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CONTENTS Martin Vilaboy Editor-in-Chief martin@bekabusinessmedia.com Brady Hicks Contributing Editor brady@bekabusinessmedia.com Gerald Baldino Contributing Editor gerald@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 Zach Zohdy Senior Sales Manager zach@bekabusinessmedia.com (310) 730-8018 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 ADVERTISER INDEX AireSpring (www.airespring.com) 11 Astound Business Solutions (astoundpartners.com) 7 Azuga (www.azuga.com) 17 C3 Complete (www.c3cloud.com) 31 CELLSMART (www.cellsmart.io) 13 CVxEXPO25 (cvxexpo.com) 27 EnTelegent Solutions (www.entelegent.com) 2 Granite Telecommunications (www.granitenet.com) 9 NHC (nhcgrp.com) Back cover QuestBlue (questblue.com) 3 Ritalia Funding (www.ritaliafunding.com) 15 Telesystem (www.telesystem.com) 5 ZeroOutages (www.zerooutages.com) 25 Disclaimer: This index is provided as a free service to our advertisers. Every effort is made for accuracy, but we cannot be held liable for any errors or omissions. THE CHANNEL MANAGER’S PLAYBOOK 6 AI Bubble and Bursting Optimism 8 Enhanced UCaaS 10 Data Realities Drag on AI By Martin Vilaboy 14 Collaborating to Control Shadow AI By Gerald Baldino 20 Partnering Smarter AI officially enters PRM and the sub-agent experience By Martin Vilaboy 22 Navigating AI Integration Strategic steps for successful Copilot deployments By Richard Acreman 24 AI Course Correct Cutting through the noise and getting to outcomes is key with AI By Gerald Baldino Volume 20: AI & Automation Trends © 2025 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 4
Experts and commentators are predicting the bubble around AI could burst in 2025, as obstacles impede the continuation of hype, particularly in the case of generative AI. After years of attracting enormous investments and stirrup up huge expectations, companies and investors will have to lower their expectations regarding what Gen AI and large language models (LLMs) can do, warned Adi Andrei, co-founder of Technosophics, and research fellow at University College London (UCL) Ali Chaudhry, both of whom are members of Oxylabs’ AI/ML Advisory Board; along with the company’s CEO Julius Černiauskas. According to Chaudhry, in some cases, technology is not as scalable as expected. “I think we will see diminishing returns in the capabilities of LLMs. Some AI labs are already hinting that scaling laws are not as effective anymore,” he said. Chaudhry names increasing regulation and general concerns regarding the dangers of AI as factors that will play into taming the Gen AI enthusiasm: “Generally, 2025 will be very important for AI safety, and we will see a lot of work (technical and non-technical) in this space.” Similarly, “responsible AI” and “green AI” will become bigger topics next year, Černiauskas concurred. “Servers that support AI development put a strain on the environment, and there are many risks flowing from a lack of transparency in how AI is developed and functions. AI companies must answer these concerns to earn and keep public trust in the benefits of this technology,” he continued. Andrei, who has more than 25 years of experience in AI/ML and data science, including at NASA, is perhaps the most skeptical of the three. “The Gen AI bubble will most likely burst in 2025,” he predicted. “I was talking about the hype going away last year, but the new influx of money pumped into it without any proof of ROI transformed the dying hype into a bubble about to burst big time.” Andrei points to a diverse and growing list of writers, artists, computer scientists, engineers and philosophers who found common ground against the Gen AI paradigm. “This has raised awareness within the general population of the irreconcilable issues posed by technology and the fact that it is being forced onto people by billionaires and their organizations,” concluded Andrei. Even so, the experts foresee some positive developments coming next year along with some interesting technologies. For Andrei, progress in decentralization technologies, rather than AI, will be fascinating to follow. “2025 will be a year of paradigm changes in the social system, and we will see the emergence of technologies – including information technologies – to facilitate a decentralized way of life and of building community, most likely decentralized social networks, local currencies, and so on.” Chaudhry foresees more AI contributions for scientific discovery. “I think 2025 will be a big year for multi-modal models, especially textto-video models, which will improve significantly in terms of the length of videos, their quality and their obedience to the laws of physics,” said Chaudhry. “We might get some interesting insights into the inner workings of deep neural networks in foundational models.” Černiauskas is hopeful for advancements in automated machine learning (AutoML), which opens this field for those without specialized ML expertise by delegating ML development to algorithms. “AutoML is a crucial step toward democratizing machine learning and AI. With more experts in diverse fields being able to create ML tools tailored to their needs, AI and ML adoption in business can accelerate and open new possibilities,” said Černiauskas. A new study by data collection experts SOAX has analyzed and determined the most popular AI tools for businesses, based on a range of user metrics spanning various categories that were tagged as “business” on AI tools directory, aixploria.com. After categorizing a list of websites that offer AI tools, SOAX gathered five different metrics to create its overall score. Here’s how the top 10 tool categories ranked in terms of use by business: AI Bubbles and Bursting Optimism The Most Popular AI Tools Transforming Businesses #1 Developer Tools #6 Education & Studies #2 Websites & Design #7 Presentation #3 Productivity #8 Transcriber #4 Automation #9 Data & Analytics #5 Chatbots #10 Sales & Conversion Source: SOAX 6 THE CHANNEL MANAGER’S PLAYBOOK
