Winter 2026 Sponsored by Volume 25 Issue 1 | channelvisionmag.com Cybersecurity REDUX AI Disillusionment ChannelVision CELEBRATES 25 YEARS AWARDs WINNERS
WINTER 2026 AI & AUTOMATION 8 AI correction 10 Edge AI explodes 12 Beyond Fulfillment The New Rules of Channel Partnerships in the AI Era By Peter Bocquet 16 Avoiding AI’s Disillusionment AI users report pain points and challenges; advisors better know them By Martin Vilaboy 20 Partner Smarter Clarus Resolve gives advisors a competitive edge CHANNEL MANAGEMENT 22 Hidden PCI Hazards Payment security risks channel partners inherit from client environments By Chris Brown 26 2026 Channel Innovation & Partner Engagement Predictions By Daniel Nissan 28 Pivoting for Impact Talkdesk’s revamped partner program is primed for a breakout year in 2026 30 Trust Factor NHC has emerged as one of the channel’s most dependable suppliers CYBER PATROL 32 Cybersecurity Redux After being overshadowed by AI, cybersecurity is primed for a spending rebound in 2026 By Martin Vilaboy 36 Securing the Digital Front Line SilverSky helps partners keep organizations connected and protected By Gerald Baldino EDGE TO CLOUD 38 VMware’s Last Hoorah VMware partners have a narrowing window to act By Gerald Baldino 42 Cloud 2.0 Arrives Lumen is laying the groundwork for the next era of cloud computing By Gerald Baldino 44 PTC’26 Delivers Unmatched Global Connection and Industry Momentum by Carolyn Pohl 46 2026 CVAI Award Winners MOBILE & WIRELESS 54 Low Hanging Fruit Samsung’s mobile devices provide an easy entry point for AI and productivity gains By Gerald Baldino CORE COMMUNICATIONS 55 Top Conferencing Trends for 2026 6 Editor’s Letter 56 ICYMI 58 Ad index CONTENTS Volume 25 – Issue 1 4 CHANNELVISION | WINTER 2026
To put the 25th Anniversary of ChannelVision into perspective – and all that has evolved and transformed since the magazine’s official launch in early 2001 – consider that when I went to the archives to reminiscence over the premiere issue, I was unable to access it because the external hard drive the issue was stored on no longer worked. I’m told that’s fairly common for first-generation storage devices from the early 2000s. The oldest issues also were backed up on CD-ROMs, mind you, but it’s hard to find a device that can read a CD-ROM nowadays. To say a lot has changed during the past two and a half decades within the network services indirect channel is like saying it gets really hot in Phoenix in July. If you’ve been through it, you already know. But looking through today’s lens at the coverage topics in the first few issues, things sure seemed rather simple back then. There was a feature story about the shrinking margins around DSL (that was an early form of broadband for the post-GenX readers), a report on how “attacking” cable companies were seizing business from telecom agents, a deep dive into the wholesale optical fiber services market and articles on the huge opportunities around conferencing and selling voice and data converged onto one bill. Basically, if it didn’t involve voice or data or the network over which they were transported, it was barely on anyone’s radar. Fast forward through the years, and you’ll see how telecom agents and channel partners have navigated a constant stream of disruption – from analog to IP telephony and frame relay to MPLS to SD-WAN, through remote monitoring and virtualized services to work from anywhere and the shift to the cloud and SaaS, to a mobile revolution and eventually to where we are today with artificial intelligence. The large “master agents” of yesteryear that used to represent more than a dozen suppliers are now “technology solutions distributors” with more than 100 suppliers in their portfolios. Indeed, today’s technology advisors and MPS must keep track of an ever-expanding menu of business and IT services. The team at ChannelVision can relate. Twenty-five years ago, almost all of our revenue was generated from print display advertising. Today, our menu of services includes print and digital publications, custom content services, email marketing, webinars, podcasts, video interviews, whitepapers, AI optimization (AIO), and the annual CVxEXPO conference and trade show. The mission, however, resolutely remains the same: helping our audience and customers navigate an ever-complex market, sell more effectively and bolster their bottom lines. Just imagine what the next 25 years will bring. 25 YEARS OF TRANSFORMATION LETTER Martin Vilaboy Editor-in-Chief martin@bekabusinessmedia.com Gerald Baldino Contributing Editor gerald@bekabusinessmedia.com Tony Jones Contributing Editor tony@bekabusinessmedia.com Percy Zamora Art Director percy@bekabusinessmedia.com Jen Vilaboy Ad Production Director jen@bekabusinessmedia.com Berge Kaprelian Group Publisher berge@bekabusinessmedia.com (480) 503-0770 Beka Business Media Berge Kaprelian President and CEO Corporate Headquarters 10115 E Bell Road, Suite 107 - #517 Scottsdale, Arizona 85260 Voice: 480.503.0770 Email: berge@bekabusinessmedia.com © 2026 Beka Business Media, All rights reserved. Reproduction in whole or in any form or medium without express written permission of Beka Business Media is prohibited. ChannelVision and the ChannelVision logo are trademarks of Beka Business Media The February 2007 issue included articles on the “disruption” caused by IP-based services, VARs moving into voice services and the early winners within managed services. 6 CHANNELVISION | WINTER 2026
