The National CIO Review recently brought together key members of its technology leadership community and subscribers for an engaging discussion on AI adoption, data governance, and organizational readiness. The TNCR Live! event featured notable technology leaders and panelists from Redis, Bill.com, and Glean, who shared practical insights on overcoming skepticism and establishing an AI foundation aligned with both business objectives and regulatory compliance.
The collaboration focused on how the journey of AI in the enterprise requires fostering a mindset shift, building trust, and ensuring that technology adoption aligns with strategic priorities.
In the well attended session, all could agree that enterprise AI adoption presents a dual challenge: harnessing the power of innovation while managing potential risks. By examining how industry leaders are approaching these issues, leaders can better understand how to transform AI into a strategic asset rather than a daunting endeavor.
Overcoming Skepticism and Embracing Change
Despite its potential, AI still faces resistance in many organizations due to fears of job displacement, data privacy issues, and ethical concerns. Steve Januario, IT leader at Bill.com, addressed these common fears, noting how popular media often amplifies them by portraying AI as a threat to jobs and privacy. His approach? Counter skepticism with education.
Januario emphasized the importance of “leading by example” by incorporating AI tools to streamline routine tasks, automating low-risk processes to boost productivity. By showcasing how AI can make day-to-day work more efficient, leaders can create a ripple effect throughout the organization. It’s critical to emphasize that AI is a tool to assist, not replace, employees.
“Remember, AI is just another tool… it’s going to help us evolve, work smarter, and faster, but it’s not something we should fear. We should embrace it because it’s definitely not going away.”
Steve Januario, VP of Technology & Deputy CIO – Bill.com
Chris Asing of Redis echoed this sentiment, calling AI literacy the next wave of workplace literacy. He predicted that AI would soon become as commonplace in workplaces as cloud tools are today, urging companies to cultivate a culture where AI knowledge and fluency are prioritized and continuously developed. Asing emphasized that AI adoption is not a one-time event but a journey that requires ongoing education and adjustment as technologies evolve.
To start their AI journey, organizations should focus on defined business problems rather than speculative projects. Identifying specific, repetitive tasks that AI can enhance, such as summarizing documents or automating customer support, can help demonstrate AI’s tangible benefits to skeptical stakeholders. The idea is to start small, win early, and then expand, using each success story as a stepping stone to larger initiatives.
By taking incremental steps, companies can build momentum and overcome the hesitation that often accompanies large-scale changes.
Keys to Success:
- Lead by Example: Leaders should use AI tools themselves to demonstrate their value and normalize their presence in the workplace.
- Educate Continuously: Provide ongoing education to help employees understand AI’s role and alleviate fears about job displacement.
- Start with Tangible Wins: Focus on well-defined, repetitive tasks that AI can improve to build credibility and showcase quick wins.
- Promote AI Literacy: Treat AI literacy as a fundamental skill, similar to computer literacy, and integrate training into standard learning programs.
Strategic Investments in AI: Where to Begin
Successful AI adoption begins with a strategic investment that delivers real, measurable benefits. Both Januario and Asing underscored the need to focus on areas with significant business impact, such as customer experience and operational efficiency. Redis, for instance, has targeted its AI investments in go-to-market functions to enhance customer service and retention. Januario pointed out that focusing on customer-facing solutions often yields the most visible returns, making it easier to justify further investments in AI.
Beyond customer-facing applications, leaders should consider internal operations, like legal and procurement, for AI-driven workflow optimizations. For instance, AI can automate parts of contract review, saving time and resources. Asing noted that cost-saving, revenue-generating, and customer satisfaction-enhancing projects typically offer the highest returns on AI investments.
When AI initiatives directly impact key business metrics, they become easier to sustain and expand.
“The most common use cases [for AI] are: ‘summarize this,’ ‘give me a sense of that,’ ‘help me respond to this email,’…and that’s going to be very normal for us. But what gets the attention of leaders are problems that impact cost, drive revenue, or improve customer experience.”
Chris Asing, VP and Head of IT – Redis
However, AI tools alone aren’t enough. Januario stressed the importance of investing in employee training alongside AI technologies to reduce resistance and ensure these tools are effectively integrated into daily workflows. The goal is to build a workforce that is both confident and competent in using AI tools. This means creating opportunities for hands-on learning and ensuring that training programs are accessible and practical.
By pairing technology investments with people investments, organizations can maximize their return on AI initiatives.
Keys to Success:
- Prioritize Customer Impact: Focus initial AI investments on customer-facing functions to create visible returns and gain buy-in for further projects.
- Optimize Internal Workflows: Use AI to streamline internal processes like procurement and contract review to improve efficiency and reduce costs.
- Invest in Training: Develop hands-on learning opportunities to ensure that employees are comfortable and skilled in using AI technologies.
- Focus on Measurable Benefits: Tie AI initiatives to key business metrics, such as productivity gains or cost savings, to demonstrate their value clearly.
