I hear CIOs talking all the time about how important it is to embrace artificial intelligence, but that the biggest challenge is scaling it effectively. Experimentation is all very well, but for me, successful adoption of AI hinges on a company’s readiness.

There comes a time to integrate AI with your processes and data, leveraging cutting–edge models–in the context of your business. And SAP will be there to help you with all of that. However, well before this stage, it’s essential to have the right cloud architecture in place.

In this article, I explore how you can achieve the readiness required to scale meaningful AI outcomes. That includes unified ERP processes and system modularity, plus the right business culture. Because, without internal readiness, AI deployment is doomed to fail.

The CIO’s changing role

Your challenge:

Are you seeing, amidst all the AI hype, a degree of wariness within your organization? It can be hard to know where to start, yet many CIOs I speak to are under pressure to just “Get on with it!”

CEOs want the technology up and running, fast; ahead of (or at the very least neck–and–neck with) competitors. Leadership is looking for those new efficiencies that come with intelligent automation. And as ERP plays a central role for effective use of AI at scale within the business processes, the pressure is getting real!

The solution:

Work first to build trust, both in your teams and across different levels of your organization. This is your foundation for optimal strategy definition and implementation.

Long–term success will come through strategic alignment between business and technology; and not taking shortcuts driving real systems and operation modernization. The modern CIO has evolved beyond technical leadership and systems ownership. Now, it falls to you to be a true transformation partner.

What CIOs need to do now

We know that outdated systems and silos limit real–time responsiveness. Misalignment between IT architecture and business KPIs surfaces organizational limitations, because

You need to seize this moment to modernize your ERP core. Unified, modular cloud architecture will allow for experimentation–without entrenching rigidity, or causing chaos. This is your path to innovation, while preserving systems control, and ensuring compliance with shifting regulation.

You  can have it all. Honestly! But it will require unified data and workflows to accelerate time–to–value from your AI use cases.

Key strategic steps

AI adoption starts with system design, and it’s not a trade-off, but a structural requirement to support both agility and control. So:

• Avoid applying your AI to broken processes, because rebuilding context outside core systems is brittle and error-prone. Instead, modernize, orchestrate, and consolidate before even thinking about deployment.
• Design your stack for experimentation, with modular services and clean interfaces to enable test-and-learn agility. Which is not about speed, but resilience and room to experiment, without breaking the business!
• Build in system connectivity and context, because AI only delivers value–real-time insights, anomaly detection, and embedded automation–when it understands the operational landscape where it’s put to work.
• Think in terms of outcomes, not workflows. A s CIO, align every transformation project to organizational KPIs and set objectives. And tie these to functions or geographies – not to technical deliverables.
• Don’t oversimplify AI business cases; from specificity comes the strength that brings CFO and CEO buy-in. So, frame arguments around cross-functional gains and strategic capabilities, not just cost savings.
• Insist on clear data boundaries, ensuring AI remains within enterprise walls. Your people, especially those new to a cloud environment, want confidence that their data won’t be used to train external models.
• And remember, far from being the enemy of speed, integration is your enabler!

I know of one SAP customer who assumed a need for significant customization during ERP modernization. However, after assessing embedded workflows, it was discovered that 70-80% of processes could be supported natively or with simple cloud extensibility. Reducing the need for bespoke builds helped the business preserve agility and avoid unnecessary technical debt.

The learning here is that AI in enterprise systems has to mirror the intelligence of the organization. It cannot be disconnected from the core operational context.

In summary

Ahead of AI deployment, cloud architecture and integration are foundational–not peripheral. Building out AI at scale for enterprise requires strategic readiness, beyond the technical challenge.

Another key component of your ERP rollout planning is trust, enabling the confidence to act on the new insights generated. So, your teams need support, training and transparency.

It is SAP’s vision to help our customers achieve all of this–transforming your culture and operations to ensure AI is native to your new architecture, and can be optimized for you. It’s within your power to bring agility, transparency and outcome-led transformation.

Partner early with your CFO to align AI investment with outcome-driven transformation goals around competitive edge-driving ERP. And never doubt that AI success hinges more on clarity of purpose and human capabilities than on the technology itself.