Despite the seemingly insatiable appetite for AI among boards and C-suites, the current supply of agentic AI models, platforms and products far exceeds demand, argued Gartner analysts. The business and technology insights company anticipates that agentic AI markets will consolidate in the short term as hype and fear of missing out (FOMO) give way to fundamental economics.The losers of consolidation will be undifferentiated AI companies and their investors. The winners will be capital-rich incumbents with the resources to acquire promising technologies and talent, Gartner predicted. “While we see early signs of market correction and consolidation, product leaders should recognize this as a regular part of the product life cycle, not a sign of inevitable economic crisis,” said Will Sommer, senior director analyst at Gartner. “Over the longer term, consolidation will enable industry leaders to develop agentic products that meet the technical and business requirements of customers who are presently struggling to adopt AI agents. Product leaders can view this correction similar to others in energy and technology as a market transitional period in which business models are forced to calibrate to transformational technologies, explained Gartner analysts. “The impending agentic AI market correction is distinct from speculative bubbles fueled by systemic financial engineering, fraud or policy,” Sommer said. “At this point, the underlying product, agentic AI, is sound, and the current market correction, where markets rationalize and consolidate, is a regular part of the product life cycle. “However, a ‘speculative bubble’ could still form if investment becomes detached from agentic AI’s intrinsic potential to deliver tangible and commensurate economic value,” he continued. Large tech companies have already been acquiring smaller, specialized AI firms, signaling the start of the market correction phase, said Gartner. A provider of cloud-native application protection and extended detection and response (XDR), Uptycs announced that in the weeks following the debut of its Juno AI Analyst, it has already been deployed by major automotive manufacturers, banks and enterprises, replacing first-generation solutions from legacy security vendors focused on compliance over verifiable protection. Unlike standard “AI co-pilots” that summarize alerts from disparate tools, Juno is an agentic investigator backed by five pending patents, said Uptycs. Juno was designed to solve the primary barrier to AI adoption in cloud-native application protection platforms (CNAPP): Trust. “Most of the time, uncertainty rules in the world of cybersecurity,” said Ganesh Pai, CEO of Uptycs. “Juno’s evidence-based approach uses AI to replace opaque ‘black box’ answers with transparent, verifiable reasoning grounded in real telemetry, so security teams can trust what they’re seeing and act with confidence.” Juno utilizes a “Glass Box” approach, said Uptycs. It does not just generate answers; it executes deterministic SQL queries against a purposebuilt Unified Multi-Cloud Ontology, a massive, normalized schema of more than 3,000 tables and 150,000 columns of security telemetry. This architecture allows Juno to take “surgical sips” of data, retrieving precise, raw evidence to prove its findings, rather than ingesting a firehose of noise that leads to hallucinations, explained the company. “Enterprise adoption, meanwhile, validates that the market is shifting from “chatbot” novelties to structural engineering solutions,” said Uptycs executive. “Juno addresses the cybersecurity professional’s aspiration to leverage a conversational interface, interact with their own data and harvest insights, explanations and recommendations,” added Srinivas Tummalapenta, CTO, IBM CyberSecurity Services. Agentic AI Correction Looms Uptycs Seeks to Break ‘AI Hallucination’ Cycle AI & AUTOMATION The AI Adoption Gap Source: Gartner (October 2025) If an AI bubble were to bust, how would it impact your organization’s Investment strategy? Among C-suite repondents Source: Accenture Which, if any, of the following have you done to improve the outputs of Generative AI tools in your company? 7% 31% 16% 35% 10% Significantly decrease investments (20% or more) Significantly decrease investments (up to 20%) No changes Somewhat increase investments (up to 20%) Significantly increase investment (20% or more) Training staff to better use AI Encouraged teams to share tips and examples for effective use of AI Used AI agents to improve outputs Use process intelligence to improve analysis Used Document AI/intelligent document processing to improve the outputs Set up regular check-ins to review AI use Considering alternative AI tools Used Retrieval augmented generation (RAG) to improve the outputs Asked staff to manually check and correct the outputs Scaling back or removing AI tools that weren’t working We got rid of all AI tools and stopped using AI completely 50% 43% 36% 35% 35% 33% 28% 25% 25% 20% 18% AGI Neurosymbolic AI Ambient AI Agentic AI AI Adoption Gap Time AI Innovation Race (Providers) AI Outcome Race (Customers) AI Agents GenAI Traditional AI (ML, NLP) 8 CHANNELVISION | WINTER 2026
