2026 may be remembered as the year AI agents moved from impressive demos to real business interactions. The technology is advancing quickly; the business models, operating practices, and expectations around it are struggling to keep up. In a recent BCStrategies podcast, five of our analysts and consultants, Tom Brannen, Blair Pleasant, Kevin Kieller, Chuck Vondra, and Steve Leaden, compared notes on what stood out most this year across customer experience, unified communications, enterprise strategy, and hands-on consulting. The common thread: AI's potential is enormous, but realizing it takes far more than deploying another agent. Tom Brannen: When AI Agents Become the Customers Personal AI agents are starting to research, book, buy, and request support on behalf of consumers. This is no longer theoretical: a major hotel brand is already fielding contact center calls initiated by AI agents. That raises questions traditional CX models were never built to answer. How do you authenticate an agent acting for a customer? Does it get the same service priority as a human caller? How do upselling, loyalty, and sentiment measurement work when the immediate participant is software? The takeaway: businesses will need to design experiences for both human customers and the AI agents representing them. Blair Pleasant: CX Is About Outcomes, Not Interactions The focus is shifting from handling and routing interactions to the complete customer journey and relationship, and from answering questions to completing workflows and resolving problems. Blair pointed to Zoom's "conversation to completion" message and Cisco's emphasis on relationship management as examples. If AI agents are digital workers, they need to be supervised, governed, and measured like workers. And with personal agents on one side and business agents on the other, agent-to-agent interaction looks set to be one of the defining CX challenges of 2027. The takeaway: the differentiator is moving from efficiently managing interactions to delivering meaningful outcomes across the whole customer relationship. Kevin Kieller: AI Has Thrown Enterprise Pricing Into Disarray Subscription licensing was predictable; consumption pricing, AI credits, and usage-based models are not, and customers often have no reliable way to forecast usage before they deploy. Kevin described the market moving from "token maxing" to "token axing" as organizations shift from encouraging experimentation to controlling costs. If an agent is positioned as a digital employee, what happens when its monthly allowance runs out mid-month? Which department pays when one agent serves several? Outcome-based pricing sounds appealing, but defining a successful outcome (was the issue really resolved if the customer calls back?) is far from simple. The takeaway: AI pricing must become more predictable, transparent, and outcome-aligned before organizations can build critical workflows on it. Chuck Vondra: The Problem Isn't Hype; It's Unrealistic Expectations Many organizations are enthusiastic about AI but haven't defined what they want it to accomplish. Chuck described a credit union that bought an intelligent virtual assistant and expected it designed and live within two months, overlooking the substantial requirements, integration, and testing work involved. As Kevin put it during the discussion: "No one wants AI. They want the magic that they think AI brings." AI isn't necessarily overhyped; it is often misapplied. The takeaway: successful adoption starts with the business problem and measurable success criteria, not with selecting the technology. Steve Leaden: Test AI to Fail, Not Just to Succeed In a healthcare deployment covering prescription refills and appointment confirmations, planned across 120 groups, pilot testing exposed long silences before transfers and repeated "do you want to be transferred?" prompts. Minor in a demo; damaging in a real patient interaction. The team recommended more testing rather than launching, including scenarios outside the scripted workflows, such as a patient describing a medication informally. The economics can still be compelling: one model compared roughly $1.2 million in annual staffing cost against about $200,000 in annual AI usage, though real results depend on resolution rates, implementation costs, and workforce decisions. The takeaway: try to break your AI before your customers do. ROI depends on disciplined implementation and testing against real-world scenarios. Five Lessons Heading Into 2027 Prepare for agent-to-agent interactions. Engagement models, security, authentication, and service policies will need to evolve. Measure business outcomes, not AI activity. Completed workflows and resolved problems matter more than interaction counts. Understand the economics before scaling. Consumption pricing introduces real budgeting risk. Start with clearly defined, achievable use cases. Executive enthusiasm cannot substitute for requirements, realistic timelines, and readiness. Test aggressively before deployment. A successful demo is not proof of production readiness, especially in high-stakes environments. Looking ahead, the panel expects context and orchestration to get much more attention as agents multiply across applications and departments. The winners won't be the organizations deploying the most agents; they will be the ones that understand where AI creates value, manage its costs, and implement it in ways that reliably deliver better outcomes. This post summarizes the original BCStrategies article. Read the full piece and join the discussion in the comments on LinkedIn.

