The AI boom is here again. This time, it is louder, more expensive, and often more misguided. Across industries, leadership teams are sprinting to adopt AI with urgency that often feels more reactive than rational. But saying “use AI” is like telling your team to “be innovative.” It is more a sentiment than a strategy.
In this moment of mass experimentation, some companies are finding signal amid the noise. They are not just layering AI on top of business as usual, but using it to solve problems that already mattered. And they are seeing returns not because they adopted AI, but because they understood why they were doing it.
More homework, less hype
“Every vendor is talking about their AI solutions,” said industry analyst and CMA Intelligence founder, Chris Marron. “There’s a lot of hype and some real substance too, but too many businesses jump straight to automation without understanding what they’re automating.”
That impulse to chase productivity gains, especially by cutting labor, is flawed and dangerous. “If you’re using AI to automate labor, you’re probably doing the wrong thing,” Marron explained. “When the total talent pool isn’t actually shrinking, cutting people just shrinks your footprint in the market. Use AI to help the same team deliver 40 % more, not to shed 40 % of the team.”
Beyond just bad math, the issue is also bad framing. Automation does not inherently translate to better customer experiences, increased revenue, or smarter operations. In fact, it can create more work if it is not paired with clear goals.
Why this AI wave is different
In past tech revolutions, from the rise of the internet to the advent of cloud computing, enterprise businesses held the advantage. They had the capital, infrastructure, and IT teams to adopt early, experiment widely, and scale quickly. This time, the AI curve looks different.
AI is the first major wave where being big may actually slow you down. “AI is reversing the traditional power dynamic in business communications. For the first time, small and mid-sized businesses can access enterprise-grade capabilities without the overhead,” said Dimitri Osler, CIO of Wildix.
With models and tools available off the shelf and open APIs making integration accessible, the AI arms race is being led not just by deep pockets, but by speed and clarity of purpose. Enterprise companies may still have scale, but they are no longer the only ones with power.
Use cases, not hopes
Lowe’s did not wander into AI. It went in with a map. “Success doesn’t come from chasing AI’s novelty,” said Chandhu Nair, SVP of Data, AI and Innovation at Lowe’s. “It comes from aligning its development with your core business values and long-term vision.”
That vision led to the creation of Mylow, a generative AI-powered assistant that helps both customers and associates. Unlike a generic chatbot, Mylow understands Lowe’s specific inventory, installation services, and customer pain points. For store associates, Mylow Companion provides the same level of support on the sales floor, spreading expertise across departments.
These tools were built to work. “If it doesn’t move the needle on conversion, efficiency, or customer experience, it doesn’t get built,” Nair said.
It is a stark contrast to what Marron describes as “go do the AI thing” KPIs. These are vague executive mandates with no tactical direction.
Agentic AI that actually thinks and acts
Wildix, a European-born communications company with a global footprint, has taken that strategic clarity and turned it into something practical with the recent launch of its embedded Agentic AI capabilities. Instead of building tools that simply respond to prompts, Wildix engineers AI that behaves like autonomous digital teammates, not for novelty’s sake, but to solve the right problems in the hands of the right customers
“In healthcare, we’ve seen our AI flatten the three daily peaks of appointment demand,” said Stewart Donnor, Wildix Global Head of Sales Engineering. “It handles routine bookings and inquiries around the clock, freeing human staff to focus on more critical issues.”
This is automation used to support humans, not replace them. And it is deeply tailored. Wildix partners with each client to identify their operational pinch points, then builds systems that solve those exact problems.
“The problem isn’t that leaders expect too much from AI,” said Steve Osler. “It’s that they expect the wrong things, faster output instead of smarter operations. It’s about saving time. And time, when given back to humans, becomes upsell, innovation, loyalty. That’s where the real growth lives.”
The brands that grow are the ones using AI to do more, not to eliminate people, but to amplify them.
Guardrails before glory
As AI becomes more capable, the risks grow. “The pace of AI development has exceeded even the most ambitious projections,” Nair noted. “That acceleration has required agility, but also discipline.”
Lowe’s maintains that discipline through its AI Transformation Office, a cross-functional team that includes engineering, legal, and product leaders. Every project is evaluated for value and risk. Lowe’s also integrates NeMo Guardrails from NVIDIA to ensure conversational safety and privacy across its AI systems.
Wildix takes a similarly careful approach. “Our platform is secure by design,” said Donnor. “We maintain strict data privacy compliance, including GDPR and HIPAA, and do not share customer data across tenants.”
This level of diligence matters more than ever. As Marron pointed out, “If your customer-facing AI gives bad information, that’s not just an error. That’s a new company policy. That’s what happened with Air Canada. You’re liable.”
A smarter adoption framework
To move from vague excitement to strategic implementation, companies need a better framework. One that starts with asking the right questions.
What specific task or problem do we want to solve?
What kind of data do we already have?
What outcome can we measure?
Where can humans stay in the loop?
Donnor describes their approach as starting small with practical tasks. “Start with the low-hanging fruit,” he said. Simple use cases that prove ROI fast, then expand. For some clients, that means using Wildix’s Kite widget to turn a website into a functional FAQ assistant. For others, it is a full-scale scheduling and triage automation.
“You don’t have to solve everything at once,” Donnor said. “But you do need to start with something real. If the AI can’t show ROI, it’s not ready.”
Post-Google search, AI portals, and brand relevance
As customer behavior shifts from search engines to AI interfaces, brands face a new challenge. How do you stay visible when users stop Googling and start asking ChatGPT?
We’re entering a world where AI becomes the first point of contact. That means brands need to build AI-ready customer portals now or risk losing the relationship entirely.
The biggest challenge is keeping control of that relationship. “The brand doesn’t want OpenAI to own that interaction,” Marron said. “So companies will invest in portals that offer fast, AI-driven support while still keeping the customer in their ecosystem.”
And that brings us back to the central theme. Not AI for its own sake, but AI as infrastructure. Not automation to shrink the business, but automation to stretch its capabilities.
Final word: you cannot outsource thinking
AI tools will continue to improve. The real question is whether your organization’s thinking will improve, too.
As Marron put it, “Don’t look at what you can automate away. Look at what you can enable with that.”
The companies that win this next chapter will not be the ones who jumped in first. They will be the ones who paused, asked better questions, and made AI do something useful.
That starts with being able to articulate the problem you’re trying to solve. Steve Osler warns, “The real risk isn’t bad AI. It’s lazy thinking. If you can’t explain what problem you’re solving, no tool will save you.”
VentureBeat newsroom and editorial staff were not involved in the creation of this content.
