For individual users, the AI boom feels simple. If a new model looks smarter, you open a new tab, try it, and move on. No approvals. No integration work. No real downside.
Enterprise AI is a different story.
In the contact center and wider CX stack, AI is now part of the core infrastructure. It plugs into knowledge systems, quality monitoring, agent assist, and self-service. Swapping platforms is less like changing an app and more like rewiring your building.
As Kevin McGachy, Head of Solutions at Sabio, puts it:
“When AI moves from personal to enterprise level, you’re no longer dealing with a single user switching between Claude and ChatGPT over lunch; you’re dealing with deep operational dependencies.”
Yet many organizations are still chasing the “next best model” as if switching were low risk and reversible. But the hidden costs of that approach are starting to bite.
Enterprise AI: From Tool to Operating Layer
As discussed, at enterprise scale, AI now often sits directly in the flow of work.
“We’ve got clients with AI integrated into their knowledge management systems, quality assurance processes, agent assist tools, and customer-facing channels,” McGachy says.
“Each of these has been customized, fine-tuned, and connected to legacy systems.”
That’s the real shift from personal to enterprise AI. It’s no longer one person experimenting with prompts; it’s thousands of people, governed environments, and regulated processes, all depending on consistent behavior.
“The fundamental change is that enterprises need governance, compliance, and consistency at scale,” he explains, detailing how a consumer doesn’t care if their AI gives slightly different answers each day.
“But when you’re handling thousands of customer interactions, that inconsistency becomes a brand risk.”
There’s also the human side. Contact centers invest heavily in training agents on specific tools and workflows. Every platform change means retraining, lost productivity, and the sense among frontline teams that the ground never stops moving.
The Costs That Don’t Show Up on the Invoice
When enterprises move from one AI platform to another, they tend to focus on visible costs: tokens, migration projects, and integration work. What rarely makes it onto the business case is everything they lose in the process.
“Beyond the obvious migration and consumption costs, we’re seeing organizations lose months of accumulated context and learning,” McGachy says.
“When you switch from one LLM to another, you’re not just changing software – you’re losing all the prompt engineering work, the fine-tuning, the edge cases you’ve solved.”
Those details are what turn a generic model into something that really works for your customers, products, and policies. Wipe them out too often, and you never get beyond pilot mode.
Then there’s the human tax: what McGachy calls “transformation fatigue.”
“Teams that have just adapted to one AI platform are often being asked to pivot again, which can result in adoption stalls, innovation stops, and the contact center reverting to manual processes because people have lost confidence in the stability of the AI strategy.
McGachy details scenarios in which he has seen organizations pour more than a year into AI transformation, only to restart when a slightly better model emerges.
CTOs and CIOs in an Impossible Decision Cycle
This pace of change is colliding with old decision-making habits.
“The conversations I’m having with technology leaders aren’t about protecting themselves – they’re genuinely paralyzed by the pace of change,” McGachy says.
“One CTO recently told me, ‘By the time we’ve completed our procurement process, there’s already a better model available.’”
The traditional pattern of picking a strategic platform and committing for five to seven years doesn’t match a market that evolves quarter by quarter.
“What they’re struggling with is that traditional IT decision-making… simply doesn’t work in AI,” he explains.
To combat this, McGachy advocates for a shift in mindset, where instead of choosing technology, leaders design for interchangeability.
Ironically, this strategy actually complements AI rather well, as when the tech is architected deliberately, it lends itself to flexibility, as he explains:
“In the AI space, unlike traditional enterprise software, it’s much quicker and easier to enable this flexibility.
“APIs are more standardized, and the abstraction layers are more mature. But this does require a different mindset than they’re used to – indeed, the industry is used to.”
Beyond Lock-In: Orchestrate Models, Don’t Bet on One
A common reaction to AI volatility is to pick a hyperscaler and go all‑in.
While there are undoubtedly merits to this approach, particularly in terms of procurement simplification, it also concentrates risk and can make it harder to capitalize on breakthroughs.
Instead of betting the house on one or two providers, Sabio is advising to “think in terms of AI orchestration rather than AI platforms.”
Instead of one model doing everything, Sabio encourages clients to use different models for different jobs, all hidden behind a single orchestration layer.
“For example, you might want to be able to use OpenAI for creative tasks, Anthropic for analytical work, and maybe a specialized model for voice transcription, all orchestrated through a single layer,” he explains.
“The key is building an abstraction layer that lets you swap models without rebuilding integrations or custom engineering.”
“Think of it like having a universal remote rather than being locked to one TV brand. This isn’t about being vendor-agnostic for the sake of it – it’s about being able to adopt breakthrough capabilities as they emerge without disrupting your entire operation.”
“That’s where working with an AI-first CX specialist like our team at Sabio is extremely beneficial – this is our approach to building the latest solutions for our clients, we know what’s out there and what’s coming down the line , and we can help you navigate that seamlessly,” he says.
From Model-Led to Outcome-Led
If the underlying models keep changing, the anchor can’t be the model name. It has to be the outcome, as McGachy explains:
“Instead of saying ‘we’re implementing GPT-4,’ you say ‘we’re reducing average handle time by 30% through AI-powered agent assist.’ The technology becomes interchangeable.”
Behind the scenes, that might mean blending several models in a single journey.
“At Sabio, we might use three different LLMs in a single customer journey – one for intent recognition, another for knowledge retrieval, and a third for response generation,” he says.
“The client doesn’t need to know or care which models we’re using; they just see the outcome: faster resolution, happier customers, lower costs.”
The payoff becomes obvious when the market moves again, as it means that Sabio can swap in the newer, superior model without the client having to re-architect anything.
As this technology continues to evolve, AI leaders will keep leapfrogging each other, and benchmarks will keep shifting.
The organizations that come out ahead won’t be the ones that jump at every new logo, but the ones that design for change – keeping their AI strategy tied to business outcomes instead of platform hype.
You can find out more about Sabio’s contact center approach by checking out this article.
You can also discover Sabio’s full suite of services and solutions by visiting the website today.