For years, the meeting room industry has measured interoperability with one question: how easily can a room join a meeting hosted on another platform? If your organization runs Microsoft Teams Rooms (MTR), joining Teams meetings is easy. Ideally, users should also be able to walk in, tap a button, and join a Zoom, Webex, or Google Meet meeting. In my latest article, I argue we may be asking the wrong question. Rather than "How easily can I join the meeting?" we should be asking, "How easily can I accomplish everything I came to the meeting to do?" As meeting platforms evolve well beyond audio, video, and screen sharing, that distinction matters more and more. Direct Guest Join: Simple, But Limited Microsoft's Direct Guest Join (DGJ) delivers a genuinely simple experience: find the meeting on the room controller, tap Join, and you're connected. For organizations standardized on MTR, joining other platforms without changing equipment or requiring a laptop is genuinely useful. For attendees who mainly need to listen, speak, and view a presentation, DGJ may be all they need. The limitations surface when users need to do more, and they can become critical when you are hosting. Microsoft's current DGJ implementation does not support sharing content through the room's HDMI input, interactive whiteboarding, or controlling the meeting lobby. It also carries restrictions on video resolution, display configurations, and access to certain collaboration features. Enhanced SIP connectivity closes some of the gap, supporting 1080p video, two displays, and HDMI content sharing. However, it is a paid interoperability service that requires a SIP dialing plan plus additional Exchange and tenant configuration. Neither DGJ nor SIP interoperability offers the complete functionality of the meeting platform's native application. An Interesting Alternative From Barco This is why I believe organizations evaluating Teams Rooms should take a serious look at Barco's ClickShare Hub. Barco's approach doesn't require abandoning an investment in Microsoft Teams Rooms. The Hub Core and Hub Pro provide a native, certified MTR experience when deployed with supported peripherals, so Teams meetings work with the familiar room controller and management capabilities. When someone needs to run a Zoom, Webex, or Google Meet meeting, they plug a ClickShare Button into their laptop, connect wirelessly to the room's camera, microphones, speakers, and display, and use their normal conferencing application. The important nuance: Zoom or Webex isn't running natively on the MTR. The meeting runs on the user's laptop, using the room's professional AV equipment. Employees use the platform exactly the way they already know how. The Experience Beyond Joining Consider an employee hosting a customer meeting on Zoom. With DGJ, they can join from the Teams Room, but may still need their laptop for host controls, content sharing, chat, or other features DGJ doesn't expose. Many end up joining the same meeting twice: once from the room for audio and video, and once from the laptop to collaborate and manage the meeting. That works, provided laptop audio is handled correctly, but it adds complexity. With ClickShare, the host uses the familiar Zoom application, with whatever host controls and collaboration capabilities their account and meeting settings permit, while the room handles the AV. There is no juggling two separate conferencing endpoints. The benefit extends beyond the meeting itself. Using a personal, authenticated client keeps recordings, transcripts, summaries, and follow-up activities connected across scheduling, conducting, and following up. DGJ doesn't prevent access to those materials afterward, but the room's guest experience sits apart from the user's own meeting account, which adds friction. The key differentiator isn't that joining is easier. It's that employees use the platform the way they already do on their laptops, while benefiting from the room's professional AV. That consistency has value. Can Direct Guest Join Ever Keep Up? Meeting platforms are evolving rapidly as AI becomes embedded in the experience: meeting assistants, transcription, translation, summaries, intelligent content, and automated actions. Each platform has its own implementation, identity requirements, licensing, and approach to exposing those capabilities to other applications. For DGJ to keep pace, that functionality must be available through the interoperability mechanism and supported by the room system. Because DGJ relies on the host platform's WebRTC experience, some improvements can arrive through the meeting provider's own updates. Still, there are architectural limits; features that work beautifully in a native client may not reach an interoperable room experience. And even native room systems don't support every capability of their desktop counterparts. My view: DGJ will likely always be playing some degree of catch-up. Not because Microsoft isn't investing, but because interoperability and native application functionality are fundamentally different propositions. As conferencing platforms become intelligent collaboration environments, that difference may become increasingly significant. ClickShare Isn't Without Compromises None of this makes ClickShare automatically the better choice. DGJ is excellent for straightforward meetings, especially when users simply want to walk in and join without opening a laptop. ClickShare requires a compatible laptop, adds another device to the workflow, and depends on the wireless connection and room peripherals working correctly. In practice, though, most employees already run meetings from their laptops when they aren't in a meeting room, so it is a familiar workflow rather than a new one. For organizations that primarily use Teams and only occasionally join meetings hosted elsewhere, DGJ may be perfectly adequate. Other options are also worth evaluating, including enhanced SIP interoperability services, wired laptop connectivity, and room systems that support more than one native meeting platform. The point isn't that ClickShare is universally superior; it's that it represents a different, viable approach to the interoperability problem. It's Time to Rethink How We Measure Interoperability Today we tend to judge room interoperability by which platforms a room can join, whether one-touch join is supported, and whether audio, video, and content sharing work. Those measures are important but increasingly insufficient. Organizations should evaluate the complete meeting workflow: scheduling, joining, presenting, collaborating, hosting, and following up. Can employees accomplish what they need without workarounds? Can they use the platform's features? Can they run the meeting with tools they already know? A room that joins every platform but restricts what people can accomplish isn't necessarily better than one that requires an extra step to connect. Successful meeting room interoperability shouldn't be measured by whether users can quickly join a meeting. It should be measured by whether they can accomplish everything they came to the meeting to do. As AI continues to reshape meetings, I suspect that distinction will only grow in importance. Update: Microsoft Cross-Platform Meeting Connect On the day I planned to publish the original article, Microsoft announced Cross-Platform Meeting Connect, which in the future intends to address some of these interoperability issues. Key points for now: it is not yet available; Zoom currently says it will arrive before the second half of 2027; and Microsoft hopes other vendors will announce participation soon. I think it's a great initiative, and I'll provide updates as features become generally available. My analysis focuses on what is available today. And the question I asked about DGJ, whether it can ever keep up, applies equally to Cross-Platform Meeting Connect. This post summarizes my original LinkedIn article. Read the full piece and join the discussion in the comments on LinkedIn.

