Almost every knowledge worker has to create PowerPoint presentations, which made this one of our most practical episodes yet. For Episode 5 of Calibrating Copilot, Dino Caputo and I asked several versions of Copilot to create a polished, customer-facing deck from an existing website. The test We used our own EnableUC website as the source and kept the prompt deliberately short: using enableuc.com, create a customer-facing PowerPoint presentation of no more than 12 slides that explains all of our services, uses the branding, visual style, colors, imagery, and logo from the website, and includes a talk track in the slide notes for a 15-minute presentation. Out of curiosity, I asked ChatGPT to improve the prompt. It produced a 464-word monster. In my one test with it, the longer prompt did not make a meaningful difference. We tested Copilot Chat, Microsoft 365 Copilot, Copilot inside PowerPoint (using both Auto and the newly available Grok, or SpaceX, models), and Cowork at different effort levels and models. Note that the Grok models require tenant admin consent and, for now, are only available inside the Word, PowerPoint, and Excel apps, not in Copilot Chat or Cowork. Can you tell which deck cost the most? When we lined up title slides side by side, it was surprisingly difficult to tell which came from the free Copilot Chat and which consumed AI credits. Copilot Chat produced a genuinely professional-looking deck in about four minutes, at no cost. Microsoft 365 Copilot was the biggest disappointment. Its deck had 11-point fonts, excessive white space, and generic stock-style images. We both agreed it was the weakest of the group, and we would not present it to a customer. Same model, same prompt, very different results Dino and I both ran Copilot in PowerPoint with the Grok model, and the results were dramatically different. Dino's deck alternated layouts, included speaker notes automatically, and ended with a strong call to action. Mine was darker, more uniform, needed a second prompt to add speaker notes (about 20 minutes in total), and closed with a lame 'Let's talk.' Since Copilot in PowerPoint does not consume AI credits, running it two or three times and picking the best result is a reasonable strategy. Cowork showed the same variability. Cowork Medium on Auto used 345 credits and produced a deck with great visual variety. Cowork Medium with a specific model explicitly selected used more than three times the credits and produced something bland and repetitive. Cowork Max, using an Opus model, took 53 minutes and about $17 in credits, and produced the most sophisticated layouts, including a Venn diagram with differentiated text. Branding, logos, and images Only Microsoft 365 Copilot, Cowork, and Cowork Max used our official logo. The others invented one, capitalizing the lowercase e in enableUC or adding a space. Cowork was the only option that used real imagery from the website, including a photo of me. It reported that it could not find a photo of Dino, even though one is on our About page. The Grok deck had one instance of overlapping text. Look closely at AI-generated images. One showed a presenter who was three times the size of his audience; another had a meeting participant staring at a blank laptop screen. None of the decks used a Slide Master. That means changing a logo or background requires editing every slide, and some bullets were built as separate text boxes rather than bulleted lists. AI-generated decks get you started quickly, but they can be harder to edit than a deck built by someone who knows PowerPoint well. Speaker notes All of the speaker notes were generally clear and concise. The Grok notes were overly salesy, promising 30% savings and implying we are available 24/7 (Dino does like to sleep). Cowork's notes were cluttered with source references that would need to be cleaned up. Still, any of these talk tracks is better than winging it. The scores Note that accuracy here does not mean hallucination; every option accurately reflected the website. It reflects how much rework you would need. Copilot Chat scored two for accuracy and three for completeness, impressive for a free tool. Microsoft 365 Copilot scored one. Copilot in PowerPoint with Grok scored three. Cowork Max (Opus) was our winner, with Cowork Medium on Auto a very close second. Even at 53 minutes and $17, Cowork Max produced in under an hour what would take a skilled person a day or two, including the talk track. A few more lessons If the free Copilot Chat is all you have, absolutely use it. It is a great way to get started. If you have a Microsoft 365 Copilot license, start in Copilot in PowerPoint rather than the M365 Copilot app. Run the same prompt more than once; results vary enormously. Even if you build the deck yourself, ask AI to write the talk track and tell it how much time you have. Copilot should give you a receipt after every task: which models it used, how long it took, and how many AI credits it consumed. Today, /cost shows credits, but Cowork will not tell you which model it used. AI reads your website differently than people do. Copilot spotted that one page called Dino an 11-time MVP while another said 13, and it found orphaned pages describing older services. We have since fixed both. Give each person the Copilot that fits This episode found that the free option was surprisingly capable, the paid Microsoft 365 Copilot app was the weakest for this task, and the most expensive option was not automatically the best. Knowing which Copilot fits which work is exactly how organizations avoid paying for capability that goes unused. That is exactly what our Copilot Calibration service is built to solve. Using your existing Microsoft 365 usage data, we deliver person-by-person recommendations: who should get a Microsoft 365 Copilot license, which licenses should be reassigned, where Cowork makes sense, and where targeted training would pay off. It is fixed-price and requires minimal effort from your team.

For Episode 4 of Calibrating Copilot, Dino Caputo and I put vibe coding to the test. Can Microsoft Copilot build a working business application from a complex set of requirements? The challenge We found a 191-page manual for cyber liability insurance for health professionals. Most of it was application material and underwriter information that should be ignored, but about seven core pages contained rating tables and fairly sophisticated calculations, including exponents and Weibull curves, which I had to look up on Wikipedia and still do not fully understand. Actuaries apparently use them. We asked Copilot to use the manual as the calculation specification and build a fully functional, interactive premium quotation app. It needed to implement all applicable rates, tables, formulas, limits, interpolation rules, credits, debits, and caps; let the user change every input required for a valid quote; and show both the final premium and an itemized calculation. Critically, we told it not to invent calculation rules that were not in the specification and to identify anything that was genuinely ambiguous. When you are quoting insurance, you do not want an LLM getting creative. We tested Copilot Chat (Basic), Microsoft 365 Copilot, the Researcher agent, Cowork at Medium and Max, and Cowork with the GPT-5.6 Sol model selected. As a bonus, we compared the results with Claude. Copilot Chat: do not use it for vibe coding Copilot Chat could not process the full PDF. Even after I gave it just the seven relevant pages, four out of five attempts produced an app whose Calculate button did nothing, because output limits prevented it from delivering the complete source code. It then wasted 20 minutes offering workarounds it could not actually deliver: a ZIP file, then a base64-encoded file with three options to choose from, none of which worked. The one time it produced a complete app, it was ugly and its results were off by a few dollars because of a factor that should have been 1 but was 1.01. Dino only got a placeholder. One star. Microsoft 365 Copilot: the winner Microsoft 365 Copilot built a good-looking, working app in just under four minutes. It created a single HTML file stored in OneDrive, which made it trivial to share with others. Its results were accurate and consistent with the other tools, and it included sample scenarios for validation. My version added a print button we did not ask for, and both versions listed the specification ambiguities they found in the manual, exactly as instructed. Our two runs also produced visibly different apps from the same prompt and input, a good reminder that AI is probabilistic. Researcher: a great story, but no app Researcher took a while and then told a wonderful story about the self-contained, browser-based quoting tool it had built. The download links were not clickable and pointed to an inaccessible sandbox. When I asked it to repackage the app, it took another 10 to 15 minutes and then served up a file I had previously created with Cowork Max, presenting it as its own work. Zero stars. To be fair, Researcher is not designed for this. Cowork: impressive, but it builds more than you asked for Cowork Max built a full React application, roughly 50 TSX files, and published it as a Power App. It was accurate and polished, took 16 minutes and 35 seconds, and used 544 AI credits, about $5. However, every Cowork run defaulted to creating a Power App, which means you use credits to build it, and anyone who runs the published app may consume usage credits too. We hit messages saying we did not have enough credits to use the app and that access to managed apps was unavailable, even though we have pay-as-you-go billing enabled. Cowork with GPT-5.6 Sol chose a different color scheme and was the most complete of the Copilot options. It added a section for mid-term policy changes and cancellations, and when certain custom inputs created an ambiguity, it declined to calculate a premium and explained exactly why. But converting its Power App into a standalone HTML app brought the total to 40 minutes, and the second step used more credits than the first. Bonus: Claude Claude Opus 5 High produced arguably the most complete app. For every ambiguity in the manual, it offered a dropdown so you could choose how to interpret the rule, and its ambiguity register highlighted exactly which ambiguities affected the quote on screen. Since Anthropic models are available within Copilot, that is something we want to test in a future episode. We also tried Copilot's experimental Advanced Reasoning option. It ran for 90 minutes, repeatedly assured me it was making progress, and produced nothing. Key lessons Copilot Chat is not suited to vibe coding; even when it produces an app, the results may be wrong. Microsoft 365 Copilot is very capable of creating standalone HTML apps, and you can iterate as many times as you like without generating an AI credit bill. As Dino put it, Cowork optimizes for completeness while M365 Copilot optimizes for sufficiency. Agentic AI tends to expand the scope unless you constrain it. Tell AI what not to build. If you want a standalone HTML app from Cowork, say so explicitly, or it will build a Power App. Validation still matters, no matter how convincing the app looks. Our recommendation: for vibe coding a business tool like this, use Microsoft 365 Copilot. It was fast, accurate, and did not cost anything beyond the license. Sufficiency is a licensing strategy too Dino's distinction between completeness and sufficiency applies well beyond vibe coding. Most organizations do not need the most powerful Copilot option for every person; they need the right option for each person's actual work, with Cowork reserved for the tasks that genuinely justify it. That is exactly what our Copilot Calibration service is built to solve. Using your existing Microsoft 365 usage data, we deliver person-by-person recommendations: who should get a Microsoft 365 Copilot license, which licenses should be reassigned, where Cowork makes sense, and where targeted training would pay off. It is fixed-price and requires minimal effort from your team.

