex operational processes. For example, our team developed a solution for Henkel to support indexing in refund and clarification management of the financial and supply chain processes. This solution automates the analysis and indexing of complaint documents received from customers and integrates cutting-edge AI capabilities directly into daily dispute management workflows. In this way, case processing is accelerated, made more precise and more flexible and efficiency is increased.
In the oil and gas industry, AI models trained with geological data, equipment logs and environmental variables improve drilling forecasts and enable proactive maintenance, improving both safety and energy efficiency. The automotive industry is seeing similar benefits as AI powers predictive maintenance, autonomous driving systems and real-time diagnostics, and can personalize the car experience. Retail uses AI that adapts to regional purchasing patterns and live sales data. This enables more accurate demand forecasting, localized inventory planning and targeted promotions, which means less waste.
Even government agencies are discovering the value of contextual AI and using it to automate routine processes, prioritize citizen requests, and design policies more precisely to deliver public services faster and more effectively.
From these examples it is clear that AI that understands the context in which it works promotes more informed decisions, more efficient processes and better results for both companies and organizations and their target groups.
SAP’s vision: developing customer-specific AI applications for companies
SAP is at the forefront of this shift to personalized AI for businesses. The company’s vision includes creating AI that is not experimental, but suitable for companies.
Instead of developing standalone solutions, SAP integrates AI directly into core business processes in finance, human resources, supply chain, etc. By co-innovating with customers and partners, SAP works to make each AI solution technically robust and tailored to realistic use cases.
For AI to really make a difference in companies, it must be fully integrated and not just coupled. This includes working closely with experts, aligning with compliance standards, and constantly optimizing models based on real-time feedback. Custom AI applications are not just about code, but about collaboration, trust and long-term value.
Our approach is to enable companies to develop AI that reflects their structure, corporate philosophy and customers and that is targeted, reliable and responsible.
Now is the right time to scale
Companies that want to remain competitive can no longer afford to treat AI as a side project. The time for experimenting is over. Now is the time to scale AI that works intelligently, responsibly and quickly. Custom AI applications are not technical functions, but rather strategic factors for innovation, efficiency and differentiation.
The future belongs to those who can scale personalization without sacrificing performance. It’s time to evolve with AI your business knows.
Sindhu Gangadharan is Head of Customer Innovation Services at SAP.
