Artificial intelligence (AI) is constantly evolving, with the latest developments promising to deliver hitherto unseen levels of automation and data-driven insights.
For enterprises, the challenge is no longer whether to adopt AI, but how to move beyond hype and scale solutions that unlock strategic value.
“Virtually every organisation understands the impact AI can have on their business,” says Spencer Martenson, strategic product marketing lead, SAP on AWS. “Now customers are asking how they can move from concept to pilot and into production as predictably and cost-effectively as possible.”
This requires robust infrastructure, clarity around use cases, and an understanding of where the next phase of innovation is heading.
Enterprise AI has progressed from machine learning to generative models. The next evolution will be defined by the adoption of AI agents that can reason, act, and adapt to automate complex processes across the business.
From generative to agentic AI: What changes?
Generative AI (gen AI) has already begun to reshape enterprise operations. Research from McKinsey shows 63% of firms use it to generate text, more than a third (33%) produce images, and over 25% create code.
The next wave of AI disruption will be even more transformative as agentic AI combines reasoning, memory, and autonomy to create systems that don’t just suggest, but decide and act.
This opens up new possibilities in finance, logistics, HR, and supply chain if the systems are ready.
Why infrastructure still matters
Agentic AI cannot run on legacy stacks. It demands high-performance infrastructure capable of real-time inferencing, parallel processing, and large-scale model training, all without compromising reliability.
“If you don’t have the right expertise to choose infrastructure, you’re going to face massive costs, availability issues, and poor data performance,” says Akanksha Bilani, senior global sales director at Intel Corporation.
That’s where technology such as Intel® Xeon® Scalable processors and Intel® Gaudi® AI accelerators prove invaluable. As part of AWS’s compute backbone, these solutions help enterprises run advanced AI workloads — ranging from training foundation models to powering live decision-making in production environments.
SAP and AWS: Co-innovating for enterprise impact
A leading example of how enterprises are operationalising AI is the co-innovation program launched by SAP and AWS. The initiative gives customers access to the combined capabilities of SAP, AWS, and GSI/ISV partners and the infrastructure needed to develop intelligent agents tailored to real-world use cases — from financial anomaly detection to supply chain optimisation.
As Philipp Herzig, chief AI officer at SAP, says, “By combining the power of our fully integrated platform with SAP Business Technology Platform and our deep business process expertise with AWS’s comprehensive generative AI capabilities, partners can now create purpose-built AI agents that solve their most pressing challenges — from identifying financial anomalies in real time to automatically optimising supply chains during disruptions.”
Programs like this show how platform integration, domain expertise, and scalable infrastructure can help CIOs accelerate from pilot to production.
What CIOs should focus on now
The innovation roadmap is about operationalising the major models available to CIOs, embedding intelligence into operations safely, seamlessly, and securely. CIOs exploring AI should focus on four key imperatives:
- Infrastructure readiness: Ensure environments can handle the demands of real-time AI agents.
- Right combination of AI tools and services: Strategically chosen to address your business challenges.
- Use case clarity: Prioritise business functions where autonomy adds tangible value.
- Platform alignment: Leverage integrated ecosystems like SAP BTP on AWS to accelerate development.
Enterprise AI is no longer a lab experiment. It is becoming the logic layer of business itself. CIOs who take a structured, platform-aware approach today will be best positioned to deliver faster decisions, leaner operations, and long-term competitive advantage.