I’m thrilled to sit down with Matilda Bailey, a renowned networking specialist with deep expertise in cutting-edge technologies like cellular, wireless, and next-gen solutions. With her extensive experience in integrating innovative tools into enterprise environments, Matilda offers a unique perspective on how businesses are navigating the evolving landscape of online AI tools. In this conversation, we’ll explore the challenges and breakthroughs enterprises face with AI, from early disappointments to newfound value, the specific applications driving success, and the future potential of these technologies in transforming the workplace.
How did your initial experiences with early online AI tools shape your perspective on their potential in an enterprise setting?
When we first started experimenting with online AI tools, I was optimistic but quickly saw the limitations. Many of these early tools, especially the basic chatbots, promised efficiency but often ended up being more of a distraction. Employees spent a lot of time tinkering with them without seeing tangible results. However, there were glimmers of potential in specific areas like automating repetitive data entry tasks for our IT support teams. It wasn’t transformative, but it hinted at what might be possible with better focus and refinement.
What was your company’s response to the early setbacks with generative AI, especially during what’s been called the ‘trough of disillusionment’?
We definitely felt that disappointment firsthand. Initially, we dialed back our experiments because the return on investment just wasn’t there. Most of our trials showed minimal impact—only a small fraction of our workforce, maybe around 10%, saw any real business value, and even then, it was marginal. But instead of abandoning AI altogether, we shifted to a more strategic approach, focusing on identifying specific use cases where AI could make a measurable difference rather than applying it broadly across the board.
The data suggests that only about 28% of computer-using employees are knowledge workers who can significantly benefit from AI. How does this align with your workforce dynamics?
That statistic resonates with what we’ve observed. In our organization, we’ve identified that our data analysts, network architects, and strategic planners—roughly a quarter of our computer-using staff—fall into that knowledge worker category where AI can add substantial value. For the rest, like our frontline support staff, the benefits are minimal at best. We’ve seen some small time savings in tasks like drafting emails, but these are often offset by errors or AI-generated content that needs heavy editing, which can be frustrating.
What shifted in your approach to AI that helped uncover more value, especially considering the jump to 85% of enterprises recognizing its potential?
The big shift for us was moving away from generic AI applications to more targeted solutions. We started focusing on tools that could integrate with our existing systems, like business intelligence platforms, rather than standalone chatbots. This allowed us to leverage AI for deeper data insights that our teams couldn’t easily uncover on their own. There was a specific project where we used AI to analyze network performance data across multiple sources, and the actionable insights we gained—identifying bottlenecks we’d missed—really turned heads internally and showed us AI’s true potential.
Have you explored embedding AI agents into business intelligence platforms, and if so, what impact has that had on your operations?
Yes, we’ve been actively working on embedding AI agents into our BI platforms, and the results have been promising. It’s helped us uncover trends in network usage and predict potential failures before they happen, which has saved us significant downtime. The time savings are real—our analysts can now focus on strategy rather than sifting through raw data. The main challenge was the initial setup; aligning the AI with our specific data structures took some trial and error, but once we got it right, the quality of insights justified the effort.
Beyond basic chatbots, have you experimented with more advanced interactive AI agents, and what has been your experience with their capabilities?
We’ve started testing some of the more advanced interactive AI tools, particularly those that can generate in-depth reports and multimedia outputs. One tool we’ve trialed offers detailed research summaries, which have been incredibly useful for preparing market analyses for our wireless solutions. The depth of content, complete with references, often rivals what a dedicated researcher might produce. We haven’t fully explored audio summaries like podcasts yet, but the potential for training and internal communication is exciting, and we’re planning to dive deeper into that soon.
What is your forecast for the role of AI tools in enterprise environments over the next few years?
I believe AI tools will become indispensable in enterprise settings, much like cloud computing did a decade ago. We’re already seeing a shift toward more specialized AI agents tailored to specific roles and industries, which will drive even greater adoption. For networking and next-gen solutions, I expect AI to play a critical role in predictive maintenance, security threat detection, and optimizing connectivity in real time. The key will be balancing innovation with practicality—focusing on tools that deliver clear ROI while addressing concerns like data privacy and copyright. I think we’re on the cusp of a major transformation, but it’ll require careful navigation to fully realize AI’s potential without getting lost in the hype.
