as marketing automation, quoting tools, basic office productivity and organization, social media moderation and even text generation and summarization. The logic here is that using even simple AI tools will provide advisors with a better understanding of the outcomes that can be delivered, providing them more confidence and credibility when leading conversations with clients. Findings from Telarus seem to support this notion, as advisors who actively use AI within their own operations report significantly greater confidence in selling AI solutions. “Whether deploying AI for internal workflows or marketing, or running solution pilots, these advisors are more likely to understand real-world challenges and demonstrate what’s possible,” said the research report. Compared to the 13 percent of all advisors who feel very prepared to discuss AI with customers, 21 percent of those who use AI for internal operations feel the same way. Nearly eight in 10 of the advisors that use AI in internal operations are very or somewhat prepared. Very similar percentages were seen among technology advisors who are using AI for marketing and sales, as well as running AI pilots. Advisors using AI internally are also more optimistic. These partners are more likely to believe AI will drive more business and revenue in the next 12 to 24 months, showed the Telarus survey. “This suggests that hands-on experience not only builds credibility but also translates into stronger client conversations and more consultative selling,” said the report. Better Outcomes Once AI conversations are initiated, advisors must be careful not to get tied down in technical capabilities and general infrastructure modernization. Similar to other areas of technology being sold nowadays, advisors are recommended to anchor their AI messaging in specific business outcomes. And across both mid-sized firms (50 to 500 employees) and large enterprises (500+), decisionmakers for the most part are looking to AI to boost productivity and operational efficiency and improve customer experience. Telarus findings, however, did point to some clear distinctions between the desired outcomes of mid-market firms and large enterprises. Mid-market organizations, for example, are far more likely to emphasize innovation, modernization and improved employee experience, while large enterprise buyers a particularly focused on cost reduction and the headcount control provided by tools such as automation. Indeed, large enterprises are greater than 4x more likely to have investment decisions influenced by costcutting than mid-market organizations, showed the survey. Mid-market, meanwhile, tends to prioritize AI investments that deliver immediate operational improvements and measurable revenue impact. In general, the mid-market is outpacing large enterprise peers when it comes to AI adoption. These organizations tend to be more agile, more open to experimentation and more likely to embed AI across departments, often coupling AI investments with cloud modernization, cybersecurity upgrades and CX platform overhaul, said Telarus researchers. “While enterprises are tightening their belts, mid-market is not holding back with investment and experimentation as AI provides them a route to catch up in scale with their enterprise counterparts,” said the report. Mid-market firms also are 3x more likely to engage external advisors. “Identification of project motive early in a sales cycle will enable advisors to properly guide selling conversations based on desired outcomes,” advised the report. McKinsey & Co. analysts Bob Sternfels and Yuval Atsmon, for their part, recommend that participants focus on clear and specific goals and outcomes instead of vague hypothesis. Rather than “improve productivity with AI,” for example, begin with specific, testable predictions such as, “Using AI to automate your monthly reporting process will reduce the time spent by 50 percent while maintaining accuracy above 95 percent,” they continued. This becomes even more important as businesses shift their attention away from “horizontal uses cases,” such as enterprise-wide copilots and chatbots, and toward more challenging “vertical uses cases,” or those embedded into specific business functions and processes, suggest McKinsey findings. Data Sets Apart One way for an advisor to stand out is a willingness to dive into the tough but important questions around data readiness. In other words, is data available, high enough quality, properly structured and aligned with AI use cases. According to a recent report from MIT and Snowflake, more than three quarters of businesses lack a very ready data foundation to support generative AI. In a recent Salesforce study, 52 percent of CIOs cited untrustworthy data (poor accuracy, recency) among their top AI fears. And while one third of buyers ranked data readiness as a top IT buying driver, only a small share of channel partners has How is your technology advisory business currently using AI? Using AI for internal operations 20.38% Using AI for marketing and sales 17.83% Offering AI-embedded solutions to customers 12.10% Running AI pilot programs 12.10% Exploring AI solutions but have not yet implemented them 16.56% Not currently using AI but interested in learning more 17.83% Not interested in AI 3.18% Source: Telarus 15 SUMMER 2025 | CHANNELVISION
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