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Telesystem announced the launch of TrustUC, an enhanced UCaaS solution offering built-in security awareness training, Webex meetings and collaboration plus Telesystem’s AI-powered business messaging solution with every seat. TrustUC offers users a seamless communications experience on any device, integrating the Webex platform with new AI-driven functionalities that optimize employee engagement and productivity. Key features, including AI-Powered Meeting Summaries and customizable conversational AI Agents, enable businesses to boost operational efficiencies and improve customer experiences, said the company. “TrustUC is Telesystem’s answer to businesses who are frustrated by the multitude of applications to try and satisfy their need for end user security, voice, collaboration and messaging in a simplistic format,” said Bruce Wirt, chief revenue officer at Telesystem. “By combining these solutions, users will find an easy-to-use solution that gives them all the tools needed to run their communications from a single source.” Telesystem has embedded its employee Security Awareness Training solution directly into the TrustUC platform, empowering organizations to educate their employees on current cyber threats, while also integrating access to 24/7 dark web monitoring, micro-trainings and unlimited phishing simulations. Additionally, all TrustUC seats are integrated with the free tier of Telesystem’s new Business Messaging solution, enabling businesses to communicate securely and efficiently across multiple channels, including SMS, MMS and iMessage within the TrustUC application. The TrustUC Business Messaging solution offers a number of key features, including bulk messaging and automated responses for common inquiries, appointment reminders and confirmations. Its embedded AI capabilities, meanwhile, can automatically analyze messaging patterns and customer responses, helping refine and automate communication strategies; predict customer needs; and provide smarter, contextaware replies that will significantly improve customer engagement. Users can access the app from any device, giving them the freedom to work how and where they want, while enjoying a more secure, engaging and fully integrated communications experience. The benefits of artificial intelligence are fairly well understood: the automation of routine tasks and processes in order to save time and money, deeper dives into data and thinking in ways that were untenable without high-powered intelligence. Realizing those benefits of AI, however, will require overcoming a diverse set of challenges, suggests CompTIA survey data. And those expected challenges include “identifying uses cases,” which ranked as high as third on the list of expected challenges with AI. Telesystem Launches Enhanced UCaaS Solution, TrustUC The Challenge of AI Challenges AI’s Diverse Challenges Source: CompTIA, 2025 Cybersecurity/privacy concerns Expected/experienced challenges Expected/experienced benefits Time savings on routine tasks Automating IT operations Deeper data analysis Automating business workflows Ability to redeploy employees New insights/suggestions Cost of new applications Identifying use cases Balancing AI and human efforts Cost of infrastructure Building datasets for training Insufficient technical skill Lack of understanding on AI output Difficulty changing workflows 40% 35% 33% 33% 32% 28% 26% 26% 25% 32% 33% 44% 45% 49% 51% 8 THE CHANNEL MANAGER’S PLAYBOOK
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Data Realities Drag on AI As organizations aggressively invest to apply AI to an expanding set of objectives, “a kink is emerging in organizations’ project pipelines,” suggest new findings from S&P Global. While more initiatives are funneled toward AI project teams, S&P Global analysts noted a buildup of initiatives that have been only partially deployed. On average, organizations surveyed have more projects classified as being in production with a limited deployment than ones with scaled-up capabilities. In the average organization, shows S&P Global data, 51 percent of AI projects are in production but not being delivered at scale. The crux of the problem, said the research firm, appears to be data quality and availability, with legacy data architectures causing this pipeline stoppage in many organizations. All the while, the constant chasing of new initiatives means many organizations fail to maximize the value of their existing investments, the research firm warned. “AI projects risk stalling in a limited deployment purgatory, costing a company money, time and resources, while not seeing desired levels of use,” said the S&P Global study, commissioned by AI company Weka. “Initiatives are becoming snagged on data siloes, poor data quality and ineffective data and model pipelines.” Data quality is the most frequently identified challenge as organizations move their projects from pilots to production, identified by 42 percent of organizations as among their top three barriers. That placed data quality as an even more significant issue than skill shortages (32 percent) and budget limitations (31 percent). Organizations in media and By Martin Vilaboy 10 THE CHANNEL MANAGER’S PLAYBOOK