Microsoft recently added an effort slider to Cowork with five settings: Light, Medium (the default), High, Extra High, and Max. Choose Max and the interface lights up in rainbow colors, a not-so-subtle hint that you are about to spend a lot of AI credits. For Episode 3 of Calibrating Copilot, Dino Caputo and I put every setting through its paces, with the Researcher and Analyst agents as bonus contenders. The test: an executive wildfire dashboard We gathered 24 Excel files containing Canadian wildfire and climate data covering 2025 and 2026 to date, stored them in SharePoint, and asked Cowork to analyze the relationship between wildfire frequency, area burned, province, temperature trends, and precipitation patterns. Specifically, we asked it to identify: The provinces with the largest increases Outlier years Evidence of climate-related trends Potential future risk areas Whether 2026 is far worse for fires, and why or why not The deliverable was an executive dashboard in Excel that highlighted the most important findings and conclusions. The results Cowork High came out on top. It delivered strong accuracy and completeness; at one point it made a slightly stronger statement than the data warranted, but it was not wrong. Cowork Max also produced excellent results, but it took about twice as long and cost about twice as much. Extra High got the answer completely wrong The most surprising result came from Cowork Extra High, which cost about $13 and landed at the bottom of our scoreboard with two stars for accuracy. The 2025 and 2026 files contained some overlapping months. Every other setting, including Light, recognized the duplication. Extra High did not. As a result, it concluded that wildfires in British Columbia were down 36% when they were actually up more than 1,000%. The worst part? The spreadsheet looked fantastic. Had we only run Extra High, we would have confidently told the fire chief that BC was fine. How we verified the results These spreadsheets were sophisticated enough, with methodology tabs and correlation statistics, that simply eyeballing them was not enough. So we fed all the outputs into other AI tools, including Claude, Gemini, Cowork, and ZoomMate, and asked them to identify where the results differed. ZoomMate delivered my favorite line of the episode: in executive reporting, accuracy must come before ambition. Extra High had plenty of ambition. Researcher and the dashboards The Researcher agent, included with the Microsoft 365 Copilot license with no AI credits required, produced the most visual result, with color and real charts (the only one that included proper graphs, although some labels were off). It clearly answered whether 2026 was worse. However, for no apparent reason, it left the provincial scorecard tab blank, even though its totals were correct. Looking across the dashboards, each setting was good at something. Max had a well-written verdict but a change column that was not formatted as a percentage and a bit of an eye chart overall. High was clean and simple, with directional coloring that Dino liked; sometimes less is more. Light was functional but had minimal formatting and cells that needed resizing. Why more effort is not always better Dino's theory is that this data set, while large, was not especially complex. More intertwined data, such as financial tick data or health data, might justify higher effort settings, and we plan to test that in a future episode. Importantly, the effort settings do not automatically scale down: choose Max and Cowork will spend Max-level effort even when Light would suffice. Run important tasks more than once We only caught the Extra High error because we ran six different tests and asked AI to compare them. If you are using AI for serious analysis, running the same task through multiple settings or models is one of the most effective ways to surface errors that would otherwise be nearly impossible to spot. As a final footnote, we also tried the Analyst agent. It enthusiastically wrote Python code, took a while, and produced nothing of value. We are still looking for the right use case for Analyst. Key takeaways Do not automatically reach for Max. More compute, more time, and more AI credits do not guarantee a better result. A polished result is not necessarily an accurate result. For important analysis, run the task more than once and compare. Spending more does not guarantee more value This episode showed that a higher setting and a bigger AI credit bill do not automatically produce a better outcome. The same is true for Copilot licensing across an organization: assigning the most expensive option to everyone does not guarantee results, while under-licensing the right people leaves value on the table. That is exactly what our Copilot Calibration service is built to solve. Using your existing Microsoft 365 usage data, we deliver person-by-person recommendations: who should get a Microsoft 365 Copilot license, which licenses should be reassigned, where Cowork makes sense, and where targeted training would pay off. It is fixed-price and requires minimal effort from your team.

In the first episode of Calibrating Copilot, Dino Caputo and I tested a relatively simple task: summarizing a single project document. For Episode 2, we raised the difficulty considerably. Dino found a real-world request for proposal (RFP) for a Cisco-to-Microsoft Teams Phone migration. It consisted of nine separate documents: Word files, Excel spreadsheets, location data, telephone information, and question-and-answer workbooks. The information was spread all over the place, the kind of package that would take a person at least a couple of days to fully digest. The test Our prompt asked Copilot to act as an experienced bid capture analyst, analyze every document (we placed them all in a SharePoint folder), summarize the opportunity in no more than 4,000 words, and, most importantly, end with a clear recommendation: bid, bid with conditions, or no bid. This is a commercial decision where hundreds of thousands of dollars could be on the line. As always, we ran the same prompt through Copilot Chat, Microsoft 365 Copilot, the Researcher agent, and Cowork, scored the results independently, and then averaged our ratings for accuracy, completeness, speed, and cost. Cowork was in a league of its own The results were not close. Cowork was the only Copilot that identified the significant risks you would absolutely need clarified before responding. Among them: A requirement to place source code in escrow, which makes no sense for a cloud-based Teams Phone deployment (Microsoft is not going to hand over its code) Unbounded liability Insurance requirements that, as Cowork noted, no national carrier (and the bidder also had to be an Operator Connect provider) would ever agree to Confusion over whether the published Q&A answers were even contractually binding, since the contract said only the main document mattered Cowork told us the bid should only proceed if specific items were confirmed before the August 31 deadline, and it supplied the exact clarification questions to ask. It even flagged the highest commercial risks right in its chat response, before we opened the Word document. It was the slowest option at 11 minutes and 11 seconds, and it consumed 588 AI credits, just under $6. We took half a star off for accuracy because it confidently reported that its assessment was about 3,000 words, well under the 4,000-word limit, when it was actually 16 pages and roughly 5,500 words. In both of our runs it went over. In this case, it did not really matter. What the other Copilots missed Interestingly, every Copilot recommended bidding with conditions, but only Cowork clearly explained what those conditions were. Researcher and Microsoft 365 Copilot partially identified the risks; in Dino's run, M365 Copilot edged out Researcher. Copilot Chat did a good job summarizing the project, but it gave you almost no help with the actual commercial decision. Quirks worth knowing about Copilot Chat cannot access SharePoint links, and its error message (the folder requires authentication or is not accessible) is confusing. I spent ten minutes convinced it was a sign-in issue. Copilot Chat then recommended zipping the files and uploading them. The problem: neither Copilot Chat nor Microsoft 365 Copilot can process ZIP files. Cowork can. The workaround is to upload the files in batches of three. Tell Copilot up front that you will upload all nine files in batches and that it should wait for all of them before processing. It followed that instruction perfectly. Dino's M365 Copilot run got stuck on 'working on creating the Word document.' After about two minutes, quitting and reopening the app revealed that the document was there. If it is taking an unusually long time, it probably is not doing anything. The verdict For complex RFP analysis, Cowork is absolutely worth the AI credits. Spending six dollars to inform a bid worth hundreds of thousands of dollars is an easy decision, and a person doing this work would take days and might still miss the subtleties spread across nine documents. That is a very different conclusion from Episode 1, where Cowork's extra cost was harder to justify. Dino offered a smart idea for making the analysis even safer: run it more than once, and consider a separate prompt focused specifically on legal risks. That helps smooth out the run-to-run differences we saw between our results. Matching the right Copilot to the right work The lesson from this episode is that value depends entirely on the task. For a quick summary, the free or included Copilot may be all you need. For high-stakes analysis across many documents, a more capable agent is a completely different value proposition. The same logic applies to the people in your organization: your bid managers and analysts may justify Cowork, while others are well served by Copilot Chat. That is exactly what our Copilot Calibration service is built to solve. Using your existing Microsoft 365 usage data, we deliver person-by-person recommendations: who should get a Microsoft 365 Copilot license, which licenses should be reassigned, where Cowork makes sense, and where targeted training would pay off. It is fixed-price and requires minimal effort from your team.