Zoho is making a significant move in the evolution of business intelligence (BI), repositioning Zoho Analytics as an agentic analytics platform that connects business data, context, AI-driven insights, and actions. The goal is to move beyond reporting and dashboards to help businesses understand what their data means and use those insights to make decisions and trigger workflows. The announcement reflects a broader shift in enterprise AI. As organizations begin deploying AI agents, access to data alone isn't enough. Agents also need to understand how a business operates, how its metrics are defined, and how different pieces of information relate to one another. Without that context, even a technically correct answer can lead to the wrong business conclusion. Zoho is addressing this through what it calls a trusted intelligence layer, built into the Analytics platform rather than requiring a separate data platform or cloud data warehouse. Business context is the foundation Two of the most important elements of the update are the new semantic and ontology layers. The semantic layer provides a business glossary for AI, standardizing terminology, synonyms, instructions, and metric definitions. This matters because organizations often use the same terms differently across departments. Revenue, for example, may have different definitions depending on the business context. Giving AI a consistent understanding of those definitions should help improve the relevance and repeatability of its answers. The ontology layer goes a step further by modeling business entities and their relationships. This gives AI a structured understanding of how the business fits together, rather than treating every data point as an isolated fact. These capabilities are particularly important as businesses move from asking AI to retrieve information toward asking it to analyze problems, recommend next steps, and take action. The quality of those decisions depends on the quality of the underlying data and business context. Zoho says these capabilities are native to Analytics, can be configured without code, and support multiple semantic models for different business contexts. The platform also adds more than 30 data connectors to its existing base of more than 500, including integrations with Zoho applications, SAP, Twilio, Intercom, PayPal, and other enterprise applications and databases. The broader opportunity is to reduce the disconnect between business intelligence and the systems where work actually happens. From asking questions to taking action Zoho is also upgrading Ask Zia, its conversational BI agent, with a multi-agent architecture. A central agent coordinates six specialized agents covering data analysis, data modeling, visualization, data preparation, coding, and actions. The idea is to let users move between tasks without losing the context of the conversation. A user might begin by investigating a business performance issue, prepare additional data for analysis, and then build a workflow based on the findings. Organizations can also build custom agents grounded in their own data, semantic models, and knowledge bases. These agents can connect to external tools through the Model Context Protocol (MCP), web search, and other integrations. Zoho is also introducing agentic actions that allow agents to trigger workflows directly. Another important aspect is that Zoho is extending its analytics capabilities beyond its own interface. Through MCP, a command-line interface, and a Claude plug-in, developers and external agents can access Analytics capabilities. Organizations can also embed agents in their own applications and portals while retaining permissions and governance controls. This openness is strategically important. Businesses are unlikely to standardize all their AI activity on a single vendor's assistant. Making business intelligence available across agents, applications, and workflows gives Zoho a potential role in a broader AI ecosystem, rather than limiting Analytics to customers who work exclusively within Zoho's applications. Governance matters as much as intelligence Zoho is pairing these agent capabilities with controls for managing their use. A unified data catalog provides visibility into data, metrics, models, and analytics assets, including lineage and impact analysis. AI observability lets administrators see which agents ran, who used them, what data they accessed, and how they responded. Usage and cost tracking, sandbox environments, version management, and rollback capabilities provide additional oversight. These aren't secondary features. As agents gain the ability to access business information and initiate workflows, organizations need to understand what those agents are doing and establish boundaries around their actions. Governance will be a significant factor in determining whether enterprises are comfortable moving beyond AI experimentation. Zoho also says pricing for Analytics remains unchanged despite the extensive updates, with Zoho LLM credits now included in each plan. The Basic plan starts at $24 per month, billed annually, for two users, 500,000 rows, and 10,000 Zoho LLM credits. The next step in Zoho's AI strategy The announcement includes more than 50 additional capabilities, spanning visualizations, pixel-perfect reporting, scenario analysis, forecasting, and data science tools. But the more significant development is the direction of the platform. Zoho plans to expand its business context capabilities, introduce decision intelligence based on the outcomes of agent-triggered actions, and build toward a broader unified data platform encompassing data integration, preparation, analytics, data science, and agent building. The challenge will be execution. Semantic models and business ontologies require organizations to invest time in defining their data and processes. AI agents also need to demonstrate that they can deliver reliable outcomes, not just compelling demonstrations. And while unchanged pricing is attractive, businesses will still need to evaluate the cost of AI usage, integration, and ongoing management. Zoho's approach is notable because it treats business context and governance as foundational components of agentic AI, rather than adding agents on top of existing analytics tools. If the company can make that intelligence accessible across applications and workflows while maintaining accuracy and control, Zoho Analytics could become more than a BI platform. It could serve as a common intelligence layer for how businesses use AI to make decisions and get work done.

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