entertainment (59 percent), higher education (53 percent), and aerospace and defense (48 percent) feel the data quality challenge particularly keenly. According to S&P Global analysts, the data quality challenge does not stem from a lack of data to build performant models. Rather, at issue is data not being set up in such a way that project teams can take full advantage of it. When asked specifically to rank the primary data challenges to move projects to production environments, respondents indicated that availability of quality data is a more notable impediment than identifying relevant data. “With 34 percent of organizations perceiving availability of quality data as a top three data challenge, outranked only by data privacy concerns (35 percent), it is clear that many organizations are poorly set up for effective data management,” said the research firm. Data management and storage are most commonly seen as the infrastructure components that inhibit AI application development. More than a third (35 percent) of respondents see them as a more serious issue than security (23 percent), compute (26 percent) and networking resources (15 percent). What’s more, organizations that are most effectively scaling AI initiatives are less constrained by these data management and storage components. Whereas 28 percent of respondents who reported that AI is widely implemented within their organization perceive storage and data management challenges as their greatest inhibitor; these respondents feel greater pressure from networking or compute resources. This compares to 42 percent of respondents who perceive AI as being limited to a few use cases or projects within their organization. “Organizations that are delivering AI at scale appear to have focused on investing in upgrading the systems and technologies used to store or manage data,” said Weka executives. More than three-quarters (80 percent) of respondents expect an increase during the next 12 months in the volume of data they use to develop their AI models, with nearly half (49 percent) forecasting growth in data volumes of more than 25 percent. All the while, the proportion of organizations using unstructured rich media and text data for AI initiatives has increased notably since 2023. In other words, immature data management toolsets are a worrying backdrop for the increasingly datahungry AI strategies many organizations are embarking upon, the study noted, and outdated data management technologies may prevent organizations from delivering these projects meaningfully. “Leaders are significantly less likely to see storage and data management as their primary inhibitors, presumably because these companies have already prioritized modernizing their data architectures.,” Weka executives concluded. “By building a solid data foundation at the outset, AI leaders have ensured that valuable pilots have a clear path to deliver at scale.” o Top three impediments to organizations moving an AI/ML application from pilot to production environments Source: Uptime Institute; 2022 Source: S&P Global; Weka Organizations find the early data steps of the AI life cycles as challenging as Source: S&P Global; Weka 18% 13% 12% 12% 11% 10% 10% 10% 11% 12% 9% 10% 8% 10% 10% 8% 9% 10% 8% 8% 9% 6% 7% 9% 6% 8% 7% 6% 7% 7% 6% 8% 6% Proportion of respondents that identify AI life cycle stage as “most challenging” Data pre-processing Model build an Criteria Used in Selecting Third-Party Firms for Cybersecurity Source: CompTIA Data quality Rank 1 Rank 2 Rank 3 Skills shortages Availability of AI accelerators Budget limitations Compliance/regulatory requirements Legacy infrastructure cannot support AI/ML applications Employee/internal resistance Illustrating the business case for further investments Potential reputational damage Insufficient vendor tooling Customer resitance 59% 41% Gathering/ sourcing data Preparing data Standardizing data Analyzing data Training a model Testi a mo Access to threat intelligence Specific knowledge in a focused area of cybersecurity Broad knowledge across multiple domains of cybersecurity Clear remadiation policies in event of cybersecurity incident Excellence in core offering where security may be embedded Ability to perform cost/benefit analysis of initiatives Offer cybersecurity insurance 44% 43% 43% 41% 39% 38% 33% Top three impediments to organizations moving an AI/ML application from pilot to production environments Source: Uptime Institute; 2022 Source: S&P Global; Weka Organizations find the early data steps of the AI life cycles as challenging as model building Source: S&P Global; Weka 18% 13% 12% 12% 11% 10% 10% 10% 11% 12% 9% 10% 8% 10% 10% 8% 9% 10% 8% 8% 9% 6% 7% 9% 6% 8% 7% 6% 7% 7% 6% 8% 6% Proportion of respondents that identify AI life cycle stage as “most challenging” Data pre-processing Model build and deployment Data quality Rank 1 Rank 2 Rank 3 Skills shortages Availability of AI accelerators Budget limitations Compliance/regulatory requirements Legacy infrastructure cannot support AI/ML applications Employee/internal resistance Illustrating the business case for further investments Potential reputational damage Insufficient vendor tooling Customer resitance 59% 41% Gathering/ sourcing data Preparing data Standardizing data Analyzing data Training a model Testing a model Deploying a model 12 THE CHANNEL MANAGER’S PLAYBOOK
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Collaborating to Control Shadow AI Companies across the board are struggling with shadow IT, or unsanctioned hardware and software. On one hand, employees need fast and easy access to productivity apps and cloud services to complete their work. Yet companies often have rigid IT policies in place restricting their usage – putting workers at a disadvantage, while doing little to protect against security threats. With shadow IT increasingly looking like an unwinnable war, IT leaders are gradually shifting away from traditional “command and control” management in favor of flexible, worker-centric policies that aim to boost productivity and improve collaboration. This shift is creating an abundance of opportunities for partners to expand their advisory roles, engage with different business units and create additional revenue streams. Let’s explore how shadow IT negatively impacts organizations, and why technology advisors should advocate for stronger partnerships between IT and business units along with greater autonomy and collaboration. By Gerald Baldino In my opinion, employees most commonly use applications not officially sanctioned by IT … To make their jobs easier 54% To be more productive 51% To test out new technology 45% To gain functionality that authorized tools don’t offer 43% Because the procurement process for new apps takes too long 36% Because the existing process for adding apps are onerous 32% Source: JumpCloud 14 THE CHANNEL MANAGER’S PLAYBOOK
