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CIO POV: Impactful AI Programs Start with ‘Why’

Published Date
Author Omer Grossman

Abstract blog feature image: Cloud computing transfer big data on internet. futuristic digital technology. Generative AI (GenAI).

Generative AI (GenAI) has the power to transform organizations from the inside out. Yet many organizations are struggling to prove the value of their GenAI investments after the initial push to deploy models.

“At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value,” according to Gartner, Inc.1

This sobering prediction is not entirely surprising. In these early days, most enterprise GenAI initiatives require hefty upfront investments. Rushing into a deployment without a proper framework, governance and guardrails makes it very difficult for organizations to advance at a constructive pace and achieve sufficient ROI (if they can measure it at all). Doing so can also lead to a buildup of technical and cybersecurity debt.

Start with ‘Why’ Are We Doing This?

Shiny object syndrome is highly contagious, as we’ve seen with previous technological revolutions. To avoid catching it, CIOs and technology leaders must start with the “why”—the true purpose behind their technology decisions—before determining “what” technology to use and “how” to go about it.

AI may be the best value driver for specific use cases, while automation and other more mature approaches may be best suited for others. Focusing on “why” can help technology leaders make this determination confidently. When AI is the best path forward, three factors are critical for sustained success.

Three Ingredients for an Impactful GenAI Implementation

1. User Adoption

User adoption is the single greatest success metric of any technology implementation. But that doesn’t mean rolling out a new GenAI tool to everyone all at once is the right approach. In most cases, it’s best to start small and focused.

Begin by tackling one critical use case for one specific group, such as automating tedious document review and summarization for the legal department or training a bot on technical customer support documentation to enable the sales team. Remember—it’s all about adding value to the use case. Once you’ve achieved some demonstrable wins and reached your target adoption rate, expand to new areas and groups.

Throughout our internal organization’s phased GenAI rollout, I’ve observed a few things worth sharing:

  • Track adoption within each phase closely and communicate often. If adoption is low within a particular group, meet with managers to discuss hurdles and brainstorm ways to overcome them. Encourage usage by sending automated reminders to individuals who haven’t used the tool recently and, if necessary, redistribute licenses that aren’t in active use.
  • Expect each new implementation phase to bring an adoption dip as users ramp up and familiarize themselves with the tool. Double down on support and enablement (more on that below) instead of getting discouraged.
  • Never underestimate the role of data management in successful AI initiatives. You need good governance around processes from the start to ensure models are trained properly with high-quality data. Make “capture, classify and clean” your mantra.

2. Manager Champions

I use GenAI to enhance my work routine and, as my team will attest, it’s hard for me not to slip GenAI into daily conversations. I’m not just passionate about this technology—my job as CIO is to model and champion our internal GenAI strategy.

Managers across the business—from HR and legal to IT and R&D—play a critical role in driving GenAI adoption and impact, helping teams integrate new tools into their workflows and change how they get things done.

A recent BCG study pinpoints three key behaviors that highly effective managers exhibit on this front:

  • They “walk the talk” by immersing themselves in GenAI. Managers in the study’s top quartile spent 229% more time experimenting with and using GenAI in one month than managers in the bottom 25%. To echo researchers, “This behavior creates a virtuous circle: high-using managers have higher-using teams, and high-using teams have higher-using managers.”
  • They care about their people and create an environment where employees feel empowered to experiment. For example, one surveyed leader asked employees to compose a poem to share with the team—a fun, low-risk activity that got people testing and talking about the tool. These actions matter: according to the study, employees who report that their managers care about them have a 14% higher GenAI usage rate.
  • They believe in—and communicate—the “why.” The study’s top-quartile manager participants held the highest personal belief that GenAI improves work. It’s essential to educate employees about the business benefits of GenAI and how new tools can enhance their daily routines and make work more rewarding. Study participants who believe they need GenAI for their work reported 66% higher GenAI usage than those who did not.

3. Consistent Enablement

Employees need the right resources to navigate new ways of working. And when it comes to enablement, consistency is critical.

At CyberArk, we’ve rolled out a series of GenAI-focused training initiatives, such as:

  • Regular company-wide emails featuring how-to video snippets and team success stories (i.e., XYZ team achieved XX% productivity gains this quarter). We also use these communications to highlight newly added features to our enterprise GenAI solution that employees will find useful.
  • An ongoing webinar series featuring common GenAI-powered business cases—such as building a meeting presentation from ideation to outline to design—that appeals to cross-functional audiences.
  • Hands-on workshops for specific business groups, digging into tactical ways to improve AI prompts and optimize outputs.

Our growing knowledge library contains our employee enablement materials, which people can access and learn from on demand. We’ve also built feedback mechanisms into our program and conduct regular check-ins with team leaders to understand what’s working and what’s not and how we can continuously improve.

The Last Word: Building AI Programs That Last

There’s a lot of AI hype out there—and much of it is real. It’s exciting and unprecedented and, like all innovations, must be considered carefully. By leading with “why” and focusing on user adoption, top-down support and enablement, technology leaders can sidestep common AI hurdles, demonstrate tangible business value and build impactful AI programs that last.

1 – Gartner® Press Release, “Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025,” 29 July, 2024. https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Omer Grossman is the global chief information officer at CyberArk. You can check out more content from Omer on CyberArk’s Security Matters | CIO Connections page.