A CIO Buyer’s Guide to Building an AI Roadmap

Artificial intelligence (AI) has increasingly become a business-critical capability in the last few years and, as a CIO, you likely hear many voices shouting about the need for faster AI adoption. However, as with the proverb popularized by Spider-Man, “With great power comes great responsibility,” while AI capabilities are rapidly being added to enterprise applications, a well-crafted AI roadmap is essential for strategically adopting them.

To help, this blog has been written to cut through all the AI “noise” around us right now to help CIOs and other C-level executives create a clear, value-driven AI roadmap. As Gartner^® Research states here, “There is no one-size-fits-all AI roadmap; you need to create an AI roadmap that works for your organization.”¹

Why CIOs need an AI roadmap (and now!)

AI touches every part of the enterprise – from customer service and supply chain management to cybersecurity and human resources (HR). Importantly, AI is now a strategic improvement lever, not just a technical asset. Therefore, AI must focus on “what matters most,” with any lack of alignment between technical implementation and business objectives avoided.

An AI roadmap is how effective CIOs make their AI initiatives work by:

• Aligning AI initiatives with business goals
• Prioritizing high-impact and high-value use cases
• Identifying the required skills, platforms, and infrastructure early
•  Ensuring compliance and ethical use (as per this Joe the IT Guy blog on AI governance platforms)
• Measuring success through well-defined key performance indicators (KPIs).

This all starts with the definition of an AI vision and objectives.

Building an AI Roadmap Step 1: Defining your AI vision and objectives

Corporate AI adoption must start with clarity of purpose. As a CIO, working closely with executive leadership is important to define how your AI ambitions will support the enterprise strategy. For example, does your AI adoption goal need to focus on:

• Improving operational efficiency?
• Enhancing employee and/or customer experiences and driving personalization?
• Reducing operational costs?
• Minimizing risk through predictive analytics?
• Unlocking new revenue streams via intelligent products?

It might encompass more than one of these or something else. However, whatever the primary motivation is, your organization’s AI roadmap must be anchored in clear, measurable business outcomes.

Building an AI Roadmap Step 2: Assessing your current data and technology landscape

With a more traditional journey, reaching a desired destination is hard if you don’t know where you’re starting from. The same is true with AI adoption.

In this case, your AI will only be as good as the data and infrastructure behind it. So, before investing in any new AI platforms, there’s a need to assess your organization’s:

• Data quality, availability, and governance
• Current analytics and machine learning capabilities
• Cloud maturity and scalability
• Security and compliance readiness.

The early AI failures can usually be attributed, at least in part, to poor data hygiene or the lack of integration across systems. So, ensure your organization evaluates whether its data is “AI-ready.” Where gaps are identified, they must be addressed.

Building an AI Roadmap Step 3: Choosing the right AI use cases

You don’t need me to tell you that not every business or IT issue or opportunity needs AI. Instead, focus on the use cases where AI adoption will deliver disproportionate value. Other organizations have seen this as:

• Predictive maintenance in manufacturing
• Dynamic pricing in retail
• Fraud detection in financial services
• Intelligent routing in customer service
• Workforce forecasting in HR.

However, you must understand “what matters most” to your organization and focus on specific improvement opportunities to deliver against these needs.

Building an AI Roadmap Step 4: Selecting the right route to the AI capabilities your organization needs

Your organization’s various AI use cases might require different approaches, such as building in-house, buying off-the-shelf, or partnering with third parties. The right choice will also likely depend on your organization’s AI maturity, budget, and strategic control needs.

Each option also has pros and cons to consider. For example:

• Building AI systems in-house might offer valuable customization capabilities, but it requires top-tier AI talent and long lead times.
• Pre-built solutions will accelerate time-to-value but may limit flexibility. This includes the wealth of AI capabilities being added to enterprise applications such as IT service management (ITSM) platforms and tools.
• Partnering with AI vendors or consultancies provides scale, but it introduces third-party dependencies and risks.

Building an AI Roadmap Step 5: Defining architecture, governance, and compliance needs

CIOs must ensure that AI systems are architected for scalability, security, and trust. This need includes the following elements:

• AI model lifecycle management
• Data governance capabilities
• Adherence to ethical AI principles
• Meeting regulatory compliance needs
• Auditability.

Joe the IT Guy’s “What Your Organization Needs from an AI Governance Platform” blog offers greater insight here.

Building an AI Roadmap Step 6: Investing in people and organizational change management

As with introducing most new technologies, AI adoption isn’t just a technical shift. It affects ways of working and, therefore, people. AI is thus a people change and an organizational transformation. To help maximize the probability of AI success, you must address the need for both skill development and cultural readiness. This includes elements such as:

•  Suitably selling the benefits of AI to achieve employee buy-in
• Upskilling existing teams in AI fundamentals and toolsets
• Hiring for key roles like data scientists and AI product managers
• Fostering a culture of experimentation
• Building AI fluency across business functions.

Gartner recommends that “The first typical step is to create a workforce plan that identifies the talent implications of AI, the current talent gaps and how they will be addressed.”

Building an AI Roadmap Step 7: Establishing metrics and KPIs for AI success

The current best practice is to establish AI KPIs across three layers:

1. Technical performance, including the AI model’s accuracy, latency, and uptime
2. Operational impact, including process automation, cost savings, and reduced lead times
3. Business outcomes, including revenue growth, employee experiences, customer satisfaction, and risk reduction.

With every AI initiative tied back to a specific business objective.

If you want help building an AI roadmap, please get in touch.

¹Gartner, The CIO’s Guide to Building an AI Roadmap That Drives Value, By Leinar Ramos, Anthony Mullen and Pieter den Hamer, January 10, 2025.

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