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How Shadow IT Harms Organizations IT needs real-time visibility into end-user behavior to protect against threats such as data leakage, malware infections and account takeovers. When shadow IT takes place at scale, it introduces significant risk. Shadow IT is to blame for expanding attack surfaces and costly security incidents. In a recent Red Canary report, 73 percent of security leaders said their attack surface has widened by an average of 77 percent in recent years. In addition, 87 percent of respondents said they had a security incident in the previous 12 months that they were unable to detect and neutralize before it caused a negative impact. At the same time, shadow IT is highly resource intensive. Companies often allocate a sizable portion of their technology budgets to shadow IT in the form of duplicate accounts and unnecessary licenses. IT workers also typically spend a significant amount of time detecting and enforcing shadow IT, which creates time waste and pulls them away from higher-value tasks. And shadow IT is accelerating, with Zendesk reporting that shadow AI usage has increased by as much as 250 percent year over year in some industries. Gartner predicts that 75 percent of employees will acquire, modify or create technology outside of IT’s visibility by 2027. The issue is also becoming more complex, with workers now using a variety of AI-driven tools to streamline workflows and boost productivity – often with little regard for how they impact security. According to Microsoft, 78 percent of AI users are bringing their own AI tools to work, in a trend known as bring your own AI (BYOAI). Meanwhile, 52 percent of people who use AI at work are reluctant to admit using it for their most important tasks. Looking ahead, shadow AI is bound to get worse as we transition into the agentic AI era, or the “third wave of AI,” and a new class of productivity tools become available to workers. It’s important to keep in mind that shadow IT is a policy-related issue. Today there are a wide range of solutions for mitigating shadow IT, including endpoint detection, security information and event management (SIEM) and data loss prevention (DLP), among others. But while these services can help detect unsanctioned technology usage and protect assets, they all fail to address the underlying issue: When IT fails to meet workers’ needs, employees will likely go rogue and acquire tools on their own. This is the root cause of shadow IT, and it will always persist regardless of what technology you throw at it. Partnering for Success Faced with the realization that shadow IT isn’t going away, a growing number of technology leaders are abandoning command and control management. Instead, they are partnering with business units to understand their technology needs more closely and improve visibility and collaboration. This shift aligns with a broader business trend that has been picking up steam in recent years. O.C. Tanner found that command and control leadership practices contribute to a 43 percent decrease in overall employee experience, as well as a 42 percent decrease in workers’ sense of opportunity. Companies with traditional command and control leadership are also 84 percent less likely to increase revenue. Attempting to place an outright ban on shadow IT wastes resources and positions IT as a barrier to success instead of a trusted ally. Leaders that The Differences Between Shadow AI and Shadow IT Shadow AI Shadow IT Definition Use of AI tools and technology without IT or data governance team approval Use of unapproved IT software, hardware, or infrastructure on an enterprise network Adoption Adopted by individual employees seeking to improve productivity and tool convenience Adopted by employees or teams to address IT challenges in real time Government and Compliance Lacks IT or data team oversight and control Lacks larger IT or organization oversight Risks • Data privacy • AI model biases • Compliance violations • Lack of transparency • Data breaches • Regulatory non-compliance • Network security threats Cultural Impact Encourages innovation but risks inconsistency in data usage and decision-making Promotes agility but risks a fragmented IT environment and reinforced silos Example Customer service team uses an unapproved AI tool to analyze customer sentiment Employee uses an unapproved storage service to store and share work files Source: Zendesk Workers Using AI Tools Not Provided by Organization Generation Z 85% Millennials 78% Generation X 76% Baby Boomers 73% Source: Microsoft; LinkedIn 16 THE CHANNEL MANAGER’S PLAYBOOK
