Henry Ford once remarked, “One who fears the future, who fears failure, limits his activities.”
Falling short of our goals is frequently part of growth and lays the groundwork for future success, particularly when exploring uncharted territory and adopting new technologies like generative artificial intelligence (genAI). However, no one wants to embark on an endeavor that seems doomed to fail, especially if it comes at a cost to their organization.
Subsequently, CIOs pitching AI projects to the rest of their leadership teams must convince other executives that investing in artificial intelligence is worthwhile. Unfortunately, quantifying ROI for AI projects can be complicated due to high costs and failure rates, in addition to skill shortages, according to The CIO Report for the first quarter of 2025 from Gartner.
If you’re a CIO wondering how to build a business case for AI, demonstrating the value of genAI for your organization will require a different approach than you’ve employed for other technologies and initiatives. Here are some expert recommendations to assist you in convincing your organization’s other executive leaders that investing in artificial intelligence will pay off in the long run, monetarily and otherwise.
1. Align your AI strategy and initiatives with your overall business strategy.
Your AI strategy should synchronize with your general business strategy and support the achievement of corporate objectives in the long run. You should also periodically update your AI strategy and realign it with your general business strategy when necessary, according to The CIO Report.
Similarly, the ebook “Stop Experimenting and Start Scaling Your AI Efforts” from Forrester states, “For AI to scale effectively and drive desired outcomes, technology leaders and business stakeholders must agree on why AI is being used and align the AI portfolio across corporate, functional, and individual use.”
2. Identify specific use cases and prioritize them by viability.
Gartner recommends defining your top AI use cases and prioritizing them based on business value and feasibility. Forecasting AI impact (which involves identifying a financial model and metrics) is also an essential element of the use case evaluation process, according to Forrester.
3. Manage expectations about the time frame.
The CIO Report warns that rushing AI projects can lead to poor decision-making and reputational damage. One of your vital responsibilities as a CIO involves managing other executives’ expectations and ensuring they understand that it might take a considerable amount of time to achieve the expected payoff from AI initiatives.
4. Consider how to fill AI skill gaps.
To turn the potential value of AI into reality for your organization, you’ll need the right skills – something that many companies lack. Nearly half (47 percent) of executives say their employees don’t have the knowledge and talent required to effectively adopt and scale AI across their organizations, according to IBM. Outsourcing to a managed service provider (MSP) can help you fill internal AI talent gaps and access pre-staged AI models.
If you’ve hit a roadblock on the path to successful AI adoption, we can help you move forward rapidly and confidently with a turnkey offering. We’ve combined professional services with a secure and compliant gateway to 12+ popular models (with new LLMs added as they emerge). The gateway relies on privately hosted infrastructure for maximum data integrity and ensures observability as well as governance, risk and compliance (GRC).
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