Microsoft now offers several flavors of Copilot, and knowing which one to use for a given task is far less obvious than it should be. That is the gap our new series, Calibrating Copilot, sets out to close. In each episode, Dino Caputo (13-time Microsoft MVP for Copilot and Teams) and I take one real business use case, run the identical prompt through each Copilot option, and rate the results on accuracy, completeness, speed, and cost. Our first test: turning a long, detailed project document into an executive summary. The test The source was a detailed project description of roughly 65 pages. We asked each tool to summarize it into an executive brief of no more than 1,800 words covering five items: Key project objectives, emphasizing specific quantitative goals Important assumptions associated with the project Critical project milestone dates Risks associated with the project Unanswered or unknown issues The prompt also told Copilot to reference only this document, to base the summary on verifiable facts it contains, and to write in a clear, concise style suitable for an executive briefing. Dino and I each ran the same prompt against the same document in all four tools, then averaged our scores. Where a model choice was offered, we left it on Auto. Copilot Chat (Basic): free, fast, and a bit inconsistent The free Copilot Chat finished in about 26 seconds. Its biggest annoyance was a citation on every single bullet, all pointing back to the same PDF; not exactly clear and concise. In my run it also missed two important numbers: the revised project budget of $2.5 billion and the estimated $1.5 trillion lifetime benefit. Dino's run caught both, which is a useful reminder that the same AI can produce different results on different runs. Accuracy averaged four stars (I gave it three, Dino gave it five). On cost, you largely get what you pay for, but it is hard to beat zero. M365 Copilot (Premium): quick and well organized The paid M365 Copilot ($30 per user per month) averaged about 33 seconds and shared the same repetitive citation habit. Funnily enough, our results flipped: my run captured both key figures while Dino's missed the $1.5 trillion benefit. Overall it scored higher than Copilot Chat on accuracy and completeness, and Dino felt it made the best use of H1 and H2 headings, producing the easiest document to navigate. Because the license is effectively all-you-can-eat, there is no incremental cost per summary. Researcher agent: the deepest analysis Researcher comes included with an M365 Copilot license. It produced the most complete output: fuller paragraphs, every key number, and a good narrative explaining how the budget grew over time. It was also the only tool that placed the executive summary at the top of the document, where an executive would expect it. The downsides: the same repeated citations, heavy bolding that loses meaning after a while, and headings that were less navigable than M365 Copilot's. Researcher also asks clarifying questions before it starts (how long, what to include, or "use my best judgment"), so do not type the prompt and walk away; it will wait for your answer. It took about 2 minutes and 45 seconds, and converting the result to a Word document took just five seconds. Scores: accuracy 4.5 stars, completeness 5 stars, and 4 stars on cost. Cowork: the best-looking document, at a price Cowork produced the cleanest Word document of the group: no citation clutter and some well-structured tables. It did, however, run about 250 words over the 1,800-word limit by Word's count (Dino noted that if you strip out citations and hyphenated words it comes close to compliance, so this one is debatable). Speed was the surprise. The on-screen summary took roughly a minute and a half, but when Cowork offered to create a Word document, that step took 6 minutes and 13 seconds in my run. Dino compared the two and found Cowork was rebuilding the document from scratch rather than copying the text across, which explains both the better formatting and the time. Cowork also consumes AI credits. At the list price of one cent per credit, the initial summary used 108 credits (about $1.08), and generating the Word document added another $3.82. Call it four to five dollars per project summary. Scores: accuracy 3.5 stars, completeness 4 stars, and 3 stars on cost. The verdict A clear pattern emerged: the two chat-based tools stayed disciplined on length but sacrificed some depth, while the two agentic tools (Researcher and Cowork) produced richer documents at the expense of the word limit. For summarizing a detailed project document, our recommendation is Researcher. It delivered the best combination of accuracy, completeness, and value. Dino felt it was close between Researcher and Cowork, and Cowork's polish may be worth it in some situations. A few dollars per summary might not sound like much, but cost is relative: multiply it across your document volume and frequency and it adds up quickly. One goal of this show is to build practical benchmarks for what common Copilot use cases actually cost, because today there are very few. Have a use case you would like us to test? Leave it in the YouTube comments and we will run it through the calibration lab. Is the right Copilot in the right hands? This episode shows that the "best" Copilot depends on the task. The same is true of the people in your organization. Paying $30 per user per month for someone well served by Copilot Chat wastes budget; leaving a heavy user on the free tier, or running Cowork without watching credit consumption, leaves productivity and money on the table. That is exactly what our Copilot Calibration service is built to solve. Using your existing Microsoft 365 usage data, we deliver person-by-person recommendations: who should get an M365 Copilot license, which licenses should be reassigned, where Cowork makes sense, and where targeted training would pay off. It is fixed-price and requires minimal effort from your team.

At the 8x8 Analyst Summit in Silicon Valley, CEO Sam Wilson made it clear that the company is playing offense. With four consecutive quarters of growth and 21 quarters of profitability, 8x8 is building on a stable foundation as it uses its network, platform, and past acquisitions to compete in an AI-driven market. Wilson was direct in describing how he views the market’s AI messaging and why 8x8 believes its approach is more practical. As he put it, "Companies that say they’re AI companies are stupid idiots – it’s like saying you’re an internet company 20 years ago. AI will be in everything.” It's not about being an AI company - it's all about using AI to solve our customers' problems. Our customers care about their business, not if AI does the routing or not." Key Themes From the Event Several themes stood out at the summit: · 8x8 positions its “One Platform” as a fully integrated environment spanning telephony, digital channels, IVA, video, and CX. · Its AI strategy centers on partnering for models rather than building them internally. As Wilson explained, “We own the network and the data - the platform is the product.” · The company placed strong emphasis on frontline workers, or “the 70% the market forgot.” Wilson argued that every worker should be treated as a first-class worker and that businesses need to stop overlooking digital orphans - employees without laptops, email, or other standard tools. · 8x8 is leaning in on usage-based AI pricing, which lowers the barrier to entry and gives organizations room to experiment and prove AI's value without a large upfront commitment. · The partner ecosystem is central to 8x8’s strategy. 8x8 provides “the network and the pipes,” while partners add complementary technologies and services. As Victor Belfor, GVP Business Dev & Strategic Partnerships, noted, “The ecosystem is part of our product. We’re the one back to pat, providing a single contract, low TCO, and no finger pointing. We own this from beginning to end – delivery, support, integration, etc." New Products Expand the Platform Story 8x8 also used the event to introduce several products, including 8x8 Resolve and 8x8 Pulse. 8x8 Resolve is a mobile-first critical communications and incident management solution designed to support businesses with distributed workforces. It helps organizations notify, track, and coordinate warehouse staff, retail associates, field technicians, and healthcare aides—workers who make up an estimated 70% of the global workforce and are often the last to know when something goes wrong. 8x8 Pulse is a conversational intelligence solution that turns conversations into business insights. It uses generative AI to analyze interactions and add context so the information can be used more reliably at the enterprise level. Pulse draws on conversation data to deliver interaction-based insights and recommendations for workforce management, customer engagement, sales, and operational improvement. It can analyze multiple communication channels, including calls, emails, voicemails, support tickets, external notes, CRM data, and more. According to Dhwani Soni, Global VP of Product Management, every conversation an organization has -customer calls, support interactions, emails, meeting transcripts, and CRM notes - creates data that can be mined for insight. 8x8 Pulse brings those sources together so organizations can generate insights from both structured and unstructured data. The company emphasized that while many organizations are generating large volumes of conversational data, much of it remains fragmented across systems. By bringing those sources into a single platform view, 8x8 aims to help businesses surface insights and improve decision-making across departments. Built on the conversations organizations already generate, 8x8 Pulse is designed to surface relevant intelligence for the right person at the right time, from executives to frontline employees. I spoke with Igor Mostovoy, Director, CPaaS Product, and Dhwani Soni, Global VP of Product Management, about Pulse and Resolve, as well as Engage. One Platform Hunter Middleton, Chief Product Officer, presented 8x8’s vision and roadmap, and how 8x8 will differentiate in the market. While much of his presentation was NDA, Middleton summarized many of the themes discussed throughout the event in this video interview. He covered 8x8’s transformation, the “One Platform” approach, and 8x8's thinking around agentic AI and 8x8 AI Studio. As Middleton explained, the “One Platform” approach goes beyond integrating UCaaS and CCaaS applications. It extends below the application layer to the communications fabric, creating a single communications system for the entire company. He also discussed 8x8’s approach to frontline workers and how non-contact-center staff, including employees without desk phones or laptops, can help customers resolve issues. He further explained how AI capabilities such as Agent Assist can help these workers support customers more effectively. Middleton also discussed AI Studio and 8x8’s approach to agentic AI. As he explained, 8x8 has taken a different route from many vendors by using the latest real-time APIs from LLM providers to enable more natural interactions, simplify workflow design, and shorten deployment timelines. This is an approach that was not practical even a year ago ,and gives 8x8 more flexibility to move quickly. 8x8 Technology Partner Synthflow As mentioned, ecosystem partners are a key part of 8x8’s strategy. Conversation AI vendor Synthflow is a new partner, providing an end-to-end Voice AI platform that enables businesses to design, launch, and operate Voice AI agents with configurable workflows, enterprise telephony, and analytics. I spoke with Synthflow’s VP of Channels and Alliances, Matt Alexander, about the company, its partnership with 8x8, and how Synthflow stands out in a crowded market for voice AI agents and conversational AI platforms. Final Thoughts The summit highlighted a company focused on execution, platform integration, and practical uses for AI. Rather than treating AI as a standalone strategy, 8x8 emphasized how it can support business outcomes, improve worker experiences, and simplify customer engagement. I’ve been saying for years that the walls are coming down between UCaaS, CCaaS, CPaaS, and CRM. 8x8 is focused on a single platform built around the capabilities customers need rather than the acronyms. By tightly integrating its technology with ecosystem partners, 8x8 is differentiating not just through individual applications, but through the platform itself. While 8x8 didn’t use the term “agentic” as often as some competitors, it is clearly laying the groundwork for agentic AI through AI Studio and partner-driven capabilities. 8x8 has evolved from its early days as a UCaaS vendor into a communications platform provider delivering AI-ready voice at global scale. The company sees an opportunity to support the broader workforce with integrated communications, CX, and AI capabilities. I agree with 8x8’s view that as AI handles routine tasks, every worker becomes more of a knowledge worker. The company’s focus on knowledge workers who “make up the 70% of the workforce that the market forgot” points to a sizable market opportunity. With 8x8 Engage and a soon-to-be-announced product, 8x8 appears well positioned to pursue that market. While some vendors are investing heavily in in-house AI, 8x8 is leaning on partners and newer capabilities from companies such as Anthropic and OpenAI. That approach may help the company move faster and stay flexible. Time will tell how well it works.