continue this approach risk hampering their workforce, slowing productivity and falling behind savvier competitors that are using emerging chatbots, automation services and analytics tools. As Compoze Labs partner and data lead Jeff Rogers pointed out, banning shadow IT is especially dangerous for CIOs who are fighting for influence in the C-suite, and whose jobs are under the microscope from other executives and line of business (LOB) leaders. Instead, Rogers advocates IT leaders “be a cheerleader for the organization, as opposed to the mean coach telling users they have to use certain tools. “What happens now is that people do shadow IT, and they’re also viewing the IT department as useless,” explained Rogers. “I would much rather have them be doing shadow IT that I am embracing, by partnering with LOBs and enabling them to do their jobs. This helps the organization, even if it means giving up direct responsibility or management aspects.” As it turns out, this trend is catching on – particularly around AI. In Foundry’s latest “State of the CIO” report, 68 percent of North American CIOs said they are working more closely with LOBs on AI applications, while 61 percent said they agree that LOB is pushing the adoption of AI-enabled products and solutions. Establishing collaborative partnerships with LOBs empowers IT and technology advisors to assume more strategic roles by guiding technology adoption, ensuring compliance with emerging regulations such as the EU’s new AI Act and driving crossfunctional collaboration. “From my perspective, the strategic advisory aspect of the business is really where this is going to become valuable,” Rogers continued. “Advisors can use it to help build bridges so that they become more helpful to the organization. The side benefit is that you connect with the actual decision makers and the people that have the money, which is the LOBs. Now you are a connection to them and can maybe provide managed services. And from an advisory perspective, you will become much more embedded in the organization as a trusted, strategic partner.” Preparing for Change It’s important to tread carefully when suggesting IT leaders alter their approach to shadow IT – keeping in mind that departments with established Why are you unable to address shadow IT? Q3 2024 Q1 2025 Our business moves too fast to keep up with current needs 31% 39% Lack of visibility into all apps employee use 32% 38% We have other more important priorities 36% 35% Lack of partnership and communication with our business partners 29% 29% No SaaS/asset management solution in place 24% 28% Source: JumpCloud Managing The Risks of Shadow IT Source: Wallarm 18 THE CHANNEL MANAGER’S PLAYBOOK
policies may be resistant to change due to security or cultural concerns. Considering this, the first step is to connect with technology and security leaders to understand their environments, discuss pain points and implement a modern security and governance framework to combat shadow IT. After all, granting autonomy into technology management shouldn’t imply throwing in the towel or ignoring the issue. In fact, more visibility and communication will be necessary to make the strategy work. API security provider Wallarm recommends using a three-phase policy, which centers around discovering and identifying shadow IT, evaluating risks and usage and establishing continuous monitoring. Technology advisors also can grease the wheel by explaining how this shift benefits IT and security leaders. As Gartner stated in a post, “the CISO and purview of responsibility is shifting from being control owners to risk decision facilitators.” Spending less time fighting shadow IT will free more resources to focus on critical initiatives such as driving business transformation and implementing new ways of working – both of which are sorely needed. Of course, obtaining buy-in from IT is only half the battle. The next step involves building bridges with different business units and teams. This requires taking the time to understand how individuals and departments interact with technology and creating an environment where business users feel they can be honest about reporting the tools they are using daily. To get the ball rolling, technology leaders and advisors can set up pilot tests and communication channels with LOBs to discuss different technologies, observe how employees work and answer questions about IT oversight and monitoring technologies. As Rogers explained, this can be an effective way to evolve beyond command and control management and create trust. “Getting anything deployed in a big organization can take months or years of compliance checks, security ticketing and so on,” Rogers said. “I had a large company that took a different approach by creating committees with partnerships between IT and LOBs. They set up show and tells and knowledge sharing – things that were positive and drove alignment around the organization but were geared toward active participation.” At the end of the day, technology advisors have an opportunity to connect IT and business units and help organizations evolve their workflows for the AI era. By knocking down silos and starting conversations with LOBs, advisors can help transform shadow AI from a pain point into a strength, while building relationships and diversifying revenue streams. o For more info contact us | berge@bekabusinessmedia.com 480-503-0770 Talking Stick Resort & Casino, Scottsdale. Ariz. Nov 3-5th 2025 SAVE THE DATE! *Not including exhibitors Scan to Register 19 THE CHANNEL MANAGER’S PLAYBOOK