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According to a survey by process optimization company ABBYY, GenAI in the enterprise is mainly used for revenue-impacting analysis and employee productivity. Summarizing data is the most common use case, empowering employees’ ability to be more strategic. The primary drivers for initial adoption include increased efficiency, enhanced customer service and the ability to implement observed results. Responsibility for AI adoption for employee productivity is led by IT, said the ABBYY study. SoundHound AI, a provider of voice and conversational AI, has partnered with leading tech advisory firm, Bridgepointe Technologies, to bring its enterprise AI solutions to Bridgepointe’s vast customer base through its expansive team of expert advisors, consultants and engineers. SoundHound’s technology is designed to handle a broad variety of customer and employee interactions, including task completion, information retrieval and transaction processing. The Amelia 7 platform can handle complex multi-step user queries by orchestrating multiple AI agents with specific functions to answer questions, execute transactions and resolve problems via text or voice, without the need for human intervention. And unlike other agentic platforms, Amelia’s AI agents come with best-inclass automatic speech recognition (ASR), which means they can easily understand human speech, allowing customers to get things done just by speaking naturally, said the company. Amelia 7’s AI Agents use Agentic+ – a flexible combination of generative AI, multi-agent autonomous orchestration, traditional deterministic models and human-in-the-loop to complete tasks based on the best possible path to the right response. How GenAI is Being Used by Businesses Bridgepointe Partners with SoundHound to Expand Enterprise AI Adoption AI & AUTOMATION The Edge AI Market is rapidly emerging as a cornerstone of digital transformation across industries, driven by the need for real-time data processing, enhanced security, and decentralized decision-making, argued analysts at Polaris Market Research. The firm projects the global market will expand from $18.07 billion in 2024 to a whopping $108 billion in 2034, a CAGR of 19.7 percent during the forecast period. Defined as the integration of AI algorithms directly on hardware devices at the network edge, edge AI enables on-device computation without reliance on remote cloud servers. This capability reduces latency, lowers bandwidth costs and strengthens data privacy, explained Polaris. “Edge AI enables instantaneous insights by processing data directly where it’s generated — on smartphones, sensors, cameras and autonomous systems — eliminating delays associated with cloud roundtrips,” said the research firm. “This capability is especially critical in applications where milliseconds matter, such as autonomous driving, quality inspection systems and remote medical monitoring.” The global deployment of 5G networks is a major enabler for edge AI adoption, said Polaris. “The higher bandwidth and significantly lower latency provided by 5G infrastructure enhance deviceto-device communication and support large-scale edge deployments across smart cities, industrial IoT (IIoT), and next-generation telecommunication networks.” Edge AI Market Set to Explode In which areas have generative AI tools been deployed in your organization? Data analysis and insights generation (such as summarizing trends, predictive modeling, data cleaning) 62% Customer service operations (such as intelligent ticket routing, automated responses, call summarization) 52% Employee productivity (such as writing assistance, project management, etc.) 52% Automating document business processes (such as accounts payable, logistics) 50% Risk management and compliance (such as anomaly detection, policy summarization, audit preparation) 47% Sales and marketing optimization (such as lead scoring, personalized content creation, campaign optimization) 46% Customer-facing applications (such as chatbots, email campaigns, etc.) 44% Process intelligence (giving a holistic view of processes and where they can be improved) 43% Operations optimization (such as revenue and cost reductions) 43% Product development and innovation (such as idea generation, rapid prototyping, product design) 41% Source: ABBYY 10 CHANNELVISION | WINTER 2026
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Beyond Fulfillment The New Rules of Channel Partnerships in the AI Era Channel partnerships used to be about fulfillment – getting products to market efficiently, reliably and at scale. Today, that’s the floor, not the ceiling. With hybrid cloud solutions and AI redefining enterprise infrastructure, the nature of partnership has evolved. It is no longer sufficient to deliver a single product or deployment. What is required today is a partnership approach that transcends vendors, services and long-term strategy. This shift from transactional to transformational demands trust, adaptability and the willingness to co-create in ways that were not necessary or even possible before. Across industries, companies are asking partners to help them solve broader, more complex challenges. That might mean speeding up data processing for AI model training, streamlining compliance in regulated industries or simply creating more sustainable infrastructure. These aren’t problems that can be addressed in isolation. What’s different is the expectation: partners are being drawn into the complete solution cycle, from architecture and integration through support and innovation. It’s no longer just about selling a product; it’s about helping create systems that can grow and evolve with the business. That shift is especially relevant in AI and data infrastructure. According to Hitachi Vantara’s recent State of Data Infrastructure report, 100 percent of companies surveyed have now adopted AI in some capacity. More than 75 percent have moved past pilots into production, and more than a third say AI already is critical to their core business. With that, the pressure on infrastructure – and those who support it – has intensified. AI & AUTOMATION By Peter Bocquet The Benefits of Working with a Partner Creating an infrastructure that works across hardware, software and AI strategy 46% Creating structures/frameworks that are future-proof 45% Working with people who have proven results 41% Sustainability solutions are included 40% The ability to test solutions before implementation 38% Avoiding common mistakes 36% Staff training included 35% Baked in security 27% Source: State of Data Infrastructure; Hitachi Ventures 12 CHANNELVISION | WINTER 2026
