How to Get AI Right in Your Business
Artificial intelligence stopped being a future topic some time ago. Few technologies carry higher expectations right now. Companies expect AI to deliver more efficient processes, better decisions, and new business models. New applications, platforms, and tools also emerge almost daily.
However, a wide gap still exists between the technology's potential and its actual business application. Many organizations have gathered initial AI experience, but few successfully transition from isolated pilot projects to measurable value creation.
“Companies often treat AI like a silver bullet that can solve any problem – at least in theory,” says Raffaela Schneid, AI Expert at Drees & Sommer.
The reality usually looks quite different. Many companies launch individual applications without defining the specific problem they want to solve or establishing the prerequisites for successful implementation. “You will inevitably be disappointed if you think you just need AI and it will magically deliver everything,” Schneid notes.
A clear trend is also emerging, where companies increasingly view AI through a strategic lens today, despite only just experimenting with individual tools a year ago. Business leaders no longer ask whether they should use AI, but how they can integrate it sustainably into their processes, decisions, and business models.
We spoke with our expert about the factors that determine the success or failure of AI initiatives.
How Can Companies Use AI Successfully?
Many companies have reached a turning point in their AI journey. They have gained initial experience, implemented pilot projects, and uncovered fundamental potential. However, the significantly more demanding phase is only just beginning. Companies are now focusing on integrating AI into existing workflows, shifting away from their early focus on standalone applications. One key insight stands out for Schneid: “The secret is moving away from the model and toward the system. The era of low-hanging fruit is over.”
Successful companies view AI as part of a bigger picture rather than an isolated tool. They must embed the technology into existing processes, ensure it can access relevant data, and use it to create real value. “You need the entire pyramid: infrastructure, data, and integration. Only then does AI come into play,” Schneid explains.
This specific point highlights the difference between companies achieving measurable results and those stuck in the pilot phase. Organizations generating real value systematically integrate AI into their business, while the rest often just focus on individual tools.
“Using a Copilot license somewhere does not mean you are doing AI. The focus is shifting from the tool itself to the actual task. Companies must define the 'job to be done' and pinpoint the exact task they want AI to perform. Only when a company answers that question can AI unleash its full potential.”
What Should Companies Consider When Using AI?
AI introduces challenges alongside opportunities. Companies cannot succeed simply by deploying applications technically. Schneid identifies governance as a central success factor for this exact reason: “The governance part requires incredibly high effort while offering very little visibility. The return on investment is simply that nothing goes wrong.”
Companies face the challenge of establishing guardrails without stifling innovation. They need clear data-handling rules, defined responsibilities for AI applications, and transparent quality assurance processes. Measuring success is equally critical because different use cases pursue different goals. “You need custom evaluations, as every use case requires different metrics,” Schneid explains.
Data as the Foundation for Successful AI Projects
The past few months have revealed another key insight: AI success depends entirely on data quality. Many companies have massive volumes of information, documents, and process data. However, not every database automatically suits AI applications. “Having a lot of data means nothing. You need clean, suitable data – otherwise, you shouldn't even start,” Schneid emphasizes.
AI applications can only deliver reliable results when they draw from structured, current, and relevant information. That what makes data the foundation of every AI strategy and acts as one of the biggest success factors for scaling AI across the enterprise.
AI That Works: Moving from Experimentation to Value Creation with Dreso.AI
Drees & Sommer demonstrates how to successfully navigate the journey from initial AI experiments to measurable value creation using its proprietary AI platform, Dreso.AI.
The platform supports employees across numerous processes in construction and real estate projects. It has quickly become an integral part of the daily work routine. Dreso.AI currently handles around 4,000 use cases, ranging from communication and knowledge management to analytics and project-based tasks. More than 4,700 active users already work with the platform.
The benefits become particularly clear during repetitive processes. Teams have achieved efficiency gains of up to 90 percent during tenders and proposal analyses, for example. This development confirms a central insight for Schneid: “You haven't successfully implemented AI until it becomes an integral part of your value proposition.”
The success of Dreso.AI relies on more than just the technology itself. The critical factors included anchoring the platform consistently within processes, collaborating closely with specialist departments, and maintaining a willingness to learn from experience.
“We achieved this ourselves by investing time, money, and data. We also hit some roadblocks along the way,” Schneid admits openly. These specific experiences now form the foundation for guiding other companies along their own AI journeys.
AI Services: From Strategy to Implementation
Every company faces unique challenges. Consequently, no universal AI strategy works equally well for all organizations.
Drees & Sommer uses its AI Services to help companies identify their individual potential, prioritize relevant use cases, and establish the necessary organizational and technical prerequisites. The focus remains on concentrating on solutions that generate real value rather than simply introducing as many AI applications as possible. “We help companies radically narrow down this universe of possibilities,” Schneid says, describing the approach.
Drees & Sommer channels the experiences from its own AI transformation directly into its consulting services by “walking the talk.” Companies benefit from proven, practical approaches in addition to theoretical expertise.
AI isn’t a Project with an End Date
Many companies currently talk about “AI transformation.” Schneid believes this term falls short, and notes that "a transformation has a beginning and an end. AI doesn’t.”
Artificial intelligence evolves continuously, and the demands placed on businesses evolve right alongside it. That’s why the most successful organizations will not be the ones deploying the highest number of tools. They will be the ones that permanently integrate AI into their value proposition.
The journey there begins with a clear strategy, a robust database, and the courage to test new technologies. Above all, it begins with the understanding that AI is not an end in itself. It’s a powerful tool for solving real challenges and creating sustainable value.