As is happening in other software categories, AI is expected to have significant impacts in 2024 within the partner relationships management (PRM) space, amplifying its efforts to automate partner management processes and deliver a more efficient, effective and personalized partner experience. Proponents see the technology evolving beyond its current use of automating tasks into playing a pivotal role in re-shaping how companies interact and manage their channels. Consider, for example, check-ins and updates that can help boost revenue without a human touch, said Kris Blackmon, head of channel communities at Zift Solutions and member of the CompTIA SaaS Ecosystem Advisory Council. “Channel chiefs will use AI to tier partners based on alignment, scope and reach and combine it with automation to personalize to-partner communications while also empowering partners to personalize vendor content for more effective outreach to prospects.” Already, we’re seeing such activity within the communications and business IT channels. Following in the footsteps of technology solutions distributors (TSDs) that have made efforts to automate and streamline their partner experiences, both Clarus Communications and AppDirect have announced AI-enhanced partner tools, both of which are expected to ramp up in the second quarter of this year. Clarus Communications, for its part, took a leap forward in the way its partners obtain quotes for telecom, internet and fiber services with the introduction of an AI-powered assistant named Charlie. All too often, explained executives at the TSD, agents spend significant time navigating various platforms and carrier systems in order to get accurate quotes for their clients. With Charlie, this oncecumbersome process is simplified to a few clicks, providing the ability to instantly access quotes for business technology and telecom services at any location across the country. “Whether a partner is working with a client in bustling New York City or a remote town in Wyoming, Charlie can quickly retrieve quotes tailored to the specific location’s telecom infrastructure,” said the company. Soon gone are the days of waiting for carrier representatives to send over pricing details. Charlie connects directly to carrier databases and provides real-time pricing information, ensuring partners always have quick access to the most up-to-date rates and plans. In addition to quotes, Charlie connects partners to order tracking, commission rates, incentive information, serviceability checks and more. It also offers personalized recommendations based By Martin Vilaboy AI officially enters PRM and the sub-agent experience Partnering Smarter 20 THE CHANNEL MANAGER’S PLAYBOOK
on the client’s needs and preferences. “Partners can rely on Charlie to suggest the best telecom solution for their clients, enhancing customer satisfaction and loyalty,” said the company. Utilizing an intuitive user-friendly interface and intuitive design, Charlie was created to make it easy for even the least tech-savvy professionals to navigate the system effortlessly. And it can integrate with existing platforms and systems so Clarus partners can continue to use their preferred tools while benefiting from Charlie’s quoteretrieval capabilities. TSD and SaaS marketplace AppDirect, meanwhile, is busy working to develop its AI Marketplace solution, which allows partners to easily create no-code, personalized AI bots that operate off data from AppDirect’s supplier marketplace and internal sales enablement data, as well as a partner’s own chosen proprietary data. In cases where partners do input data, that data would reside on a “vector database” that AppDirect hosts and manages, as opposed to the trusted advisor sending its data directly to OpenAI, explained Peush Patel, AppDirect’s vice president of product management, in a blog post surrounding the solution’s introduction. “AppDirect’s AI Marketplace is designed to empower users to easily transform AI bot ideas into reality without needing any coding skills, while also offering a secure platform for sharing and effectively monetizing bots in various marketplaces,” said the company. Partners can build AI bots based on their choice of popular large language models, such as ChatGPT, Llama, Bard or Cohere. By providing specific training data, including files, websites and drives, users can program AI bots to focus on the work they need to accomplish. Technology advisors even could train their AI bot to deliver new business insights for upsell opportunities, said AppDirect. The resultant bots can be private, available to an organization or eventually shared and monetized on the Marketplace. According to Patel, the idea for AI Marketplace started as a proprietary application AppDirect was building as a customer service and sales bot. This “painfully excruciating” experience left his team thinking about the issues AppDirect channel partners would face building their own internal chatbots, Patel said. “We realized that if this was so painful for us, it’s going to be so painful for everybody else in the market. We have a 250-person engineering team today,” he continued. “So, what about small companies with 15, 20, 100 employees? They don’t have the technical skills. They don’t have all the resources at their disposal. So, they need a no-code solution.” The bots eventually will pull information on actual orders and payments, which would be of particular interest to technology advisors that frequently express the desire for increased visibility and automation from their distributor’s digital platforms when it comes to the status of their orders, said Patel. Initially, a select number of technology advisor firms are testing out the chatbots. After collecting feedback in Q1, AppDirect will make its AI Marketplace available to more partners, said Patel. Those partners’ end user customers who possess an AppDirect account will eventually get access, as well. Elsewhere in the channel, PRM and through-channel marketing automation platform provider Zift Solutions recently unveiled its ZiftONE AI Assistant, what the company calls the industry’s first generative AI solution designed to assist partners with content discovery and guidance for an enhanced and more efficient partner portal experience. The ZiftONE AI Assistant addresses the industry-wide struggle of resource constraints, said the company. By leveraging AI to create a virtual channel account manager, it assists partners in discovering the right content for their needs and guides them on the next best action for the task they are undertaking. Through intelligent task automation, constructive conversation