Members of Frost & Sullivan’s Customer Engagement Leadership Council recently participated in a group discussion designed to generate ideas and problem-solve about shared challenges implementing and integrating AI in the enterprise. Read on for key details and insights:
Patrick Lawton, Managing Director, Azantx, guided participants through an in-depth exploration of strategies to streamline AI deployment within organizations. The discussion focused on breaking down barriers to adoption, identifying what was working, and addressing persistent challenges.
Key topics included aligning AI initiatives with business objectives, ensuring data readiness, navigating operational complexities, and overcoming cultural resistance. Through shared insights and experiences, the group explored practical solutions to accelerate AI adoption and maximize its impact.
Key brainstorm challenges covered:
- How to align AI initiatives with business objectives for maximum impact: AI efforts often focus more on technological innovation than solving real business problems.
- How to ensure high-quality data and availability for effective AI implementation: Many AI projects fail due to insufficient or poor-quality data.
- How to overcome cultural resistance and drive AI adoption within the organization: Resistance to change and a lack of understanding of AI’s benefits can hinder successful deployment.
Attending members included contact center and customer experience vice presidents, user experience managers, learning and development specialists and others customer contact professionals.
Strategy First, Technology Second
Moderator Patrick Lawton opened the session by noting the evolution of AI from robotic process automation (RPA) to intelligent AI agents to the coming of age of open AI platforms. He emphasized that AI is not just an emerging technology; for those who implement it strategically, it’s a true game-changer. However, success with AI isn’t about jumping on the bandwagon – it’s about getting it right.
Patrick led participants to consider their approach to AI, emphasizing that it should never be viewed through a “technology-first” lens. Instead, organizations must start by clearly defining their business objectives. The key question is not “Where should we invest in AI?”, but rather, “What business problem are we trying to solve? Only after identifying core challenges, whether reducing costs,
enhancing customer experiences, or driving revenue, should AI solutions be explored, tested, and deployed accordingly.
Patrick emphasized that AI must generate tangible business value. “AI should have a financial impact, enhance the customer experience, and solve real-world problems,” he stated. Without a clear alignment between AI initiatives and business strategy, companies risk investing in technology that overpromises but underdelivers.
Several members shared their experiences exploring AI applications for their organizations or implementing AI. One member shared that they were focusing on using AI for internal processes as a start. Part of their method was to use it to try and solve problems. They had a systematic process for evaluating proof of concept, which helped determine whether to move forward. They had experienced both successes and failures with AI. They planned to use it for client interactions as they became more comfortable with the technology and testing.
A few members noted that they had created AI councils or centers of excellence to share information about AI and its capabilities and to help get stakeholders on board. They sought to ensure that teams across departments were aligned and invested in AI outcomes.
Ensuring Data Integrity
Patrick highlighted that AI’s effectiveness is only as strong as the data it learns from. Poor-quality, biased, or fragmented data can significantly hinder AI outcomes. Participants were encouraged to evaluate their foundational data sets, ensuring accuracy, completeness, and consistency before deploying AI. Patrick emphasized that data oversight is critical, companies must invest in data governance to maintain high-quality inputs. Without clean, structured, and unbiased data, AI models risk amplifying errors rather than optimizing decisions.
Cultural Adoption and Workforce Readiness for AI Success
Patrick Lawton addressed what he described as potentially the biggest challenge in AI adoption today – the human factor. AI’s impact on jobs and livelihoods is profound, and resistance to change understandably remains a major barrier.
AI adoption isn’t just a technological shift – it’s a cultural transformation. Patrick emphasized that success depends on employee buy-in and strategic workforce adaptation. AI will inevitably reshape roles, but organizations that take a proactive approach clearly communicating AI’s benefits, providing reskilling opportunities, and aligning AI with human expertise will see the greatest long-term success.
Discussions focused on the importance of transparent leadership in easing fears and fostering trust. Instead of allowing uncertainty to breed resistance, leaders must position AI as an enabler, not a replacement showcasing how it can enhance productivity, elevate job roles, and open new career pathways.
To this end, members were asked: how is your company preparing for AI adoption? What plans are in place? In response, a member shared that his organization had created a center to help staff understand how AI Virtual Assistants would take over simple calls. They also reminded agents that they would still need them for more complex calls and other work. As noted, the customer contact industry has a high AI adoption rate. It changes agent jobs, usually starting by having bots take rote or easy calls.
Another member shared that they used AI on smaller scale, to help with internal documentation. His company assured workers that new AI tools would promote efficiency and consistency, and free them up for more high value agent tasks. This approach was going very well for them.
Yet another member shared that her team was nervous, but she was framing AI as a helpful tool, articulating why the company was starting to implement it and sharing their AI strategy. Another member stated that their organization was still in the AI planning stages but was encouraging employees to experiment with AI Copilot in their daily work. The idea was to help them get comfortable with the tech and experience some of AI’s efficiencies for themselves. They were also encouraged to use AI as a creative tool, for design projects and even contests. All in attendance agreed it was a great way to de-mystify AI!
As one member summed it up, “The AI launch is not the end…it’s the beginning…now or eventually the team will see its effects. Keep communication open and transparent as things progress.”
Brainstorm Key Takeaways
As the virtual discussion began to conclude, these three key takeaways were reiterated:
- Align AI with the business strategy
- Ensure high quality data and governance
- Build a culture of AI adoption
Summary
AI isn’t a futuristic concept – it’s happening now. We’re at the start of massive AI adoption in business. And the companies that succeed with AI will be the ones that are building AI-ready organizations focused on strategy, data, and people – those companies will lead their industries in the next decade.
Patricia Jacoby, Senior Content Strategist at Frost & Sullivan, produces and frequently writes for the company’s monthly Customer Engagement and quarterly Innovation Newsletters. Throughout her tenure at Frost & Sullivan she has covered numerous industry sectors, including customer contact, marketing, innovation, and manufacturing, writing blogs, executive summaries, and white papers. She also supports and writes executive briefs for Frost & Sullivan’s Growth Innovation Leadership Council and Customer Engagement Leadership Council.
