Opinion
In a guest post, Kyle Schofield, client director in the MSP division at ContinuServe, draws parallels between the shift from break-fix IT support to managed services and the current rise of AI and automation. He warns that partners who treat AI adoption as a one-time project, as many did with cloud lift-and-shift, will miss out on recurring revenue from ongoing optimization and governance. Schofield outlines three pillars for building an AI business: stand up automation and cloud security as dedicated service towers, package AI governance and change management as a salable service, and use scale to make AI tooling affordable as an operating expense. He notes that larger providers are already moving downmarket with AI-enabled offerings, and midmarket clients lack clear AI strategies and talent, creating an opening for partners who can lead the conversation.
Enterprises are deploying AI faster than they can track or govern it, with 70% of CIOs/CTOs saying AI is deployed faster than teams can track it and 77% saying adoption outpaces governance capacity. Only 13% of organizations are AI pacesetters with mature governance. This control gap leads to financial losses (99% experienced AI-related losses, average over $4 million) and legal risks. Channel partners can seize the opportunity by offering AI governance services: inventory assessments, policy development, and investigation readiness. Governance is ongoing, and partners who act early can become trusted advisors.
Greg Davis, CEO of Bigleaf Networks, writes that businesses now have multiple diverse connectivity options including fiber, 5G and Starlink, but more connections do not automatically create a better experience. He distinguishes connecting multiple circuits from orchestrating them, noting that a circuit can be technically up while applications like Teams, VoIP or point-of-sale systems perform poorly. Davis urges the channel to move beyond SD-WAN feature battles, start with what customers are trying to accomplish, and treat 5G and satellite as complementary paths that need to work together. He also frames this as a channel simplicity problem, calling on vendors to make technology easier for partners to position and customers to understand, and concludes that selling another circuit cannot be the end of the conversation.
Channel partners often fall into the "pilot trap" in customer AI projects—demonstrations succeed but implementation fails. This article analyzes the structural reasons behind this, compares the essential differences between traditional BI sales models and AI deployment, proposes the concept of "decision-level AI," and uses real cases to show how to build scalable AI services with decision design as the guide.
Santhosh Keshavan, Chief Technology and Operations Officer at Voya Financial, believes that the success of AI transformation depends on whether employees are ready for and trust new technologies. Through approximately five hours of AI foundational training, company-wide participation in design, and internal innovation competitions, Voya achieved nearly 100% of employees obtaining literacy certification and 98% of employees using Copilot an average of 24 times per week within three months, successfully shifting employee mindsets from concern to curiosity.
After companies spend heavily on introducing AI technology, employee engagement often declines months later, precisely when consultants withdraw from the project. Sam Shar, CEO of Trend-Setters Consulting, points out that to bridge the gap between deployment and sustained effective use, digital transformation partners need to significantly extend their stay, even stationing full-time staff in client offices to provide ongoing training and immediate support.
AI spending is projected to grow 47% to $2.5 trillion by 2026, but most pilot projects fail to scale. A Zapier survey shows that only 13% of organizations have widely deployed AI projects. This article points out that leadership control issues are the core obstacle, covering four aspects: business problem ownership, technology fit and validation, full-cost evaluation, and operational model design. By clarifying business outcomes, adopting full-cost metrics, and establishing control layers and learning loops, enterprises can improve pilot success rates.
An Exclaimer survey shows that 80% of organizations still manage email signatures manually, and 92% believe signature consistency is crucial to a professional image. Regional MSPs, leveraging customer trust and localization advantages, can turn signature governance into recurring revenue, filling the gap left by larger service providers in the mid-market.
Chen Goldberg, Executive Vice President of Products and Engineering at CoreWeave, believes that token-based billing was effective in the early stages of AI, but when faced with diverse production-grade workloads, this metric cannot capture the true economics. He suggests that enterprises shift to evaluating GPU-hour utilization, fault detection speed, and elasticity costs, and make decisions through real workload testing.
Most enterprise software evaluations begin with a feature checklist and ultimately yield a decision that appears confident but is inherently fragile. Sudhakar Shivaraju, Vice President of Global Real Estate Systems at JPMorgan Chase, draws on two formal selection experiences across industries and years to point out that the real risk lies not in choosing the wrong vendor, but in evaluating the wrong things, or mis-weighting the right things under real constraints. He advises CIOs to segment functional domains by actual usage scenarios (security and compliance, integration and API architecture, user experience and adoption resistance, vendor support and roadmap transparency, AI capabilities, total cost of ownership) and set weights based on their own constraints rather than generic templates. At the same time, vendor roadmaps should be treated as primary evidence rather than marketing material, distinguishing confirmed commitments from visionary statements; support responsiveness and roadmap transparency should be scored separately; AI capabilities should be examined as an independent domain, probing whether they are based on the enterprise's real data, how the privacy model works, and whether they are already in production; and switching costs such as data migration and user retraining should be honestly quantified.