and content automation, the platform enables Zift customers to accomplish more in less time with fewer resources. ZiftONE AI Assistant will become available in March, as a new feature on the ZiftONE platform, an all-in-one solution for managing partner marketing activities, onboarding, training and performance tracking all in one place. There’s no doubt, effective channel management can be a time and laborintensive process, particularly for solution providers and distributors that must simultaneously support hundreds, if not thousands, of partners and sub-agents with marketing, onboarding, training, lead generation and dealclosing support. Too often this is done through highly manual, home-grown or resource-intensive tools and processes. So it’s no surprise partner management and enablement are among the early software categories to leverage the power and efficiency provided by AI capabilities. Indeed, the above examples are likely just the beginning of a much bigger shift in PRM and channel management. It appears inflationary pressures are going to continue to press through 2024, and buyers – after two years of seemingly absorbing higher prices – are increasingly pushing back on higher input costs being pushed their way. In turn, vendors and distributors are being forced to do more with less, and AI can suddenly turn what was once impossible or highly impractical – across a vast array of customers, partners and products – into an almost ordinary, if not mundane, task. o AppDirect AI Marketplace demo sign-in screen 21 THE CHANNEL MANAGER’S PLAYBOOK
Navigating AI Integration By Richard Acreman Strategic steps for successful Copilot deployments As businesses increasingly integrate AI tools into their operations, Copilot for Microsoft 365 stands as a significant milestone in enterprise technology. However, its deployment in a large, global organization presents unique challenges. As part of the early adoption program for Copilot, our company Reply (www. reply.com/en) had the opportunity to experience this first-hand and, as a result, I look to share what I consider some essential steps for a successful rollout, focusing on technical readiness, user adoption, data governance and future extensibility. Before jumping into our experience, it’s important to understand exactly what Microsoft Copilot for 365 is and where it differs from other AI tools such as, for example, ChatGPT. While both are advanced AI tools, they serve different purposes and cater to distinct needs. Copilot for Microsoft 365 is a sophisticated productivity enhancer that integrates AI capabilities across various Microsoft applications – functioning similarly to ChatGPT but with a specialized focus on utilizing company-specific data. For example, Copilot is adept at creating tangible outputs such as documents, presentations and meeting notes, and is versatile enough to adapt to different applications, including Excel and PowerPoint. ChatGPT, on the other hand, is a more generalized conversational AI capable of a wide range of text generation tasks such as dialogues, creative writing and coding. So why would a company want to use Copilot? Perhaps the most obvious answer is that it represents an excellent tool for improved productivity – it is great at tasks such as drafting emails, creating presentations and analyzing data, leveraging the specific context and data of an organization’s Microsoft 365 environment to provide customized support for officerelated work. Understanding what Copilot can do for an organization is only a precursor and before getting started, companies should be sure they are fully prepared across these key areas. Technical Readiness: The Foundation Deploying Copilot for Microsoft 365 requires a solid technical foundation. At a minimum, prerequisites include: • Applications including Excel, Word and PowerPoint must be deployed organization-wide. • Essential services such as Entra ID and OneDrive must be enabled for all users. • Users should be on the current or monthly enterprise channel for M365 apps. • Compliance with M365 connectivity principles is crucial for optimal functionality. Data Governance: Securing the Core Incorporating Copilot for Microsoft 365 opens exciting opportunities for secure, compliant and responsible AI usage. Through robust data governance using SharePoint Advanced Management and Microsoft Purview, organizations can confidently protect sensitive data, ensure adherence to regulatory standards, and uphold data integrity. This proactive framework not only shields against unauthorized access and data breaches but also enhances the effectiveness of AI tools. They deliver precise, tailored and compliant services, thanks to features such as sensitivity labeling and data loss prevention. Embracing these governance tools is a powerful step in leveraging AI’s potential safely, making data governance a cornerstone of a successful and forwardthinking AI strategy with Copilot for Microsoft 365. 22 THE CHANNEL MANAGER’S PLAYBOOK