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Pressure from All Sides AI doesn’t just introduce new capabilities; it exposes old bottlenecks. Many organizations expect their data storage needs to double by 2026, yet they’re already managing three times more data than anticipated. Nearly half of IT leaders cite data quality and availability as top concerns. The result? A rising demand for channel partners who can help not just with delivery but with design, implementation and optimization across hybrid environments. The State of Data Infrastructure Report found that 42 percent of IT leaders cite managing hybrid and multi-cloud environments as one of their top infrastructure challenges – underscoring the need for partners who can navigate that complexity with both speed and precision. To respond, more alliances are forming between infrastructure companies, cloud providers, ISVs and services firms – often blending capabilities to build joint solutions that solve business problems at scale. That’s where the real value of today’s partner ecosystem lies. Why Services Are the Stickiest Layer One of the more visible changes in channel strategy is the growing emphasis on services. Products alone don’t differentiate anymore. What matters is who can make them work – together, securely and efficiently. Up to 58 percent of IT leaders are prioritizing partner services to manage modern IT complexity, according to the infrastructure report. And according to IDC, global spending on professional and managed services tied to digital infrastructure is expected to surpass $400 billion in 2026. That might involve onboarding large data sets, ensuring interoperability between platforms or navigating the nuances of governance and compliance. The service piece becomes the connective tissue. It’s what allows organizations to move fast without compromising stability or trust. And this is where many partners are finding new relevance: by building capabilities that go well beyond fulfillment and into strategy, training, optimization and support. A Case for Ecosystems Old models prioritized control. But in today’s environment, flexibility is more valuable than exclusivity. The strongest partnerships tend to be those with built-in openness – where both sides can challenge assumptions, adapt offerings and share insights. That often starts with cultural fit. Successful collaborations require more than aligned technology; they need aligned incentives and a shared approach to problem-solving. In some cases, that might involve co-developing new solutions; in others, simply sharing go-to-market insights to better serve a mutual customer. It’s not a handoff. It’s a handshake. Beyond the Hype With AI adoption growing more than 220 percent in 2026 (as projected in the report), there’s pressure to move quickly. Yet there is a genuine danger of over-promising or concentrating too much on near-term victories such as launching a chatbot or retrofitting a workflow with machine learning. The companies making headway with AI are usually those addressing it as a foundation change – a priority, not an add-on. That implies reimagining infrastructure, data governance, talent models, and yes, channel strategy. Partners who can facilitate this type of long-term thinking, helping to bridge the gap between innovation and execution, will be in high demand. Effective channel strategies are no longer defined by the number of resellers or volume of units shipped. They’re defined by the ability to help customers move smarter, not just faster. They’re about reducing complexity, enabling agility and unlocking potential from the massive volumes of data companies already possess. In this environment, the most impactful partnerships are often the least rigid. They’re built on shared goals, not static contracts. They evolve with customers, adapt to new pressures and bring in the right expertise at the right time. That’s the opportunity in front of us – not just to meet demand but to shape what comes next. o Peter Bocquet is senior director, partners & alliances, APAC, Hitachi Vantara. Top Factors for Why AI Projects Have Been Successful Used high-quality data 38% Good project management/ governance 37% Partnerships with AI vendors/experts 37% Skilled AI team 36% Regular AI monitoring/ evaluation 35% Good department collaboration 34% Flexible/agile approach 32% Clear objectives/use cases 30% Continuous training 30% Strong leadership support 30% Robust infrastructure 27% Source: State of Data Infrastructure; Hitachi Ventures Areas IT Leaders Say They Need Help Implementing AI Building AI models / LLMs 32% Training IT staff 31% ROT data storage 28% Data preparation 28% Data processing 27% Secure implementation 27% Sustainable implementation 27% Providing scalable solutions 26% Making data available 25% Training users 25% Deciding use cases 24% Strategic implementation 23% Finding amount of unused data 22% Educating leaders 19% Across the entire process 3% We don’t need help 0% Source: State of Data Infrastructure; Hitachi Ventures 14 CHANNELVISION | WINTER 2026
