- September 3, 2025
- FOXITBLOG
AI may dominate headlines, but many organizations are still struggling with how—and when—to use it effectively. Too often, leaders chase hype-driven solutions without asking the most important question: what problem are we really solving?
Matthew Jones, Chief Technology and Communications Officer at Greenhouse Group, put it plainly: “AI is a tool. Sometimes it’s the right tool. Sometimes it definitely isn’t”. That mindset is critical in a business climate where every vendor promises “innovation” and every new product claims to be indispensable.
The Problem with AI-as-a-Default
When companies lead with AI first instead of identifying root problems, they risk inefficiency and wasted spend. Chatbots are a prime example. Many were rushed to market as proof of being “AI-powered,” yet delivered clunky, impersonal experiences. As Jones noted, “It just seemed to be the whole sum of all this hype ended up on chatbots”.
The takeaway? AI should never be a solution in search of a problem. It should be evaluated alongside speed, accuracy, cost, and customer experience—the same way you’d evaluate any other tool.
Cutting Through the Hype
Marketing buzz plays a big role in AI’s adoption curve. Claims of cost savings and efficiency push companies into “fear of missing out” mode. Yet few vendors reveal the true methodology behind their ROI claims. That’s why tech leaders need a healthy dose of skepticism and the ability to manage stakeholder expectations.
“Once you can cut through the hype with some examples, it helps carry that conversation through,” Jones explained, emphasizing how trust and transparency are vital when boards and executives are swept up in hype.
Why Data Comes First
AI is only as strong as the data behind it. Many businesses still struggle with data quality, governance, and integration. Deploying AI before fixing these foundational issues only magnifies inefficiencies.
“AI is only useful with good quality data,” Jones stressed. Solid processes and reliable data pipelines are non-negotiables before layering on machine intelligence.
Balancing Efficiency with Human Connection
While automation can enhance personalization and streamline workflows, it cannot replace human context. Summaries, predictive suggestions, and automated responses may save time, but they often miss nuance. “Context is all. AI will always struggle with context. That’s why having that human interaction is still so important,” Jones explained.
Interestingly, the rise of AI may actually increase the value customers place on authentic human interaction. Younger generations, often assumed to be most tech-dependent, are showing resistance to impersonal automation, preferring real conversations when it matters.
Real Innovation vs. Theater
Perhaps the most important insight is distinguishing genuine innovation from “innovation theater.” Flashy demos and vanity projects may look exciting, but real innovation is measurable, sustainable, and problem-driven.
“Innovation should be measurable. If it’s flashy and short-lived, then it’s just theater. It’s not real innovation,” Jones said.
FAQs
Q: What’s the biggest mistake companies make with AI?
A: Starting with AI as the default solution instead of identifying the actual business problem.
Q: How can leaders evaluate whether AI is the right tool?
A: Use metrics like speed, accuracy, cost, and customer experience to compare AI against other available tools.
Q: Why is data quality so important?
A: Without good data, AI amplifies inefficiencies rather than fixing them. Clean, reliable data must come first.
Q: Does AI replace human connection?
A: No. AI can automate tasks and speed processes, but context and trust still rely on human interaction.
Q: How do you spot real innovation versus hype?
A: True innovation is measurable, sustainable, and solves a real problem without introducing unnecessary complexity.
📌 Want more insights on building AI strategies that actually work? Watch the full conversation with Matthew Jones.
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