Addressing Privacy, Security, and Compliance
Privacy and compliance are major barriers to AI adoption, especially with regulations like GDPR and CCPA evolving. The TNCR Live panelists emphasized that AI should be treated like any other technology handling sensitive data, with strong governance frameworks to ensure data privacy, access controls, and regulatory compliance.
AI adoption without proper safeguards can expose organizations to significant risks. Asing explained that much of the success in deploying AI responsibly depends on maintaining strong internal controls over how data is accessed and used. He warned against using consumer-grade AI tools, such as public versions of ChatGPT, without careful oversight.
These tools, while powerful, may not offer the level of data security required by most enterprises.
Instead, leaders should establish clear policies and consider AI-native solutions designed to operate within secure, private environments. Januario recommended using retrieval-augmented generation (RAG) tools that allow organizations to safely retrieve relevant data without compromising sensitive information. This approach helps organizations strike a balance between leveraging AI’s capabilities and maintaining compliance with privacy regulations.
Companies can also work with legal and compliance teams to develop a governance framework that fits their specific needs, ensuring that every AI deployment is compliant from day one.
Keys to Success:
- Implement Strong Data Governance: Develop a robust governance framework to manage data privacy and regulatory compliance effectively.
- Avoid Consumer-Grade AI Tools: Discourage the use of public AI tools that may not meet enterprise-level security standards.
- Adopt Secure AI Solutions: Use AI tools specifically designed for secure, private environments to minimize data risks.
- Engage Legal and Compliance Teams: Collaborate with legal experts to ensure AI deployments are compliant with evolving regulations from the outset.
Encouraging Adoption: Practical Tips for Technology Leaders
Successfully adopting AI requires embedding AI into everyday workflows and demonstrating its value. Leaders must actively champion AI adoption by showing how these tools can enhance productivity and support employees in achieving their goals. AI integration should not be seen as a top-down mandate but as an evolution that benefits the entire workforce.
By addressing the unique needs and concerns of employees, technology leaders can foster an environment where AI is embraced rather than resisted.
“As leaders who were early adopters of AI, you’re paving the way for others. Once these strategies are implemented, it’s all about driving adoption, which often means embedding AI into workflows, showcasing benefits, and celebrating small wins along the way.”
Kat Crichton, Customer Success – Glean
Simply providing AI tools without a clear integration plan often leads to underutilization or even resistance. To truly harness AI’s potential, organizations need to integrate it thoughtfully into their existing processes, ensuring it complements current workflows rather than disrupts them.
Effective integration requires collaboration across teams to identify specific areas where AI can add value, streamline tasks, and reduce friction. Leaders must also ensure that AI solutions are accessible, easy to use, and aligned with the organization’s broader strategic goals.
By integrating AI into everyday scenarios, leaders can help employees see the immediate benefits and foster a culture of continuous improvement driven by innovation.
Keys to Success:
- Create Hands-On Learning Opportunities: Incorporate AI training into employee development programs. Hosting regular training sessions can boost employees’ confidence with AI tools, making them feel more comfortable integrating AI into their daily tasks. Training doesn’t need to be formal; it can include workshops, lunch-and-learns, or collaborative exercises where teams solve problems using AI.
- Showcase Success Stories: Highlight positive outcomes from early adopters or share case studies demonstrating efficiency gains from AI. This helps alleviate concerns and build enthusiasm. Januario suggested spotlighting internal “champions”—employees who have successfully integrated AI into their workflows—to inspire others and provide real-life examples of AI in action.
- Keep AI Within Familiar Tools: Minimize disruption by embedding AI functionality within platforms employees already use. Glean’s tool, for example, integrates seamlessly into browsers, allowing employees to find relevant information without needing to switch tools. By embedding AI into familiar environments, organizations can reduce friction and make AI adoption feel less daunting.
- Celebrate Small Wins: Recognizing teams or individuals who benefit from AI can reinforce its value and foster a culture of innovation. Celebrations can be as simple as shout-outs during team meetings or more formal recognitions like awards. Januario emphasized that these small moments of recognition help build momentum, turning AI adoption into a company-wide movement.
Metrics for Success: How to Measure AI’s Impact
Measuring the impact of AI can be challenging, especially when results aren’t always tangible. Januario suggested combining quantitative metrics, like time saved on routine tasks, with qualitative feedback from employees. Tracking the usage of AI tools and gathering insights from end users can reveal how effectively AI addresses business needs.
Financial metrics, such as cost savings and revenue increases, are also important. Redis and Bill.com use benchmarks like productivity gains, cycle time reductions, and customer satisfaction improvements to gauge the effectiveness of their AI initiatives. By connecting AI initiatives to concrete outcomes, leaders can make a compelling case for continued or increased investment.
In addition to internal metrics, Januario highlighted the importance of external benchmarks. Comparing AI adoption rates and results with industry peers can provide a clearer picture of how well an organization is leveraging AI. Are competitors seeing higher customer satisfaction scores because of AI? Are they reducing operational costs at a faster rate? Understanding these metrics can help companies calibrate their AI strategies to stay competitive.