Set aside any preconceived notions about Zendesk. While the company built its reputation around service and ticketing, its focus today is on the Autonomous Service Workforce, AI agents, and resolutions. At Zendesk Relate 2026, the company’s annual event that brought together more than 2,000 attendees, Zendesk outlined its vision for the Autonomous Service Workforce, built on the Zendesk Resolution Platform. Service and ticketing remain core parts of the business, but the company’s AI portfolio - including AI agents, AI Copilot, and AI QA -grew more than 100% over the past year. Zendesk’s broader mission is to resolve customer issues through a combination of AI agents and human agents, powered by the Zendesk Resolution Platform, a connected system of agents and copilots designed to support service operations. As CEO Tom Eggemeier noted during his keynote, Zendesk introduced the Resolution Platform last year with the idea that customer resolutions require specialization. Trained on roughly 20 billion ticket interactions, the platform unifies data, intelligence, and workflows. The Resolution Learning Loop creates a continuous cycle of improvement, allowing the system to learn from every interaction and become more effective over time. The shift underway goes beyond automation. It reflects service organizations moving from assisted workflows to autonomous resolution, from disconnected tools to a unified learning system, and from reporting on past activity to continuously improving future outcomes. Autonomous Service Workforce The central theme of the event was the Autonomous Service Workforce, a collection of specialized AI agents designed to perform specific functions. The strategy centers on: AI agents that execute tasks AI copilots that improve employee performance and support human agents by identifying customer intent, recommending responses, and suggesting next-best actions A learning loop that continuously makes the system smarter According to Shashi Upadhyay, President, Product, Engineering, and AI, Zendesk is building a workforce that not only assists employees, but also completes work and changes how service is delivered. He explained that “The future isn’t one general purpose bot – it’s a network of specialized agents – agents for billing, returns, collections, claims, etc.” Each agent is grounded in the appropriate data and designed to handle real-world complexity, while the system coordinates work and drives outcomes. These AI agents are aligned with customer workflows, integrated into customer environments, and available out of the box. A central component of this strategy is the Resolution Learning Loop, where every interaction becomes a learning signal that improves AI agent performance. The process focuses on identifying where AI failed, determining the root cause, learning from those failures, and feeding that information back into knowledge management systems to improve future interactions. More resolutions generate more learning data, which leads to additional recommendations and improvements, ultimately driving better outcomes over time. Outcome-Based Pricing This shift also requires a new business and pricing model. Zendesk argues that traditional seat-based software pricing is becoming less relevant in an AI-driven environment. Instead, the company is positioning itself as an early mover in outcome-based pricing, where customers pay only for outcomes produced by AI agents that are verifiably resolved or contained. According to Zendesk, “If we don’t resolve the problem, we don’t get paid; customers will only pay for outcomes that are verifiably resolved or contained." New Products and Anouncements Zendesk announced a large number of new products and capabilities at Relate, including: Agentic AI agents — specialized AI agents that can reason across messaging, email, voice, and backend systems. Built on Zendesk’s acquisition of Forethought, these agents operate across messaging, email, voice, and AI platforms, as well as both Zendesk and third-party service environments. Agent Builder, a no-code interface that allows teams to create and refine custom agents using natural language Voice AI Agents for both Zendesk Voice and Zendesk Contact Center Proactive copilots for agents, administrators, knowledge teams, analysts, and service leaders Action Flows, MCP, and expanded Knowledge Connectors that allow AI agents to take action across existing enterprise systems Zendesk Contact Center, a native unified contact center offering A modern ITSM solution built on the same Resolution Platform Zendesk Contact Center For analysts covering the contact center market, one of the key focuses at the event was Zendesk Contact Center. Built on the acquisition of Local Measure, Zendesk Contact Center is a native, AI-powered contact center built on the Zendesk Resolution Platform. The platform unifies voice, digital, and self-service interactions into a single workspace. With multimodal capabilities, digital and video channels become part of the customer interaction experience. AI-powered workforce engagement management (WEM) is also built into the platform, supporting forecasting, scheduling, and interaction monitoring. Built on Amazon Connect and leveraging AWS and Amazon Connect AI capabilities, the solution provides agentic AI capabilities through Voice AI agents embedded directly into the Zendesk Resolution Platform. This enabling seamless transfers to live agents with full context when escalation is required. Zendesk Contact Center supports: Voice routing Omnichannel interactions Voice AI Agents that understand customers in real time and take action toward resolution Voice Copilots that provide AI-driven call guidance and next-best actions AI-powered voice assistance Video calling and screen sharing Real-time transcription Workforce support Voice QA across 100% of voice transcripts An integrated agent workspace While Zendesk Contact Center is currently targeted primarily at SMBs, the company sees opportunities to expand further into larger enterprise environments over time. Although the offering will likely appeal most to existing Zendesk customers, Zendesk Contact Center will also be available as a standalone offering, broadening its potential market. As CRM and CCaaS platforms continue to converge, Zendesk (along with companies such as Salesforce) is betting that customer service organizations will increasingly prefer a single vendor that can deliver an integrated solution with fewer vendors, tighter workflows, and broader functionality. Differentiation As shown in this video, I had the opportunity to speak with Keith Pearce, SVP, Product Marketing, about the Autonomous Service Workforce, the Agent Builder for creating custom AI agents, Zendesk Contact Center, and other announcements. He also discussed Zendesk’s differentiation strategy and its approach to outcome-based pricing. Thoughts Zendesk has evolved significantly from its origins as a ticketing vendor. The company now has a broader vision centered on an AI-first operating model and the changing relationship between humans and AI in customer service. The company’s focus on resolution rather than tickets aligns well with broader market trends, and the outcome-based pricing model reinforces that positioning. A recurring theme throughout the event was the need for businesses to rethink workflows and operational processes. While organizations want to move quickly with AI adoption, trust, security, and governance remain major considerations. The challenge is not only implementing new technology, but also redesigning workflows, modernizing knowledge management, and helping teams mature operationally so AI can deliver value at scale. As Zendesk expands its vision, it will also face several challenges. Acquisition alignment and integration: Zendesk has acquired multiple companies in recent years, including Forethought, Local Measure, Unleash, HyperArc, and Ultimate. These acquisitions have accelerated product development and expanded capabilities, but integrating technologies, infrastructure, analytics, and product roadmaps often takes longer than expected. Overlap between acquired and internally developed products will also need to be rationalized over time. For example, Zendesk currently offers internally developed Voice AI capabilities alongside capabilities gained through the Forethought acquisition. Zendesk must unify these experiences without creating inconsistencies across products and channels. If the platform appears fragmented, it could weaken the company’s broader “single system” message. Company perception: Zendesk is still widely viewed as a customer service and support company. The company will need to continue educating the market about its expanded AI capabilities and broader platform strategy. Many customers attending Relate still primarily use Zendesk for help desk and ticketing functions. While interest in the company’s AI tools was high, many organizations have not yet deployed them in production. Adoption will take time as customers become more comfortable with operational AI systems. Competition: As Zendesk expands into adjacent markets, it is also expanding its competitive landscape. Although Zendesk continues to partner with many CCaaS vendors, Zendesk Contact Center also places the company in direct competition with them. Established vendors such as Five9 and NICE already have strong enterprise credibility and mature voice infrastructures. Zendesk will need to differentiate through platform integration, workflow simplicity, and operational efficiency rather than feature parity alone. Trust: Many organizations remain cautious about deploying AI into production environments. Zendesk must demonstrate measurable ROI, strong governance controls, reliability, and consistent performance at scale. Pricing: The move toward outcome- or resolution-based pricing also introduces complexity. Defining what qualifies as a “resolution” can be difficult, and some organizations may view pricing variability as a risk. Zendesk will need to clearly demonstrate value so customers see the model as an improvement over traditional licensing structures. Despite these challenges, Zendesk is well-positioned to continue pushing the market toward resolution-centric service models. By focusing on outcomes rather than tickets, the company is attempting to redefine how customer service platforms create value. It's about impact, outcomes, resolutions - this is a message most businesses can relate to, helping them deliver better customer experiences.

