The rapid adoption of AI is creating an entirely new consulting opportunity for partners as organizations struggle to understand and control the cost of AI consumption, according to industry analyst firm Omdia.
“As we move into this world of tokenization, there is an entirely new economic reality which is emerging,” said Omdia’s chief analyst Alastair Edwards. Edwards and his colleagues were presenting at Omdia’s channel transformation event earlier this month. Omdia and Channel Dive are both owned by Informa TechTarget.
Edwards argued the shift is opening the door to a new wave of consulting and advisory services as customers look to understand the financial implications of AI adoption.
“As you increase the adoption of AI, your costs associated with that, or the consumption of tokens goes up exponentially. This is something that most organizations don’t fully understand, and it’s creating real worries for customers in terms of how they can actually balance that.”
From licenses to consumption
Unlike traditional software purchases where costs are largely predictable, AI usage can fluctuate dramatically depending on how widely organizations deploy models and how intensively they are used. That uncertainty is creating new questions for customers.
“How do they bring AI more into the organization without leading to a massive increase in costs?” asked Edwards. “That’s leading to a new era of FinOps opportunities, helping customers to manage or to define the right cost structure.” [Ed. note: FinOps is the practice of tracking and controlling cloud spending.]
Infrastructure choices become strategic
Edwards said the conversation extends well beyond managing token costs. As organizations become more dependent on public LLMs hosted by hyperscalers, many are beginning to question whether those platforms will remain the most cost-effective option over time.
“If you’re exposed to the big public LLMs running on hyperscalers, then you’re entirely exposed to the massive increases that could come from that sort of token consumption,” he said.
That’s prompting more discussions around alternative deployment models.
“What we’re seeing now is a lot of discussions around how you build a more open source-led model, perhaps delivered on your own infrastructure,” said Edwards.
“Partners are actively talking to customers about how they can build that next-generation infrastructure layer for AI.”
Building the hybrid AI stack
Edwards said those conversations are driving demand for a new generation of hybrid AI architectures combining public cloud services with private and edge environments.
“This creation of a new hybrid AI tech stack is going to open up big opportunities for partners, connecting public, private, and edge environments for that future AI architecture layer,” he said.
“These hybrid models, they have to be secure, they have to be sovereign, and they have to be compliant.”
That, in turn, creates opportunities that extend well beyond infrastructure sales. “It creates new opportunities for partners to deliver business models around the advisory, the design for customers, the deployment of those technologies, and ultimately the management of those through their managed services,” he said. “Managed services is one of the biggest opportunities that we see for partners around this.”
Edwards also said that AI is making technology decisions significantly more complex, strengthening the role of trusted advisers.
“This is an industry that’s getting more complex, and where partners can really help reduce that complexity for customers,” said Matthew Ball, Omdia’s chief analyst for cybersecurity channel strategy, .
Edwards added that uncertainty around AI infrastructure demand and token economics means organizations are unlikely to have all the answers themselves.
Token costs vary significantly between different AI models and deployment approaches, Edwards noted. That creates more uncertainty and, for partners, more opportunity. “We’re still not clear about how much capacity is really going to be needed,” he said.
