I often hear CIOs discussing how important it is to embrace artificial intelligence, but that the greatest challenge is scaling it effectively. Experimentation is all very well, but for me, successful adoption of AI depends on a company’s preparedness.

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 assist you with all of that. However, well before this stage, it is essential to have the correct 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, as well as the right business culture. Because, without internal preparedness, AI deployment is destined to fail.

The CIO’s changing role

Your challenge:

Are you noticing, amidst all the AI hype, a degree of caution within your organisation? It can be difficult to know where to begin, yet many CIOs I speak to are under pressure to simply “Get on with it!”

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

The solution:

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

Long-term success will come through strategic alignment between business and technology, and not by taking shortcuts when driving genuine systems and operations modernisation. The modern CIO has evolved beyond technical leadership and systems ownership. Now, it is your responsibility 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We know that outdated systems and silos limit real-time responsiveness.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 reveals organisational limitations, because

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

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

Key strategic steps

AI adoption begins with system design, and it is not a compromise, 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 fragile and prone to errors. Instead, modernise, orchestrate, and consolidate before even thinking about deployment.
• Design your stack for experimentation, with modular services and clear interfaces to enable test-and-learn agility. Which is not about speed, but resilience and scope to experiment, without jeopardising 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 is put to work.
• Think in terms of outcomes, not workflows. A s CIO, align every transformation project to organisational KPIs and set objectives. And link these to functions or geographies – not to technical deliverables.
• Don’t oversimplify AI business cases; from specificity comes the strength that secures CFO and CEO buy-in. So, frame arguments around cross-functional benefits and strategic capabilities, not just cost savings.
• Insist on clear data boundaries, ensuring AI remains within enterprise walls. Your staff, especially those new to a cloud environment, want reassurance that their data will not 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 customisation during ERP modernisation. 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 maintain agility and avoid unnecessary technical debt.

The lesson here is that AI in enterprise systems must reflect the intelligence of the organisation. It cannot be disconnected from the core operational context.

In summary

Before deploying AI, cloud architecture and integration are fundamental—not peripheral. Developing AI at scale for enterprises requires strategic preparedness, 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 optimised for you. It is within your power to bring agility, transparency and outcome-led transformation.

Engage early with your CFO to align AI investment with outcome-driven transformation goals focused on gaining a competitive edge through ERP. And never doubt that AI success depends more on clarity of purpose and human capabilities than on the technology itself.