Zoom held its Zoom Perspectives analyst conference at the Ritz-Carlton Half Moon Bay, giving analysts the opportunity to hear about the company’s latest AI developments. The central theme of the event was “conversation to completion.” It's not about meetings any longer - it's about how conversations kick things off, but the real work is in how AI is used to deliver outcomes, completed tasks, and resolutions. A conversation may start in a meeting, on a phone call, in a customer service engagement. The value Zoom provides is capturing that information and applying its AI tools, including Zoom AI Companion and others, to complete the work at hand. This is a whole new ballgame, and where the industry as a whole needs to be headed. Strategic Priorities Kicking off the event, Head of Market Insights and Analyst Relations, Cheri Hulse introduced Zoom’s strategic priorities, which would be reinforced throughout the event: Elevate Workplace with AI Drive growth of new AI products Scale AI-first CX These strategic priorities are vastly different from what we've seen from Zoom in the past. The company is still focused on its core offerings in Workplace, but it's all about enhancing these offerings with AI. Of course every vendor in the industry is AI-focused, but Zoom is adding new AI products to its offerings, but Zoom is using AI to change the way people work and the way work gets done. Conversation to Completion CEO Eric Yuan explained the concept of conversation to completion, noting, “We focus on the conversation and aim for completion. Before the AI era, that wasn’t Zoom’s strength, but now with AI, everything is possible and we can focus on the completion part.” He added that Zoom is evolving from a collaboration- and communications-centric company into a conversation and completion company, with AI acting as the connector. Zoom continues to expand its product breadth and depth with a full enterprise software stack and one connected platform. This is a different architectural diagram than we've seen in the past, with Zoom's many apps on the top layer, above the semantic layer, considered "the brain of the system." Similar to a routing engine, the Orchestrator turns conversations into coordinated actions and determines what happens next and where work needs to be done. Memory connects all your meetings, chats, documents, interactions, remembering what was discussed previously and carries that forward into the current conversations. Zoom's federated AI brings it all together. CFO Michelle Chang expanded on the conversations to completion concept, describing Zoom’s vision for a system of action. In this model, work happens both inside and outside the organization and is brought together into a unified system that drives outcomes. According to Chang, Zoom is redefining modern work by turning conversations and unstructured context into action and monetization. By connecting internal collaboration with external interactions, Zoom aims to transform conversations into outcomes through embedded context. She explained, “With the system of action, Zoom moves into a differentiated position in terms of the value we provide to customers. We bring context into action and move into a whole new relationship, and into a richer relationship with our customers.” Another major focus is reducing friction by connecting signals that already exist across the organization and turning them into actionable insights. Conversations take place across many environments—meeting rooms, webinars, and in-person interactions—creating a large amount of unstructured data. Zoom’s AI analyzes this information, extracts insights, and then drives automation and action. The System of Action for Modern Work Setting the stage for the rest of the sessions, CMO Kim Storin described Zoom’s system of action for modern work as a framework that turns live interaction into completed work. Built on Zoom’s integrated platform, the system captures context from conversations wherever work happens—both inside and outside the organization. This approach creates what Storin described as an execution surface that can observe and act on work in real time. AI then turns live context into completed workflows. For example, documents, presentations, and spreadsheets can be created directly within Zoom. The platform captures both the human-to-human interactions and the human-to-system and human-to-agent connections where work actually occurs. Storin explained that Zoom’s AI is embedded across the platform and captures context at the moment of interaction. Zoom’s federated AI model selects the most appropriate model for each task. As a result, the entire platform can be layered on top of this system-of-action concept. Storin also provided additional detail on the company’s strategic priorities. In this video interview, Storin discussed the key themes and messages from the event, as well as the Zoom Ahead marketing campaign. Scaling AI-First CX Analysts covering the customer experience market were particularly interested to hear that one of Zoom’s three priorities is scaling its AI-first CX strategy. While Zoom entered the CCaaS and CX market relatively recently, the company has moved quickly and now offers a competitive platform. As Chris Morrisey, GM of CX, pointed out, “At my first Zoom Perspectives two years ago, the contact center track was an interesting side conversation. Look at us today - we are a strategic pillar for the company.” The numbers reflect that momentum. Zoom CX grew in the high double digits in fiscal 2026, and all of the 10 largest CX deals in Q4 2025 included paid AI. Like most contact center vendors, Zoom is focusing on customer experience rather than just CCaaS. The goal is to enhance human intelligence with AI-driven automation. Scaling AI-first CX involves building a system of action across customer experience workflows and customer-facing processes. It also means unifying virtual agents and AI-assisted human agents to improve service outcomes while reducing costs. Morrisey explained that the Zoom CX platform delivers the same scalability and reliability as the broader Zoom platform, orchestrating workflows and completing tasks across the organization. The system-of-action approach includes more communication channels and customer touchpoints, all powered by federated AI. Building on the conversation-to-completion narrative, Zoom CX completes the customer journey by either taking action automatically or enabling the human agent when an interaction is transferred. Another important part of the CX strategy is CX Insights, which recommends automation opportunities—such as high-volume use cases—and estimates the impact on staffing levels. These insights also tie into Zoom’s workforce management capabilities. In this video, Chris Morrisey explained what conversation to completion means in the context of Zoom CX and provided an update on the platform. Drilling down further into Zoom CX, Michelle Couture discussed the key CX messages from the event. She expanded on Connected CX, which has a shared intelligence layer that provides insights and guidance to drive more connected customer journeys Couture also highlighted updates to Zoom Virtual Agent (ZVA) 3.0, including enhanced natural language, new administrative capabilities, and KB Suggest, where the virtual agent learns from live human engagement calls and autocreates knowledge.   Zoom Workplace No Zoom Perspectives event would be complete without a discussion about Zoom Workplace. In this video, Theresa Larkin, Head of Zoom Workplace & AI Product Marketing, discussed how the conversation-to-completion concept applies to the Workplace platform. Larkin explained how Zoom uses agentic capabilities to take actions such as triggering workflows, creating drafts, and building presentations. She also outlined Zoom’s agentic AI strategy and how the company combines structured and unstructured third-party data to trigger agent-driven workflows. In addition, Larkin highlighted new Workplace capabilities, including agentic features that enhance the meeting lifecycle. Closing Thoughts The conversation is no longer just about meetings and collaboration—it is about using AI and agentic AI to drive outcomes. Actions, task completion, and measurable results are the key focus areas, providing value beyond simply AI-enabling everything. Meeting summaries and interaction notes are useful, but the real value comes from what organizations do with that conversational data. This is where Zoom is placing its bets. Zoom occupies a unique position because it sits on top of many of the conversations organizations have internally and externally. These interactions occur across meetings, collaboration, calling, CX, workforce engagement (Workvivo), as well as events and webinars. Zoom is now extending beyond the conversation layer into completion capabilities, including the ability to create documents, presentations, and spreadsheets directly within the Zoom app with AI Docs, AI Sheets, and AI Slides. While many wondered why Zoom was getting into this space, but now it's all making sense. One challenge Zoom—and many other vendors—faces is enabling channel partners to effectively sell AI capabilities. Selling AI and business outcomes is very different from selling a meeting license. Zoom is working to address this through partner enablement programs, including advanced analytics and profitability dashboards. One technique the company has found effective is providing trial licenses so partners can use the technology themselves and better understand its value. As Nick Tidd, head of global channel GTM, said about enabling partners to sell AI, “use it and they will come.” The messaging at Zoom Perspectives was clear: Conversation to completion A system of action built on Zoom’s embedded AI that brings context and understanding to the organization Differentiation through a unified platform that connects conversations happening both inside and outside the organization Every business communication vendor is trying to differentiate and figure out where they fit in the new agentic AI world. Some will succeed, some won't. Some will lead, some will follow. Some will disrupt, some will be disrupted. Zoom is placing its bets on being a leader and disrupter. Based on what I heard at the company's recent analyst event, I think they have a great shot at it. Zoom made a convincing argument for its system of action and conversations to completion approach. The company's focus and values have always been on simplicity and happiness (Work Happy). Hopefully Zoom can maintain these values while competing in the fast-changing agentic AI world.