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AI & AUTOMATION AI users report pain points and challenges; advisors better know them By Martin Vilaboy Avoiding AI’s Disillusionment AI technologies might force a rethinking of some longstanding assumptions about technology maturation and adoption. Even though generative AI technologies have largely started their slide toward the “trough of disillusionment” on the famed Gartner hype cycle, while agentic AI likewise has pushed past its “peak of inflated expectations,” there doesn’t appear to be the expected amount of disillusionment around AI adoption. Indeed, despite research and reports of bubbles, gaps and pilots failing to takeoff toward implementations, surveys suggest C-suites, board rooms and IT decision-makers are full steam ahead when it comes to AI investments. According to a recently released report from Accenture, for example, 86 percent of C-suite leaders plan to increase AI investment in 2026. Nearly the same percentage (78 percent) said they now see AI as more beneficial to revenue growth than cost reduction, up from 65 percent who said the same in June 2024. Even if the proverbial “AI bubble were to burst,” a significant percentage of executives said they would increase their spending on AI. “AI remains the centerpiece of 2026 investment strategies,” said Accenture analysts. In other words, business leadership is not dissuaded by the reality that fewer employees said they regularly work with AI agents, according to Accenture findings, down to 32 percent in January 2026 compared to 42 percent in 2025. Similarly, they are not disillusioned by the increasing number of employees who said they’ve “used or tested AI agents but don’t work with them regularly,” from 36 percent in January of last year to 43 percent this recent January. “The positivity reverberating across the C-suite does not align with what their workforce is experiencing, even though talent is the primary accelerator of AI scale,” Accenture researchers noted. 16 CHANNELVISION | WINTER 2026
Providers and distributors of AI-oriented technologies and solutions, on the other hand, can’t afford to be quite as cavalier about employees’ AI misgivings. While they may seem to have come rather quickly, we are seeing clear pain points among users of AI as well as barriers to achieving intended outcomes. And providers and partners who are either unaware of or avoid acknowledgement of known pain points will have a tough time establishing themselves as trusted advisors. And make mistake, for employees, “the underlying capabilities of AI at work remain fragile as 54 percent cite low-quality or misleading AI outputs leading to wasted time and productivity,” showed Accenture’s data. Similarly, new research from Workday shows that while many organizations are realizing gains from AI, a substantial share of that value is being quietly lost to rework and low-quality output. Roughly 37 percent of the time saved through AI, for example, is being offset by rework, according to the SaaS provider of human capital management and financial management platforms. Only 14 percent of employees consistently achieve net-positive outcomes from AI use, showed the Workday data. Accenture, for its part, found that 13 percent of respondent say they “frequently encounter” misleading and low-quality outputs from their AI use. “Employees report spending significant time correcting, clarifying or rewriting low-quality AI-generated content – essentially creating an AI tax on productivity,” said the Workplace study. “For every 10 hours of efficiency gained through AI, nearly four hours are lost to fixing its output.” Interestingly enough, employers aged 25- to 34-years-old, while often assumed to adapt most easily to new technologies, account for nearly half (46 percent) of employees experiencing the highest levels of verification and correction of AI output, hence emerging as a consistent hot spot for AI-related rework. “These employees tend to use AI frequently and with confidence, but they also report spending significantly more time auditing results – adding a hidden layer of work rather than eliminating it,” said Workplace researchers. “In practice, AI accelerates output, while responsibility for ensuring quality remains squarely with the employee.” A lack of consistent accuracy in outputs seems to be creating trust issues that could prove a barrier to further adoption. According to a survey performed for enterprise agentic automation provider Camunda, 84 percent of process automation professionals are worried about the business risk of AI in day-to-day processes when IT does not have the appropriate controls in place (which is often), while 80 percent are concerned about a lack of transparency into how AI is used. What’s more, half of respondents believe untamed agentic AI risks “fanning the flames of poorly implemented processes and automations.” All told, Camunda found that almost three-quarters (73 percent) of organizations admit there is a gap between their agentic AI vision and the current reality. “The promise of agentic AI is undeniable, but trust remains the key barrier to adoption,” said Kurt Petersen, senior vice president, customer success at Camunda. “Right now, exercising caution with agentic AI means many organizations can’t move beyond pilots or isolated use cases. Without clear guardrails and visibility, agents will stay at the edge of the business.” Camunda’s survey bears this out. Eight in 10 process automation professionals said most of their AI agents are chatbots or assistants that simply summarize or answer questions, instead of handling mission-critical use cases. Neary half (48 percent) said their AI agents operate in silos and are not woven into end-to-end business processes. Possibly even more concerning, 39 percent said they flat out don’t trust delegating critical tasks to AI. Likewise, just 27 percent of executives surveyed by Accenture strongly agree that they are comfortable delegating tasks to them. AI Trust Issues: What’s holding your organization