Keys to Success:
- Use Quantitative Metrics: Track time saved on routine tasks, cost savings, and revenue increases to understand AI’s measurable impact.
- Gather Qualitative Feedback: Collect insights from employees using AI tools to determine how effectively AI meets business needs.
- Benchmark Against Peers: Compare AI adoption rates and outcomes with industry peers to ensure competitiveness.
- Monitor Usage Metrics: Track the frequency and extent to which AI tools are used to identify adoption levels and areas for improvement.
Future-Proofing AI Investments
With AI evolving rapidly, technology leaders need a strategy that allows them to stay agile while ensuring their investments remain relevant. Januario and Asing recommended building a strong foundation with scalable infrastructure, such as a company-wide data lake, to support future AI applications. The goal is to build an infrastructure that can grow with the organization’s AI capabilities rather than needing constant overhauls.
Regularly revisiting AI strategy is key to avoiding obsolescence—this includes assessing data quality, reviewing use cases, and monitoring industry developments. AI is not a set-it-and-forget-it technology; it requires continuous iteration. Asing emphasized staying informed about advancements in data governance and security to remain compliant and effective. Leaders should schedule regular reviews of AI projects to ensure they are delivering value and are in alignment with current business objectives.
Another critical aspect of future-proofing is building a flexible workforce. Januario stressed the need to invest in upskilling employees so they can adapt as AI technologies evolve. This includes not only technical skills but also strategic thinking about AI’s role in business.
When employees understand both how AI works and why it’s being used, they are better equipped to leverage it effectively and help the organization remain competitive in an AI-driven future.
Keys to Success:
- Build Scalable Infrastructure: Develop infrastructure, like data lakes, that can grow with your AI capabilities.
- Revisit AI Strategies Regularly: Continuously assess data quality, use cases, and industry trends to ensure AI remains effective and relevant.
- Invest in Workforce Flexibility: Upskill employees with both technical and strategic knowledge of AI to ensure adaptability.
- Stay Informed on Governance and Security: Keep up-to-date with advancements in data governance and security to maintain compliance and effectiveness.
Building a Culture of AI Literacy
AI adoption requires a cultural shift towards continuous learning, responsible innovation, and strategic investment. As the TNCR Live session revealed, a balanced approach combining AI literacy, data security, and practical application can generate significant value.
Creating a culture of AI literacy means that everyone, from the executive team to entry-level employees, understands AI’s role, capabilities, and limitations. By fostering this kind of environment, organizations can demystify AI, reduce fear, and encourage proactive engagement with AI tools leaders can make AI part of the organizational DNA.
Building this culture also involves ensuring that employees have the right tools and support to effectively engage with AI. This means providing training opportunities that go beyond technical skills, helping employees understand the broader context of AI’s role in their work and the company’s strategic objectives.
Leaders need to communicate the benefits of AI clearly and regularly, so that every member of the organization feels involved in the AI journey.
Another critical element is transparency. Employees are more likely to embrace AI if they understand how decisions are made regarding its implementation and usage. This involves openly discussing the limitations of AI, the potential ethical challenges, and the ways in which AI will be used to complement rather than replace human roles. Creating forums for open dialogue where employees can ask questions and share concerns can go a long way in building trust and acceptance.
Finally, establishing a culture of AI literacy means making continuous learning a priority. AI technologies are evolving rapidly, and staying updated requires a commitment to ongoing education. Leaders can foster this mindset by encouraging employees to pursue certifications, attend workshops, and participate in industry events focused on AI.
By making AI literacy part of the organization’s ethos, companies can position themselves to leverage AI more effectively in the future.
Keys to Success:
- Provide Contextual Training: Go beyond technical skills by helping employees understand AI’s role in their specific functions and in the organization’s overall strategy.
- Promote Transparency: Encourage open discussions about AI’s limitations, ethical challenges, and how it will be used to support rather than replace human work.
- Encourage Continuous Learning: Foster a culture of ongoing education by offering workshops, certifications, and access to AI-focused industry events.
- Make AI Part of Everyday Conversations: Regularly communicate AI’s benefits and progress to demystify its usage and encourage proactive engagement across all levels of the organization.
The Wrap
For CIOs and technology executives, the journey to successful AI adoption involves thoughtful planning and strategic implementation.
An effective AI strategy requires consistent alignment with broader business objectives, a clear framework for managing risks, and an approach that ensures employees feel supported and informed throughout the AI journey. Addressing data security, ethical concerns, and workforce engagement is crucial for minimizing resistance and maximizing the potential of AI within the organization.
By learning from the experiences of others and adopting a proactive approach, technology leaders can transform AI into a strategic asset that drives efficiency, innovation, and value. This requires being adaptable, monitoring AI’s impact, and refining strategies based on new developments and lessons learned.
A future where AI is effectively integrated into every aspect of work is not only possible but within reach, empowering employees, fostering innovation, and driving meaningful business outcomes.