At its Zoho Day analyst event in Austin, TX, Zoho marked several key milestones. The company celebrated its 30th anniversary and reported more than one million paying customers and 150 million users. For a privately-held company with a long-standing emphasis on engineering over marketing, these figures reflect sustained growth at scale. Strategic Context Vijay Sundaram, Chief Strategy Officer, outlined the trends shaping Zoho’s strategy: AI has moved from experimentation to operational deployment. AI is accelerating innovation—and raising expectations. Best-of-breed buying has created application sprawl; organizations now seek consolidation and a single system of record. Spending is under scrutiny; value is increasingly tied to measurable outcomes. Systems of record, APIs, and infrastructure layers have become strategic assets. Sundaram argued that AI and agentic systems are reshaping the SaaS model. Under traditional SaaS, economic, security, and operational risk shifted from customer to vendor. With AI and agentic systems, elements of that risk shift back toward customers—particularly as they design, train, and deploy their own agents. Customers are no longer just users of predefined workflows; they are designers of behavior. As control increases, so does responsibility. Organizations must determine whether they are prepared to manage that risk. Zoho is approaching these changes in several ways: ·       Maniacally focusing on value, by building a model that delivers value while giving customers the freedom to customize Serving as the corporate system of record, providing context, permissions, and governance for AI. Driving customization through platform approach that allows customers and partners to build applications within a unified ecosystem. ·      Enabling massive automation with AI/agents The objective is to expand customer control without increasing operational exposure. Addressing Fragmentation Mani Vembu, CEO – Zoho Division, highlighted the core problem of enterprise software: enterprises run too many disconnected applications, systems are difficult to change, and each new tool often adds complexity rather than reducing it. The best-of-breed procurement model has led to fragmented architectures, overlapping vendors, and limited incremental value. AI amplifies this issue. Without unified systems, shared data models, and consistent knowledge structures, AI outputs are costly and inconsistent. Fragmentation exists at three levels: applications, middleware, and data. Users must navigate multiple systems, and IT retains limited centralized control. Vembu noted that mid-to-large enterprises average roughly 275 SaaS applications, while IT directly manages only a subset. Fragmentation, he argued, ends when software is designed as an integrated system rather than assembled from components.   AppOS To address this, Zoho introduced AppOS, a unified business application operating system. AppOS provides: A shared data foundation. Common business and process models. Built-in workflows and automation. Identity, permissions, and governance by default. Applications are built natively within the platform rather than integrated after the fact. Key implications: Data is structured consistently across applications. Business rules execute uniformly. Teams retain application-level control within centrally enforced standards. Applications can evolve without architectural rework. For customers, this translates to reduced time to value, lower total cost of ownership, and predictable governance.     Vertical Integration Zoho is also pursuing vertical integration across the technology stack. The company operates its own operating system, database (optimized for GPU workloads), internally developed LLMs and SLMs, and is developing proprietary hardware (details remain under NDA). This “delayering” approach spans: User experience Orchestration and execution Software infrastructure Hardware infrastructure By controlling the stack, Zoho aims to improve cost efficiency, performance, and data sovereignty. It also enables tighter integration between AI models, data, and applications. Zoho manages its own data centers and supports cloud, hybrid, and on-premises deployments—an increasingly relevant capability as data sovereignty requirements expand globally. In a discussion on AppOS and vertical integration, Chanadrashekar LSP, Managing Director, Zoho Canada, described Zoho’s evolution from a platform foundation  toward horizontal applications in HR, finance, and other domains. He explained that AppOS, once released, will serve as a platform layer that exposes core capabilities to partners and system integrators, enabling development of industry-specific solutions.     Zoho CX Within customer experience, Zoho emphasized orchestration across teams and workflows. The company’s CX focus includes: Unified CX work orchestration across customer-facing teams. Native, embedded AI for contextual intelligence. Personalization across stakeholders. A system of execution aligned to operational workflows. Modular architecture. Solution-oriented pricing. To get more insights into Zoho CX, I spoke with Dlip Nagarajan, Head of Product, Zoho CRM, and PVK, Head of Market Strategy, Zoho CX on CX Strategy  Cliq Zoho Cliq, the company’s team collaboration application, supports messaging, chat, voice and video calls, and meetings, didn’t get much attention on the stage, but is a key application for many customers. In a discussion with Shanthana Laksmi, Senior Marketing Analyst at Zoho, she noted that Zoho’s focus was on deeper AI integration into collaboration workflows. AI capabilities are being embedded directly into user workflows to act as proactive assistants. Zoho has also expanded workflow automation and unified related capabilities within the platform.   Customer Perspectives Customer sessions remain a core element of Zoho analyst events. I had the opportunity to speak with two customers who shared their experiences at the event. Newcross Healthcare Solutions Mo Umerji, Head of Enterprise Architecture, described Newcross’s use of Zoho One applications, including CRM, Creator, Recruit, Analytics, Campaigns, DataPrep, Expense, Desk, FlowMail, People, Sign, Survey, Contracts, Vault, and Cliq. Umerji emphasized that AI augments users rather than replaces them, noting “AI isn’t a replacement for a person. It’s about giving the user more power and more information to improve how they operate and how quickly they can do things." By building its healthcare application (Karivo) on Zoho Creator, Newcross can keep their data secure and centralized while reducing costs. Integris Credit Union Jeff Anderson, Vice President of IT, discussed Integris Credit Union’s use of Zoho applications, including CRM, Analytics, Directory, Expense, Cliq, Projects, Vault, Desk, Flow, SalesIQ, Campaigns, Assist, Bookings, DataPrep, Mail, Calendar, and Forms. Following a merger, Integris needed to integrate 30 applications, including Zoho and its in-house core banking system. Anderson explained that Zoho Analytics provides visibility into member behavior, churn, asset and loan aggregation, delinquency, and portfolio value. Key Themes Several themes emerged: ·      Platform over portfolio : Zoho is positioning itself as a unified application platform rather than a suite of loosely connected tools. With AppOS serving as the architectural foundation providing unification, rather than fragmentation, Zoho makes it easier for customer and partners to build custom app or customize existing Zoho apps using the same coding tools, with everything built on the same foundation. ·      Verticalized stack : By controlling hardware, infrastructure, database, operating system, and AI models, Zoho improves cost efficiency, optimizes performance, while better supporting data sovereignty by design. Zoho is in a unique position, as most competitors can’t provide this full stack at the cost and efficiency of Zoho. ·      Embedded AI : AI is integrated into the platform rather than layered on top. Internally developed LLMs and SLMs can be trained and deployed for specific customer requirements, with an emphasis on efficiency and latency control. ·      Data sovereignty and deployment flexibility : With owned data centers and support for cloud, hybrid, and on-premises models, Zoho addresses regulatory and geographic constraints. ·      Customization at scale : AppOS is intended to enable customers and partners to build vertical and team-specific applications using a shared foundation. Zoho’s goals for the event were to: ·      Strengthen its enterprise story year after year ·      Provide a clear understanding of the customer outcomes they want to create ·      Share its vision and strategy during this volatile, fast-changing time Based on what we heard at the event, the company succeeded in these areas.

Mitel’s introduction of Mitel WX (Workforce Experience) signals a meaningful shift in how enterprise communications are built and used. Instead of focusing primarily on meetings and messaging for desk-based employees, WX is designed as a communications framework that embeds real-time, voice-first interactions directly into day-to-day workflows. It brings together frontline, mobile, office, and contact center workers into a single, role-aware experience, while supporting flexible deployment across cloud, hybrid, edge, and on-premises environments. This isn’t a rebrand or incremental upgrade—it’s a rethink of how communications should work across a diverse, distributed workforce. What stands out is the focus on the 80% of workers who don’t sit at a desk. In industries like healthcare, manufacturing, retail, and field services, employees are constantly on the move and need immediate, often hands-free communication that fits into how they actually work. Mitel WX addresses this by tailoring the experience based on role and context, and by embedding communications directly into operational processes. Add in AI-driven workflow automation and low-code tools, and the platform starts to look less like a traditional UC solution and more like a system for getting work done faster and with fewer steps. From a market standpoint, this reflects a broader shift toward workflow-centric communications. Platforms like Microsoft Teams have set the standard for knowledge worker collaboration, but they’re still largely centered on meetings and chat. Mitel is taking a different approach—leaning into voice, real-time coordination, and deeper integration with business processes, especially in environments where timing and context matter. At the same time, WX doesn’t force customers to choose; it works alongside these platforms, extending their value rather than replacing them. For Mitel customers, that translates into a practical path forward. WX allows organizations to modernize without a disruptive rip-and-replace, while maintaining control over deployment, security, and data. It also creates a more consistent experience across all worker types, reducing fragmentation and reliance on workarounds like personal devices. The net effect is better coordination, faster response times, and more connected operations overall. If Mitel executes well, WX could position Mitel less as a traditional UC vendor and more as a leading provider of communications that directly support how work gets done.