back? Business risk of AI in day-to-day processes when IT does not have appropriate controls in place 84% A lack of transparency around how AI is used within business processes 80% Compliance concerns around the use of AI agents 66% Missing internal skills to manage AI effectively 56% Concerns that AI will make poorly implemented processes worse 50% Questions about using agentic AI where it doesn’t add value 42% Don’t trust delegating critical task to AI 39% Source: Coleman Parkes; Camunda Source: Gartner (October 2025) If an AI bubble were to bust, how would it impact your organization’s Investment strategy? Among C-suite repondents Source: Accenture Which, if any, of the following have you done to improve the outputs of Generative AI tools in your company? Source: ABBYY 9-Step PCI Compliance Checklist Source: SecureTrust by VikingCloud 7% 31% 16% 35% 10% Significantly decrease investments (20% or more) Significantly decrease investments (up to 20%) No changes Somewhat increase investments (up to 20%) Significantly increase investment (20% or more) Training staff to better use AI Encouraged teams to share tips and examples for effective use of AI Used AI agents to improve outputs Use process intelligence to improve analysis Used Document AI/intelligent document processing to improve the outputs Set up regular check-ins to review AI use Considering alternative AI tools Used Retrieval augmented generation (RAG) to improve the outputs Asked staff to manually check and correct the outputs Scaling back or removing AI tools that weren’t working We got rid of all AI tools and stopped using AI completely N/A. We have not done anything in particular to improve the outputs of AI tools 50% 43% 36% 35% 35% 33% 28% 25% 25% 20% 18% 2% Install a firewall and VPN Monitor and track user access requests Encrypt data while at rest and in transit Establish a foolproof update schedule Restrict cardholder data with strong password and multi-factor authentication Physically secure any premises where cardholder data is stored Run penetration tests and vulnerability scans Be clear on security policies and keep personnel up to speed Use up-to-date antivirus and anti-malware software to prevent threats 1. 6. 7. 8. 9. 2. 3. 4. 5. Time 17 WINTER 2026 | CHANNELVISION
And just 17 percent said they enjoy using AI and seek new ways to apply it, down from 21 percent in 2025. Incidentally, Accenture also noted signs of good old-fashioned FUD (fear, uncertainty and doubt). The IT professional services firm found that about half of workers feel secure in their jobs – down 11 percentage points from the 59 percent who said the same as recently as Summer 2025. About six in 10 workers also believe that young professionals are having a harder time finding jobs due to automation and AI. The percentage of workforces that said they “enjoy using AI and seek new ways to apply it” fell from 21 percent last summer to 17 percent this January. Overall, studies suggest AI implementations have focused primarily on lower-level, lower-risk activities such as answering questions, summarizing information and automating repeated tasks. AI agents currently operate at the edge of business processes rather than within core, mission-critical process flows and are heavily dependent on human oversight and approval for important decisions. There’s a growing understanding, however, that in order to justify deeper investment in AI, and achieve its massive potential, it must be better applied to strategic decision making and high value tasks and integrated deeper into business processes. And that will not be friction free. About a third of IT decision makers surveyed for process optimization company ABBYY reported struggles integrating GenAI into business practices, while training GenAI models was harder than expected. One root cause of such struggles is staff not having the skills necessary to train models and integrate data within business-critical business processes, Camunda executives argued. And just because business leaders say their staff is being properly trained, don’t automatically believe it. “While two-thirds of leaders (66 percent) cite skills training as a top investment priority, that investment is not consistently reaching the employees most exposed to rework,” reported the Workday study. “Among employees who use AI the most, only 37 percent report increased access to training – a nearly 30-point gap between stated intent and lived experience.” The issue is compounded by lagging role redesign. “(N)early nine in 10 organizations report that fewer than half of their roles have been updated to include AI-related skills,” Workday researchers continued. “AI has been layered onto roles that were never updated to accommodate it – forcing employees to use 2025 tools within 2015 job structures. Rather than reducing effort, this mismatch often increases it, as employees are left to reconcile faster production with unchanged expectations around accuracy, judgment and accountability.” For employees already doing a large share of rework, “outdated role definitions make it harder to capture AI’s benefits,” the study warned. “Without clear expectations for how AI should be used – and where human judgment must apply – employees default to verification and correction, absorbing the cost of low-quality output themselves.” At the same time, technology stacks are becoming more distributed and the number of endpoints involved in each process is growing, as a full 76 percent of organizations surveyed by Camunda said the volume and diversity of endpoints is increasing exponentially. “As a result, 85 percent say they Which, if any, of the following challenges have you encountered when