Licensing and Maximizing Value with Copilot Copilot for Microsoft 365 is not just an expenditure but an investment in enhancing organizational efficiency. A strategic approach to licensing ensures your customers get the most out of their investment. Tailoring the rollout to different user groups ensures that each segment derives maximum benefit from Copilot’s capabilities. This targeted deployment strategy should align with an organization’s business objectives, pinpointing areas where Copilot can significantly boost productivity. Establishing clear, measurable criteria for ROI assessment ensures that Copilot’s adoption directly contributes to business goals, making it a cost-effective and valueadding solution. Beyond technical readiness and data governance, the focus shifts to adoption and impact measurement. Engaging users through structured communication, tailored training and effective change management strategies is crucial for maximizing Copilot’s utility. Regularly evaluating its effectiveness helps in making informed decisions and strengthens the business case for Copilot. Planning for future integration with external systems and continuous enhancement of organizational processes ensures that Copilot remains a vital tool for long-term business success. Adoption Strategy: Fostering Enthusiasm and Engagement Successful adoption of Copilot for Microsoft 365 starts with cultivating a group of early adopters and champions who are passionate about its benefits. This group plays a pivotal role in driving wider engagement by showcasing the practical advantages of Copilot and sharing their positive experiences. Tailored communication, personalized training and a robust change management plan are key elements in building this momentum. Engaging these advocates not only accelerates adoption but also fosters a supportive environment where they can mentor and assist others in understanding Copilot’s functionality. Their hands-on involvement can significantly ease the transition for the rest of the workforce. It’s natural to encounter some resistance when introducing new technology. Acknowledging and addressing concerns is crucial for a smooth adoption process. By managing hesitations and aligning expectations with Copilot’s capabilities, users become more receptive to the change. This strategic approach ensures a well-supported transition, leading to enhanced productivity and a more empowered workforce across the organization. Measuring Success and Building a Business Case In our experience, and as we have worked with other organizations to implement Copilot, we’ve found that it is imperative to measure success at regular intervals in order to build a business case for its broader deployment. This can be accomplished through a combination of regular surveys, AB testing, and industry benchmark comparisons. Using these methods provided insights into the tool’s impact and effectiveness within specific business units. Planning for the future expansion of Copilot for Microsoft 365 across an organization involves enhanced integration with external systems. This process includes developing specific plugins to perform unique tasks and assimilating data from various business systems, to enrich Copilot’s functionality. Such integrations aim to provide a more seamless and efficient user experience, making Copilot not just a tool for the present but a scalable solution that can adapt to an organization’s future needs and technological advancements. The deployment of Copilot for Microsoft 365 in large organizations requires a detailed strategic approach that encompasses technical readiness, establishing robust data governance, fostering user adoption, measuring the tool’s impact, and more. My hope is that by methodically addressing these key areas, your customers will be able to significantly elevate productivity and improve the decision-making processes with Copilot. o Richard Acreman, managing partner at Reply, leads a team focused on leveraging Microsoft Cloud Solutions to transform businesses into intelligent enterprises. With a commitment to strategic outcomes and user-centric design, his team utilizes Microsoft technologies to rapidly and securely reshape business operations. Top 4 Concerns Around Implementing Generative AI Quality and Control (e.g., the risk of losing control over the content, misinformation and/or deep fakes) 51% Safety and security risks (e.g., cyberattacks and data breaches) 49% Limiting human innovation (e.g., workforce relying too heavily on generative AI technologies) 39% Human error (e.g., lack of understanding how to use the tool and accidental human-driven data breaches) 38% Source: Insight Primary Drivers for Adopting Generative AI Employee productivity 72% Engaging customers (e.g. chatbot) 66% Research & development 53% Automate software development 50% Personalized CX 44% Marketing and creative work 44% Automate human workflows 44% Market and business insights 44% Insight discovery from data 42% Supply chain management 41% Inventory management 40% Source: Insight 23 THE CHANNEL MANAGER’S PLAYBOOK
The artificial intelligence market is starting to resemble California around the height of the gold rush, when surface gold started disappearing, forcing miners to dig deeper into the earth to find it. As business leaders become savvier about AI and intelligent technologies lose their initial marketing appeal, surface-level sales opportunities driven mostly by hype will become increasingly scarce. Warning signs already are starting to appear in the market, with AI moving toward a state of inflated expectations and concerns about false advertising starting to mount. In response, channel marketers need to avoid the trap of focusing too much on AI when selling and instead try to connect AI technologies to specific outcomes. Partners must also help clients see through marketing jargon and understand exactly what AI products offer and how they solve specific challenges. AI Washing Crackdown Companies are facing growing scrutiny about how they use AI in their products and services. While there is currently no federal AI regulation policy in the United States, both the Federal Trade Commission (FTC) and the Securities and Exchange Commission (SEC) are cracking down on organizations that make exaggerated claims in their products and advertising – a practice now being referred to as “AI washing.” In 2023, the FTC filed a suit against several companies for misrepresenting how they used AI in ecommerce By Gerald Baldino Cutting through the noise and getting to outcomes is key with AI AI Course Correct 24 THE CHANNEL MANAGER’S PLAYBOOK
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