Enterprise Connect 2026 has come and gone—and as always, it was a whirlwind. I spent nearly all of my time in vendor meetings and recording video interviews, attending only a handful of sessions in addition to the one I moderated. Unsurprisingly, the event centered on AI—particularly agentic AI—including virtual agents, workflow and agent studios, analytics, testing, and more. Below are the key themes that emerged across conversations.   Key Themes The word of the week: “vibe.” This showed up in two ways. First, “vibe coding,” with vendors noting that some applications are now being built in days—or even hours—reshaping development cycles. Second, the overall vibe of the event felt different. The move to Las Vegas, along with the absence of companies like Microsoft and Cisco on stage and in the exhibit hall, changed the dynamic. And, of course, many people missed gathering at Wreckers Bar. Voice is back. Voice is re-emerging as a critical interface—natural, intuitive, and rich in data. It’s increasingly treated as a core data channel, capturing customer intent, behavior, and preferences. That data is essential for training AI and powering agentic systems. Trust and governance are table stakes—but not enough. Vendors are focused on enterprise trust in their platforms, but end-user trust in AI interactions is just as important and often overlooked. The shift to agentic AI is moving faster than organizations can absorb. The rapid transition from generative to agentic AI has caught many companies off guard. Planning even 12–18 months ahead is difficult, and adoption is lagging behind innovation. Differentiation is getting harder. Vendors are struggling to stand out in an increasingly crowded and converging market. Salesforce is disruptive, creating discussions about where the CX stack will live. Its contact center announcement is already influencing vendor strategies and positioning. While some question maturity and pricing, others see it as a logical step toward deeper convergence of CRM and CX platforms, combining the CRM system of record with the CCaaS system of engagement. ROI remains unclear for many organizations. Beyond productivity gains, businesses are still working to quantify the value of AI investments. “SaaSpocolypse” discussions are gaining traction. With tools like Claude and similar platforms, AI agents can perform tasks traditionally handled by SaaS applications. This is prompting enterprises to reassess their software stacks. Pricing for agentic AI is unresolved. The industry is moving away from per-seat pricing, but there’s no consensus on alternatives. Options being explored include consumption-based pricing, outcome-based pricing, and even revenue-sharing models. Token-based pricing is widely viewed as unsustainable. Verticalization as a differentiation strategy. Vendors are increasingly focusing on industry-specific solutions, particularly in healthcare, as a way to stand out. Several vendors announced specific vertical applications, including GoTo Connect CX for Healthcare, Amazon Connect Health, and RingCentral AIR Pro for Healthcare. Expect to see continued focus on vertical applications as a way to differentiate. Businesses are looking for ROI and outcomes from AI beyond productivity enhancements. In addition to AI agents and automation, buzzwords included orchestration, autonomous, single-stack platforms, Agent Studios, trust/governance, sovereignty, guardrails, workflows, and vibe coding.   Vendor Announcements and Interviews As noted, most of my time was spent in vendor briefings. Where possible, I recorded short video interviews to capture what these announcements actually mean in practice.   AWS Pasquale DeMaio, VP of Amazon Connect, emphasized that “deflection is dead,” arguing that companies should focus on engagement rather than containment. AWS is positioning its tools to help businesses build longer-term customer relationships using agentic AI. He highlighted capabilities such as customizable agent dashboards, no-code workflow tools for business users, and built-in security and governance. He also discussed Amazon Connect Health, AWS’s new healthcare-focused offering.   Crescendo Crescendo won “Overall Best of Enterprise Connect 2026” for its Multimodal AI platform, which unifies voice, text, and visual interactions into a single conversation. New key capabilities include: AI-Surfaced Insights and Dynamic Visualizations Built-In Evidence, where insights include supporting detail ‘Time Travel’ Analysis with  built-in historical comparisons that let leaders step back through time to see how performance shifted over time Shared, Governed Environment Tod Famous, Chief Product Officer, described Crescendo’s approach as AI-first, combining automation with human labor while continuously optimizing both. Key capabilities include multimodal interactions (including image sharing), an AI-driven Insights Dashboard, and MCP-based agentic experiences. Famous explained how Crescendo’s multimodal AI platform lets consumers share and receive images while engaging in an interaction with the AI agent. .     Dialpad Dialpad’s latest offerings are aimed at helping businesses quickly and effectively deploy agentic workflows where they can provide the greatest ROI and business outcomes. Dialpad is focusing on the full lifecycle of agentic AI—from discovery through deployment and governance. Its approach includes: Skill Mining to identify automation opportunities Agent Studio to build agents Proving Ground to test them Guardian to enforce compliance and safety Calvin Hohener, lead product manager, and Fabrice Della Mea , Vice President of Product Management, Contact Center emphasized how Dialpad’s agent AI solution with analytics across a single platform creates a continuous feedback loop between human and AI agents.       Five9 Five9’s Matt McGinnis, VP Product Marketing, provided an update on Five9’s AI progression, highlighting its continued AI evolution with: Genius Routing to match customers with the right agent (human or AI) AI Insights with Spotlight for visibility into key trends Agentic Quality Management for full interaction coverage across human and AI agents The company also expanded its Fusion ecosystem to go beyond integrations and support broader CX partnerships.     GoTo David Evans, VP Product Management, described how GoTo is expanding its vertical strategy into healthcare, building on its success in automotive. GoTo is focusing on SMB customers and large customers with multiple locations, particularly retail and clinic-based healthcare organizations, with its single unified communication and CX platform. Tools like AI Receptionist support 24/7 service and scheduling while maintaining compliance.     Infobip Infobip introduced AgentOS, a modular agentic platform spanning sales, marketing, and service. According to Krešo Žmak, Chief Information Officer, the company’s key differentiation is rapid deployment—enabling customers to define use cases and launch proofs of concept quickly—as well as strong native channel integration and MCP-based development for agentic workflows.     Journey Journey is  the Zero Knowledge transaction layer for contact centers and agentic AI—enabling payment completions, user authentication, and regulated data collection. Journey’s President, COO, and Founder, Alex Shockley, discussed the availability of Journey.ai  Payments, Journey Authentication, Journey Forms, and Journey Signatures within Zoom Virtual Agent (ZVA) to extend end-to-end automation to workflows involving sensitive, regulated, or complex data. These integrations extend automation into regulated workflows. Separately, during a hands-on demo of ZVA 3.0, I experienced first-hand how Zoom’s virtual agent seamlessly and quickly passed my food ordering interaction to Journey for payment authentication.   LeapXpert LeapXpert enables compliant business communication across messaging platforms like WhatsApp and Signal. The platform provides governance, security, and threat protection, while also capturing conversation data to generate insights and trigger workflows. Dima Gutzeit, Founder and CEO of LeapXpert, discussed how the company helps enterprises use messaging channels such as WhatsApp, Signal, etc., isecurely, providing governance, while protecting the company from threats such as viruses and malware.   Mitel Mitel introduced two major offerings: Mitel WX (Workforce Experience): A unified, AI-enabled platform connecting frontline, mobile, and knowledge workers within their workflows Mitel Edge: Extends hybrid communications to on-prem environments for resiliency, security, and local control. Designed for environments that need high reliability, security, and data control, features include: Local call control System survivability if connectivity fails Edge-based routing Secure remote access Data sovereignty support Mitel CMO Eric Hanson noted that Mitel WX is a new user experience for all types of workers – frontline, knowledge workers, and contact center agents - offering a widget-based design that surfaces the right tools for users in a single pane of glass. He explains that Mitel Edge builds on Mitel’s hybrid strategy, which can be deployed on-prem or private cloud. These announcements reinforce Mitel’s hybrid strategy and focus on vertical use cases.     NiCE NiCE won Best Innovation for Customer Experience for Automated Insights, which converts interaction data into structured intelligence and automatically generates AI agents. Carmit DiAndrea, head of AI Data Management, explained how the platform identifies automation opportunities, quantifies ROI, and continuously learns from high-performing interactions. Automated Insights analyzes cost to serve, customer experience, revenue, and compliance to identify top automation opportunities and generate workflows that drive business value. Built into NiCE AI Agents (Cognigy), Automated Insights continuously learns from top human interactions and ideal resolutions to automatically create high-performing AI agents.     Jennifer Wilson provided an update on NiCE Workforce Engagement Management (WEM) and Workforce Optimization (WFO). Using both human and AI agents in the workforce requires changes in the way organizations approach forecasting, scheduling, quality management, etc. Wilson noted that NiCE can monitor AI and human agents individually or together as a workforce and determine where interactions are best served by AI agents and where human agents should assist. She also discussed using copilots for workforce management to proactively assist supervisors and uncover trends and correlations.     Salesforce Salesforce’s Agentforce Contact Center was one of the most discussed announcements at Enterprise Connect. It brings voice, AI, and CRM into a single native platform. This move positions Salesforce more directly against traditional CCaaS vendors, many of whom have historically partnered with Salesforce. According to Ashish Seth, VP Products, Agentforce Contact Center, “For Salesforce to be successful in CX, we need to own voice and be in the contact center space. If you own the channels, you own agentic experiences. Convergence has been happening for a long time, CCaaS and CRM had to come together.” During a meeting, Seth explained that Salesforce owns the voice channel – from the edge to desktop, noting, “We created the telecom and voice infrastructure, which is 100% native and operated by Salesforce.” Agentforce Contact Center is generally available as an add-on to all Agentforce Service customers in the US and Canada.      Talkdesk Talkdesk introduced the CXA Operations Center along with enhanced analytics tools to manage AI agents alongside human workers. The focus is on operationalizing AI—covering discovery, deployment, monitoring, and optimization—while ensuring governance and performance in production environments. As Pedro Andrade, VP of AI for Talkdesk told a group of analysts,   “AI agents should be part of your workforce. The   challenge is coordinating AI with your human workforce. If AI is part of the team, it must be governed as part of the workforce. If AI is part of the team, it must be governed as part of the workforce.” The CXA Operations Center provides a centralized interface for managing the operational lifecycle of AI agents in live production, ensuring that AI performance remains consistent and safe as customer scenarios evolve. Key features include AI Agent Observability to provide real-time operational intelligence into how AI agents behave in production, and AI Agent Evaluation to ensure AI agents perform reliably, safely, and consistently. The enhanced Talkdesk Interaction and Quality Analytics provide CX Insights by connecting conversational intelligence directly to business outcomes, and Automation Mining to identify high-impact automation opportunities within customer interactions.     TechSee TechSee announced enhanced Visual AI capabilities integrated into Sophie Live, an intelligent remote visual assistant designed to help service organizations resolve their most complex and costly customer issues. Sophie Live is an intelligent visual service platform for enterprise contact centers that combines live visual assistance with embedded Visual AI to help agents resolve complex, situational issues faster. Features include Visual Agent Assist, pre-call visual intake, and built-in connectivity intelligence. Elizabeth Tobey, SVP of Marketing, explained how TechSee works with companies to help understand and solve the problem using visual capabilities. As a visual intelligence layer, TechSee works with third parties like Salesforce, NiCE, ServiceNow, and others, acting as a visual Tier 2 tech. At Enterprise Connect, TechSee announced an end-to-end solution for self-service through field service for a seamless journey. UJET UJET introduced AXO (Agentic Experience Orientation), a persistent AI layer that maintains context across the entire customer journey. The platform uses a “self-improving loop” to identify successful interactions and apply those patterns to future engagements. It also incorporates Computer Using Agents (CUAs) to execute workflows autonomously. According to CEO Vasili Triant, a self-Improving loop is where the conversational engine and the customer data are one and the same. He explains that when an interaction ends successfully, AXO identifies the ‘successful DNA’ of that call. The next time an agent faces a similar problem, the system proactively orchestrates the specific guidance based on the successful call. Matt Clare, UJET’s Vice President Product Marketing, explained that AXO unifies disparate data sources within the contact center and uses AI to consolidate this data and recommend what workflows to automate. It uses these contextual data sources to provide real-time assistance to contextually display information to agents in real time.     