implementing generative AI tools within your company? Training our Generative AI models was harder than we expected 31% Our staff did not have the necessary skills to deploy it 29% It was difficult to integrate within our business processes 28% We did not have proper governance or AI policies in place 26% Our staff misuse the tools 21% Paying for access was too expensive 19% It was not able to accurately incorporate our company data 19% Our staff ended up spending more time on tasks 18% It used too much energy 16% It experienced hallucinations 16% N/A. We have not encountered any challenges in particular 19% Source: Opinium Research; ABBYY The AI Adoption Gap Source: Gartner (October 2025) If an AI bubble were to bust, how would it impact your organization’s Investment strategy? Among C-suite repondents Source: Accenture Which, if any, of the following have you done to improve the outputs of Generative AI tools in your company? Source: ABBYY 9-Step PCI Compliance Checklist 7% 31% 16% 35% 10% Significantly decrease investments (20% or more) Significantly decrease investments (up to 20%) No changes Somewhat increase investments (up to 20%) Significantly increase investment (20% or more) Training staff to better use AI Encouraged teams to share tips and examples for effective use of AI Used AI agents to improve outputs Use process intelligence to improve analysis Used Document AI/intelligent document processing to improve the outputs Set up regular check-ins to review AI use Considering alternative AI tools Used Retrieval augmented generation (RAG) to improve the outputs Asked staff to manually check and correct the outputs Scaling back or removing AI tools that weren’t working We got rid of all AI tools and stopped using AI completely N/A. We have not done anything in particular to improve the outputs of AI tools 50% 43% 36% 35% 35% 33% 28% 25% 25% 20% 18% 2% AGI Neurosymbolic AI Ambient AI Agentic AI AI Adoption Gap Time AI Innovation Race (Providers) AI Outcome Race (Customers) AI Agents GenAI Traditional AI (ML, NLP) 18 CHANNELVISION | WINTER 2026
need better tools to manage the intersections between processes, highlighting the challenge organizations face in realizing full value from their AI and automation investments,” said Camunda executives. Bridging the gap between AI vision and reality, Camunda executives argued, requires a move beyond standalone, siloed agents toward “agentic orchestration,” which enables teams to blend deterministic and dynamic orchestration of business processes, leveraging agents to add dynamic reasoning to deterministic processes so they can adapt in real time. Apparently, professionals responsible for automation within their companies tend to agree, as 88 percent said AI needs to be orchestrated across business processes if organizations are to get maximum benefit from their AI investments, while a full 90 percent said AI needs to be orchestrated like any other endpoint within automated business processes to ensure compliance with regulations. At the same time, 85 percent also said their organization has not yet reached the right level of process maturity to implement agentic orchestration, suggesting a need to increase process maturity and AI maturity in parallel. “Deterministic orchestration has always established structured guardrails. By blending it with dynamic orchestration patterns to leverage reasoning across AI agents, people and systems in end-toend processes, enterprises can build a foundation for AI agents they truly trust,” said Camunda’s Petersen. “This is enterprise agentic automation in practice, and it is how organizations will turn today’s AI experiments into durable, businesscritical capabilities.” ABBYY, for its part, found that 99 percent of organizations that augmented GenAI deployments with complementary tools, such as process intelligence, Document AI and retrieval augmented generation (RAG), reported improved outcomes, including more consistent results (50 percent), greater accuracy (43 percent), stronger trust (43 percent), and cost savings (42 percent). The failure to address existing pain points, challenges and the gaps between AI hype and reality threatens more than the rate of growth and further investment in AI technologies. In addition to the ding it could make to a partner’s advisor status, there is the very real chance businesses could pull back on AI-oriented programs altogether. After all, Accenture’s recent study found that regular AI agent usage among employees dropped 10 points since the summer, while a soon-to-be released survey from RingCentral found that nearly 40 percent of organizations have paused or cancelled an AI project, mostly because expectations, workflows or training weren’t aligned from the start. The ultimate lesson for technology advisors could be to focus less on accelerating usage and more on improving how AI implementations and outcomes are designed, measured and supported. o AI Infrastructure Pain Points According to Gartner, about half of AI based spending this year will be on infrastructure. Perhaps that’s not so surprising considering that infrastructures are still catching up to new and evolving AI workloads. Add to that fact how network operators don’t seem to have a lot of confidence in the ability of their current AI infrastructures to meet the demands of AI-driven applications in the next few years. A survey from A10 Networks, a provider of security and infrastructure solutions found that 79 percent of organizations were either “somewhat confident” or “not very confident” in their infrastructure’s ability to handle the performance, latency and availability demands of AI-driven applications. Only 13 percent said they