By Jan Gilg
The Era of Business AI has moved well beyond the initial hype. Today, it’s already taking its place at the center of executive decision-making.
According to SAP’s recent AI Has a Seat in the C-Suite survey, nearly two-thirds of executives have integrated generative AI into their decision-making processes or are actively expanding its role. For many of the largest organizations, AI is replacing or significantly bypassing traditional methods.
This shift is happening because business AI is built on a simple truth: the more it’s used, the more valuable it becomes. Each interaction strengthens this “feedback loop,” feeding new information into the system that improves both the quality and the reliability of future recommendations.
Why the feedback loop accelerates growth
At its core, generative AI is a system that relies on data. Strong inputs — primarily clean and relevant data — create stronger outputs. When leaders consistently apply AI to real business challenges, they are not just solving today’s problems; they are also training the system to be smarter tomorrow.
Think of this as a flywheel of innovation. Each decision fueled by AI produces new insights, which feed back into the system. Over time, the cycle accelerates. Better data drives better recommendations, which in turn guides better outcomes. You can also think of this as a chain reaction of smarter decisions that drive business outcomes.
On the flip side comes stagnation. Without consistent use, the AI loop weakens, depriving leaders of the chance to refine insights and leaving organizations stuck with static, underperforming systems. So, when this loop is truly embraced at scale, companies begin to see productivity gains and business outcomes that were not possible before.
From experimentation to scaled results
Leaders are already reporting measurable results from strengthening the AI feedback loop. Nearly half (49%) say AI has improved the speed and accuracy of decision-making, according to SAP survey data.
Others are experiencing improvements that directly affect the bottom line. But most importantly, leaders are no longer chasing innovation for its own sake. Instead, they are prioritizing scalable results that can be replicated across the business.
Turning data into trust
One of the biggest barriers to unlocking AI is data reliability. Many businesses still struggle with misalignment between IT and business functions, fragmented systems or concerns about data quality. Without addressing these foundations, the AI loop cannot reach its full potential. To ensure data reliability and optimize AI applications, leaders must focus on:
– Integration across systems: Cloud technology now makes it possible to unify data from across the enterprise. Leaders should prioritize connecting disparate applications so that AI works with a single, coherent view of the business.
– Relevant, business-specific applications: Generative AI must understand not just data, but also context. Applications built for specific industries allow AI to generate insights that are immediately useful and trusted.
– Ease of use: AI adoption grows when the interface is intuitive, like Joule. The simpler it is for employees to use AI tools in their daily work, the more inputs the system receives, thus fueling the feedback loop.
With these building blocks in place, the system is no longer a “black box” but a partner in decision-making.
Action steps for leaders
For leaders who want to harness the power of the AI feedback loop, a few actionable priorities stand out:
– Commit to consistent use: The value of AI compounds with frequency. Encourage teams to apply it daily across functions, from operations to customer engagement.
– Invest in data quality: Clean, integrated and industry-relevant data that has semantic meaning is the foundation of A’s effectiveness to drive outcomes and in turn build trust. Without it, outputs will be limited.
– Design for scale: Pilot projects are valuable, but their true worth comes when insights can be replicated across the enterprise. Build with long-term scalability in mind.
Empower people: Make AI tools intuitive and accessible. The easier they are to use, the more inputs the system will receive, creating stronger outputs.
Measure both hard and soft benefits: Track gains in speed, accuracy and productivity — but also monitor improvements in employee well-being and decision-making confidence.
The sooner organizations embrace these principles; the sooner they can unleash the massive potential of business AI to drive productivity growth and business outcomes today and for the long term.
About the author
Jan Gilg is a member of the SAP Extended Board and co-leads the global Customer Success Board area, which is responsible for the totality of SAP cloud revenue and customer growth. He also leads the SAP Americas region and has oversight of the global SAP Business Suite organization.