Techaisle Analyst Insights

Attrition, not missing features, is the contact center's real problem. Techaisle's study of 3,980 organizations shows the fix is a governed AI workforce. The surprise: the most tightly controlled companies give their AI agents the most freedom.

Ask a contact center manager what their hardest operational problem is, and the answer is rarely a software gap. It is that agents keep quitting. In Techaisle's data, attrition is the single biggest challenge contact centers report. The cost of running the operation, the thing most vendors pitch against, actually fades as a worry as companies grow, from 47% of firms citing it down to 16%. The contact center looks like a technology problem. It is a staffing problem wearing a technology budget.

The workforce is getting harder to manage

The contact center is the fastest-growing part of the communications market. Agents rise from 4% of the workforce in the smallest firms to 14% in the largest; about 70% of midmarket companies run more than 100 agents, and the real budgets appear in the 100-to-999-employee range.

The work is not going away. Inbound phone calls stay near-universal, at 90% to 95%, so the call is still the core of the job. What changes is where the people sit. More agents work from the office again, up from 43% to 58%, and more work is handed to outside firms, with outsourcing rising from 12% to 33% and offshore staffing from 8% to 30%. A midmarket contact center is now running a workforce that is bigger, more scattered, more outsourced, and still walking out the door. A longer feature list fixes none of that.

The fixes that help, and the one that matters

Most AI pilots do not stall because the technology failed. They stall because the buyer and the partner are standing at different points on the same path. Drawing on studies of SMB, midmarket, and channel partner populations, I built the AI continuum to show where those points are, what gets bought at each one, and how customers pay for it.

Three years ago I published a slide mapping how small and midmarket firms moved along the cloud continuum. It traveled further than almost anything else I have produced, mostly because people put it into their own decks and argued with it. I have now rebuilt it for AI and agentic adoption, because the questions vendors and distributors are asking me this year are the same questions they asked about cloud, and the honest answer is again that the market is not one market moving at one speed.

Here is the argument in a sentence. Demand exists at every stage of AI adoption. Partner supply does not. And the gap between the two widens the further right you go.

Continuum diagrams are usually somebody's hypothesis drawn neatly. This one rests on Techaisle's 2026 survey programs across small business, midmarket, and channel partner populations, running to thousands of respondents globally, and on the qualitative work around them: structured interviews, vendor and distributor briefing sessions, and a large number of unstructured conversations with buyers and partners. The surveys established the stages and the volumes. The conversations established the sequence, and sequence is the part a questionnaire cannot reach.

Why a continuum instead of a maturity model

Maturity models rank companies. They tell a vendor which customers are ahead and which are behind, which is a comfortable thing to know and a useless thing to sell against. Every maturity model ends up recommending that the laggards catch up.

A continuum does something different. It maps what a customer actually buys at each stage, who they buy it from, and how they pay for it. That turns the picture into a route. A partner can look at a continuum and see the specific line item they are missing. A vendor can look at it and see which stage their program is funding and which stage their marketing is describing, and those are frequently not the same stage.

The version I have built runs on the same spine as the original: partner entry point across the top, partner revenue flywheel through the middle, end-point on the right, current state to future ready along the bottom. Four stage columns, with five layers of data underneath each one.

For partners serving the SMB and midmarket, the security conversation has changed in a way that resets the profit equation. Security has stopped being a discretionary line item that customers fund when they get around to it. The reason is not abstract. Identity-related attacks have moved to the center of how breaches happen, and attackers armed with stolen credentials and AI-assisted social engineering increasingly bypass multi-factor authentication by logging in rather than breaking in. The control surface that buyers most need to defend has shifted from the network to the identity itself, and the budget is following suit.

Techaisle’s 2026 SMB and Midmarket Security study shows that the organizations these partners serve are not only spending more on security, but also restructuring how they buy it. Over 60% of Core Midmarket organizations plan to increase security budgets by 5% or more in 2026, and 28% of the Upper Midmarket plan to increase budgets by more than 15%. The pattern reads as a structural reset rather than a fear-driven spike. Buyers have accepted that breach inevitability has moved downmarket, and that identity is now the control surface that matters, which is why protecting the identity plane ranks as the single highest security priority across the SMB market.

For the smaller partner and the MSP, that reset is the largest unclaimed annuity in the channel. The question is whether the partner has the architecture to capture it.

Key Takeaways

• The advantage is not the AI PC itself. It is the breadth of device categories Lenovo can integrate and manage through a common software and services layer, which is what lets a single agent work across an entire fleet. A vendor focused primarily on PCs has fewer categories to work across.
• The FIFA World Cup 2026 proved the model at scale. More than 26,000 Lenovo and Motorola devices across three countries, deployed and managed as one fleet, with FIFA highlighting Lenovo’s rapid deployment and managed lifecycle approach as helping accelerate operational readiness in weeks rather than months.
• The constraint is go-to-market, not portfolio. MSPs influence 61% of SMB PC decisions, yet only 34% of SMBs say their MSP explains the business value of an AI PC. Closing that gap is the highest-return move available to Lenovo today.

For the past two years, almost every conversation I have had about the AI PC has been a conversation about a single device. How many TOPS. Which NPU. How the battery holds up running a model locally. Vendors brief on it, partners repeat it, and buyers listen politely before asking the only question they actually care about: what any of this does for their business on Monday morning.

I have come to think the industry has been measuring the wrong thing, and that the mistake is more basic than any argument about silicon. We keep asking a single device to deliver value that is not created on a single device. Almost nobody does their job on one screen anymore.

Watch how a piece of work moves through a company today. A proposal gets drafted on a laptop, discussed in a message thread on a phone, approved on a tablet in the back of a taxi, and then picked apart the following week on a workstation by someone in finance who was never in the original meeting. No single device holds that story. The work lives across all of them, and so does the context that explains it.

That distinction matters enormously once you put an AI agent into the picture, because an agent is only as useful as the context it can actually reach. An agent that lives on the laptop and nowhere else is reasoning about a fraction of what happened. It will summarize the document but miss the decision, because the decision was made on the phone. Techaisle research consistently shows buyers reporting the same frustration in different words, and it is the reason so many AI PC deployments have been underwhelming in practice even when the hardware was perfectly capable.

"We keep asking a single device to deliver value that is not created on a single device," Anurag Agrawal .