were “very confident.” What are the biggest limitations or pain points in your current infrastructure when it comes to supporting AI workloads? Security constraints (current security applications/solutions can’t inspect or protect AI-related traffic effectively) 49% Legacy systems that are inflexible or hard to integrate with new AI platforms 39% Scalability limitations (hard to scale out infrastructure quickly for AI demand) 38% Lack of visibility/monitoring for AI workloads (potential for “blind spots” in traffic or performance metrics) 33% Insufficient computing power (CPU/GPU) for AI processing 30% Network bandwidth or latency bottlenecks for moving large data/AI traffic 29% Data storage or data management bottlenecks for AI (throughput, I/O, etc.) 19% Other 4% Source: A10 Networks Also not so surprising, security constraints emerged as the top challenge, as 49 percent of respondents named security as their biggest limitation for current infrastructure to support AI workloads. Four in 10 already have deployed new AI-specific security solutions into their infrastructures, showed A10’s data. o 19 WINTER 2026 | CHANNELVISION
AI & AUTOMATION 20 CHANNELVISION | WINTER 2026 Despite years of technological advancement in the channel, advisory workflows remain slow, fragmented and largely manual. Advisors today must balance a wide range of responsibilities, often across multiple disconnected tools, leaving less time to focus on relationship-building and business growth. Clarus Communications is transforming the process with Resolve, a new operations platform purpose-built for busy technology advisors. Designed to fit seamlessly into how advisors already work, Resolve acts as a productivity and intelligence layer that enhances preparation, analysis and execution, without replacing existing tools or disrupting established workflows. Advisors continue working within their current ecosystems while gaining an operational advantage that helps them move faster and operate with greater confidence. Resolve enables advisors to engage with more than 25 AI-powered Digital Teammates, each modeled after realworld roles advisors rely on every day. These Digital Teammates function as extensions of an advisor’s team, supporting specific tasks with speed, structure and consistency; simplifying complex tasks and freeing them to focus on high-value work. Digital Teammate examples include a prospect research analyst that produces structured intelligence reports to support business development; a discovery strategy advisor that prepares high-impact, role-specific discovery questions; and a commission analyst that validates supplier payouts and flags discrepancies. Additional Digital Teammates specialize in areas such as sales management, cloud and contact center architecture, installation planning, and long-term account strategy, allowing advisors to automate work that would otherwise require hours of manual effort. Most importantly, advisors remain in the driver’s seat. Digital Teammates can be deployed as needed to support specific workflows, but they never interact directly with customers. The advisor stays at the center of every engagement, using Resolve to prepare better, respond faster and guide customers with greater clarity. “Resolve was designed to add value and deliver expertise where it’s required, not to replace the advisor,” explained Ed Pearce, Clarus managing partner. “Advisors remain the primary point of contact and decision-maker in every engagement.” While many advisors have tried generic AI tools such as ChatGPT, Resolve eliminates the guesswork from using AI. With pre-structured prompts, industry-specific context and guardrails that remove trial-and-error, Resolve ensures consistent, decision-ready output. Advisors do not need to experiment with prompts or determine how to apply the results. The thinking has already been done for them, based on how top-performing advisors research, sell and manage accounts. “I can use ChatGPT, but Resolve is different,” said one advisor using the platform. “It’s already tuned to how I work. I’m walking into conversations better prepared than competitors who are still doing this manually.” Resolve was intentionally developed to be TSD-agnostic and to operate independently of any single TSD or supplier platform. Advisors can continue using the tools and relationships they rely on today, while Resolve enhances execution across the full advisory lifecycle, from initial outreach through long-term account management. While Resolve is new to the channel, its impact will be immediate. As Bain & Company recently noted on AI, “sellers may spend only about 25 percent of their time actually selling to customers.” AI has the potential to significantly increase that percentage by absorbing much of the work that surrounds selling but doesn’t add much value, leaving more time for customer service. “Resolve is a giant leap beyond generic AI tools,” Pearce added. “It offers the ease and familiarity of ChatGPT, but it’s loaded with exclusive insights and industry best practices, which deliver a true competitive advantage.” Resolve fundamentally changes how advisors engage customers providing a new way to research accounts, recommend services and guide them through increasingly complex technology decisions. By implementing intelligence rather than simply automating tasks, Resolve gives advisors a durable competitive edge in an increasingly complex market.o Scan the QR code to explore the full portfolio of Digital Teammates and see how Resolve helps advisors work smarter. Partner Smarter By Gerald Baldino Clarus Resolve gives advisors a competitive edge
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