Assess Your AI Maturity

Look before you leap.

Analyst Perspective

Don’t put your organization at risk by taking on too much too soon.

The thought of maturing organizational artificial intelligence capabilities and approaches can be a sobering experience. Until recently, machine learning and artificial intelligence were niche interests for many organizations: something that an IT leader might ponder briefly while thumbing through a trend report or attending a keynote. For the most part, investments were minimal, tentative, and often quickly abandoned.

And then November 30, 2022, happened. The introduction and popular usage of ChatGPT and other similar products changed the priorities of many IT organizations. Since then, IT leaders have been playing catch-up in terms of assessing AI capabilities and creating strategies and policies.

Don’t put your organization at risk by taking on too much too soon. Start with an assessment that helps you assess your goals and gaps from a governance, data, people, processes, and technology perspective. With an appropriate view of where the organization currently is versus where it needs to be, you can create strategies, policies, and roadmaps that will help better balance the need to innovate with the need to mitigate the risks and protect and maximize shareholder value.

Bill Wong Principal Research Director Info-Tech Research Group	Andrew Sharp Research Director Info-Tech Research Group	Harshita Bordiya Research Specialist Info-Tech Research Group

Executive Summary

Info-Tech Insight

The goal in assessing your AI maturity isn’t to set targets to reach a top-level of maturity. Oftentimes, the costs of reaching level 5 or top maturity outweigh the organizational benefits. Instead, use an AI maturity activity to help identify AI goals that the organization can support and benefit from, without imposing unrealistic expectations or opening the organization up to unnecessary risk.

Your Challenge

Assessing your organization's existing level of maturity for AI adoption is essential in formulating a strategic roadmap to advance toward your AI objectives.

• The rapid adoption of AI technologies across business functions is forcing IT and business leaders to assess their maturity to implement AI systems and their organizational maturity to succeed with them.
• Indeed, despite the explosion of usage and interest in the technologies, currently few organizations have had success with a unified and widespread adoption of AI technologies, and even fewer with generative AI.
• What’s more, jumping into an AI implementation that exceeds the IT’s and business function’s abilities to responsibly and safely sustain it will open the organization up to multiple risks (e.g. cybersecurity, reputational, shareholder risk).

This research will introduce you to Info-Tech’s approach to assessing AI maturity (i.e. our framework and maturity assessment tool) to help ensure you set AI goals that you can support and benefit from.

59% of companies view AI as critical or highly critical to their business in the next year, and 69% in the next three years. The increasing capabilities and availability of generative AI will accelerate AI adoption.

Measure the value of this research

Conduct a maturity assessment to uncover insights that lead to measurable value for your organization.

Mitigate risks

Identify and address governance, process, or technical gaps that can identify and mitigate serious risks before they impact your organization.

Measure Risk Mitigation: Estimate the impact of risks identified via the maturity assessment and mitigated/avoided in terms of customer impact, revenue impact, compliance impact, etc.

Get to market faster

Identify and address maturity gaps that can delay solution development, in order to develop and deliver AI solutions faster by removing the obstacles to delivery.

Measure Accelerated Development : Evaluate the impact on development time that infrastructure, tooling, and process gaps identified via the maturity assessment caused your organization.

Reduce costs

Prepare for development and deployment to reduce unnecessary costs and streamline delivery, leading to long-term cost savings.

Measure Cost Savings: Estimate the costs reduced or avoided through better-fit infrastructure, processes, tools, techniques, or solutions that were identified by working through the maturity assessment.

AI Maturity Dimensions

Assess your AI maturity to understand your organization's ability to deliver in a digital age.

AI Governance: Do we have an enterprise-wide, long-term strategy and clear alignment on what is required to accomplish it?

Data Management: Do we embrace a data-centric culture that shares data across the enterprise and leverages it to drive business insights?

People: Do we employ people skilled with delivering AI applications and building the necessary data and technology infrastructure?

Process: Do we have the processes and supporting tools to deliver on AI expectations?

Technology: Do we have the required data and technology infrastructure to support AI-driven digital transformation?

Evaluate maturity across dimensions with Info-Tech’s principles-based approach to AI

Level 1: Exploration
No experience building or using AI applications. Awareness of the uses of AI but lack of skill or capabilities for use. No familiarity with governance or processes.

Level 2: Incorporation
Some skills in using AI applications and/or AI pilots are being considered for use. In development and pilot use. Focus on building on dimensions through training, developing guidelines, and acquiring technology.

Level 3: Proliferation
AI applications have been adopted and implemented in multiple departments. Some controls are in place to monitor compliance with responsible AI guiding principles. No uniformity in AI use across departments.

Level 4: Optimization
The organization has automated most of their digital processes and leverages AI to optimize business operations. High level of compliance and control with responsible AI principles. Not an industry leader yet.

Level 5: Transformation
The organization has adopted an AI-native culture and approach for building or implementing new business capabilities. Responsible AI guiding principles are operationalized with AI processes that proactively address possible breaches or risks associated with AI applications.

Level 1: Exploration

Learn before you launch.

Organizations at this level:

• Are in the process of learning about AI technologies. No use cases deployed.
• Use publicly available AI technologies in an informal (and possibly unauthorized) manner.
• Focus on individual and team learning to develop an understanding of the ways AI could deliver value and have a high-level understanding of potential risks.

If you’re at this level, you should:

• Consume Info-Tech’s research, including our A Primer on AI for Business Leaders and Develop Responsible AI Guiding Principles.
• Focus on building the governance, data practices, skills, processes, and infrastructure required to support pilot projects.

Level 2: Incorporation

Run pilot projects.

Organizations at this level:

• Are initiating pilot projects to gain practical experience and insights into AI applications.
• Have AI implementations that are relatively limited in scope and scale.
• Focus on learning from the outcomes of pilot projects. Organizations gather feedback, iterate on their approaches, and refine their understanding of AI technologies and their potential applications.

If you’re at this level, you should:

• Clearly identify the business objectives and problem areas that can potentially benefit from AI implementation.
• Evaluate the successes realized and challenges faced during pilot projects.