Verint Verint provided its first major update following the Calabrio acquisition. Key points: ·      There is no big ‘platform consolidation’ project ·      Verint is not going through a big ‘migrate the base’ project ·      Calabrio customers can now deploy Verint Bots to their existing environment ·      No products are targeted for end of life. For now, Verint will continue developing on both product lines. ·      There will be one product team for Verint and Calabrio. ·      Verint is maintaining the Calabrio name as a product brand. The strategy focuses on interoperability and expanding capabilities without disrupting customers. Calabrio customers now have access to Verint’s AI bots through the Verint CX Automation Platform, including Genie, TimeFlex, Intelligent Virtual Assistant, and Copilot. Verint customers now have access to Calabrio workforce engagement tools, such as workforce management, performance analytics, and quality monitoring, expanding Verint’s capabilities in agent management and scheduling.    Dave Singer, Global Vice President, Go-To-Market Strategy explained that the company is building all of its AI and bots to work on either of the product lines.     Zoom Zoom announced more than 45 AI enhancements, positioning its platform as a system of action rather than just a meetings tool. Key updates include: AI Companion as an agentic platform Zoom Virtual Agent 3.0 AI Expert Assist 3.0 Customer Workflow Orchestration using natural language   The Zoom enterprise agentic AI 3.0 platform features a range of new features, including: Custom and prebuilt AI agents with no-code orchestration New third-party integrations for AI Companion, including 10 secure enterprise search connectors New AI-first canvases (Zoom AI Docs, AI Sheets, and AI Slides) Zoom Phone Mobile SMS for Zoom Virtual Agent AI Receptionist AI Expert Assist 3.0 for Zoom Contact Center Natural-language customer workflow orchestration Meeting security enhancements   Zoom Phone features new Agentic workflows, SMS for Zoom Virtual Agent AI Receptionist (formerly AI Concierge), Zoom Phone Mobile, and more.   For Zoom CX, new capabilities include: Zoom Virtual Agent 3.0 Agentic AI Expert Assist 3.0   with a real-time, agentic AI layer empowering agents and supervisors with contextual guidance, intelligent orchestration, and automated task execution Zoom CX Insights, enabling businesses and CX leaders to ask questions about customer experience, workforce, and automation performance in natural language Customer Workflow Orchestration to   design and automate customer journeys across systems, channels, and teams using natural-language workflow creation triggered by contact center events or signals from enterprise systems Advanced Quality Management for Zoom Virtual Agent   The emphasis is on reducing friction and enabling workflows that translate interactions directly into outcomes. During a demo at Zoom’s booth I saw Zoom Virtual Agent (ZVA) 3.0, AI Expert Assist 3.0, and Customer Workflow Orchestration in action. ZVA makes it easy for customers to engage with businesses using natural language, while AI Expert Assist 3.0 helps contact center agents provide faster and better service to customers, even performing tasks on the human agent’s behalf. One of the most impressive product announcements made at Enterprise Connect was Zoom’s Customer Workflow Orchestration, which lets administrators simply use natural language to create workflows.   Ram Rajagopalan, Ted Yoshikawa, and Kentis Gopalla discussed Zoom Virtual Agent 3.0, Agentic Expert Assist, which offers a live AI interpreter for real-time speech translation, and CX Insights, providing recommended actions and more.     Final Thoughts on Enterprise Connect 2026   As noted, the vibe this year was different, with people speculating if this is the final year of our beloved Enterprise Connect. While Informa has plans to host the event at the MGM Grand in 2027, many vendors complained about the lack of customers attending the event, questioning whether the show has lost its mojo.   With its roots in enterprise business voice communications (the event was previously called VoiceCon), earlier sessions and conversations were around IP-PBXs, SIP trunking, SBCs, and softswitches. Today, Enterprise Connect is viewed as a CX show, with “traditional” UCaaS vendors touting their CX capabilities. Every keynote included a CX component, with CX vendors representing a large portion of the vendor sponsors and exhibitors.   Things are moving at an extremely fast pace. In 2025, people were just learning about AI and trying to figure out what agentic AI was all about; in 2026, agentic AI and autonomous AI agents were part of every discussion and demo. 2025 was about introducing the concept of AI agents; 2026 was about using low code/no code tools to build and integrate AI agents into workflows.   My prediction for 2027 – a key theme will be personal AI agents that perform tasks on behalf of individuals and interact with business AI agents. If you’d like to discuss this, have your AI agent call my AI agent.

RingCentral has had a busy year, with new products, ongoing innovation, and several leadership additions. At the company’s 2026 Analyst Summit in Scottsdale, Arizona, analysts received an update on RingCentral’s business momentum and a preview of upcoming announcements—some of which will be formally introduced at Enterprise Connect. In her first year as President and COO Kira Makagon, opened the event by framing the broader industry shift. She described the current moment as an inflection point, with AI reshaping how people work, evolving buyer expectations, and a market in transition. As she noted, “We’re in the industry that lets people communicate—we’re at the essence of what AI enables people to do.” Rather than replacing communication, AI enhances it. Makagon summarized RingCentral’s focus simply: “Our mission is to shape the communication that is now aided by AI.” Expanding the Portfolio with Agentic AI RingCentral has long been a major player in business communications, beginning as an early cloud telephony provider and expanding into meetings, collaboration, video, events, and contact center platforms. Today the company is embedding AI—and increasingly agentic AI—across both customer and employee communications. Over the past year, RingCentral introduced several new products while adding AI capabilities across its existing portfolio, including: AI Receptionist (AIR) AI Virtual Assistant (AVA) AI Conversational Expert (ACE) Workforce Engagement Management (RingWEM) Customer Engagement Bundle for informal contact centers AI Meetings (AIM) for video, meetings, and chat Adoption appears strong, as RingCentral reported more than $100 million in annual recurring revenue from new products in 2025. The “Three A’s”: AIR, AVA, and ACE A central theme of the event was what RingCentral calls the “Power of AND”—the way its three core AI offerings work together across the customer journey. AIR serves as the front door. This 24/7 voice AI agent greets customers, answers and routes calls, provides self-service, captures leads, and schedules appointments. When human interaction is needed, AVA assists employees in real time, acting as an AI coworker that provides guidance and support during the conversation. After the interaction, ACE analyzes conversations and calling patterns using conversation intelligence. The insights feed back into the system to support continuous learning and improvement. To explore RingCentral’s AI strategy in more detail, I spoke with Steven Zachok, VP and GM of AI. In the interview below, Zachok explains how RingCentral aims to deliver a “system of outcomes” that drives action throughout the customer engagement lifecycle. He also discusses how the three A’s work together to support customer engagement, and how RingCentral differentiates with a voice-first architecture where AI is built directly into the platform rather than added as a separate layer. AI Receptionist (AIR) AIR was one of the most discussed products at the event. The platform is designed to deliver a conversational, human-like experience while automating routine front-office tasks. AIR can: Answer and route calls Respond to common questions Capture and qualify leads Schedule appointments and reservations It can also run as a standalone solution alongside existing SIP-based PBXs, potentially opening the door for organizations that are not ready to replace their current phone systems. I spoke with Joe Fahrner, GM of AIR, about how organizations are deploying the solution. In the video below, Fahrner discusses common use cases, including how businesses use AIR to provide a 24/7 receptionist capability. He also outlines adoption patterns across several vertical markets and highlights operational and cost benefits customers are seeing. AVA: AI Assistance for Employees I also spoke with Ashu Varshney, SVP of EX Products, about AVA, RingCentral’s AI Voice Assistant. AVA functions as an AI copilot across RingCentral’s EX, CX, and meetings platforms. It allows users to interact with the system using natural language prompts similar to ChatGPT. During interactions and meetings, AVA can: Take notes automatically Generate summaries and insights Recommend next-best actions Transcribe conversations and meetings In this discussion, Varshney outlined several use cases and customer benefits, along with a preview of capabilities on the roadmap. ACE: Intelligence Across Interactions The third component of the “three A’s” is ACE, formerly RingSense, which provides conversation intelligence and analytics across interactions. ACE analyzes every conversation to identify why customers are calling, what topics they discuss, and even whether competitors are mentioned. The platform surfaces insights that help organizations identify customer friction points and convert those insights into operational improvements. CX Takes Center Stage RingCentral continues to expand its customer experience portfolio. In addition to RingCX, which targets mid-market and enterprise contact centers, and RingCentral Contact Center for large enterprises, the company now offers the Customer Engagement Bundle (CEB) for informal contact center environments. CEB runs on the RingEX platform and is designed for employees who interact with customers but are not traditional contact center agents. The goal is to provide contact-center-style capabilities with lower complexity and faster adoption. Since its availability in late 2025, more than 1,000 customers have deployed CEB. RingCX supports voice, along with 20 digital channels and recently added native workforce engagement management through RingWEM. RingCentral Contact Center, leverages technology from NICE for enterprise-grade deployments. In the following video, Jim Dvorkin, SVP of CX Products, provides an overview of the CX portfolio and recent momentum. He discusses the Customer Engagement Bundle, RingCX, RingCentral Contact Center, RingWEM, and AI Interaction Analytics. I also spoke with John Finch, VP, Global Product Marketing, about how RingCentral’s AI agents work together in real customer service scenarios. In this discussion, Finch explains how the company approaches automation across the full interaction lifecycle—before, during, and after conversations—and how AIR, AVA, and ACE combine to automate, assist, and analyze customer interactions while keeping humans in the loop. Bringing It All Together As President and COO, Makagon leads RingCentral’s product and technology organization, overseeing marketing, strategy, engineering, and operations. Throughout the event, she emphasized the company’s focus on practical innovation and customer outcomes. In this closing video, Makagon summarizes the event’s key themes, including RingCentral’s position in agentic AI and how AIR, AVA, and ACE work together under the “Power of AND.” She also highlights several sessions from the summit, including previews of upcoming announcements and discussions with customers and channel partners. Final Thoughts While RingCentral continues to innovate and increasingly focus on its CX and contact center offerings, it remains true to its roots as voice communications remain central to its strategy. Throughout the summit, executives emphasized the value of voice as a source of insight that can drive better business outcomes. The company has spent years enhancing its voice network, based on a redundant, reliable, secure standards-based architecture   With ongoing AI innovation and a broad portfolio spanning employee and customer communications – including calling, messaging, video, collaboration, and events, as well as a range of contact center and CX products, RingCentral is positioning itself to play a significant role as AI reshapes how businesses communicate.