tal quality difference is noticeable, ChatGPT consistently produces better outputs, even though our vendor claims to use the same underlying technology.” Yet the same lawyer who favored ChatGPT for initial drafts drew a clear line at sensitive contracts: “It’s excellent for brainstorming and first drafts, but it doesn’t retain knowledge of client preferences or learn from previous edits. It repeats the same mistakes and requires extensive context input for each session. For high-stakes work, I need a system that accumulates knowledge and improves over time.” In other words, users appreciate the flexibility and responsiveness of consumer LLM interfaces but require the persistence and contextual awareness that current tools cannot provide, said MIT. “Our data reveals a clear pattern: the organizations and vendors succeeding are those aggressively solving for learning, memory and workflow adaptation, while those failing are either building generic tools or trying to develop capabilities internally,” argued MIT. Agentic AI systems, which specifically maintain persistent memory, learn from interactions and can autonomously orchestrate complex workflows, directly address the learning gap that defines this gen AI divide. This can be seen in customer service agents that handle complete inquiries end-to-end, financial processing agents that monitor and approve routine transactions, and sales pipeline agents that track engagement across channels demonstrate, said MIT. In the meantime, an analysis of buyers and organizations that successfully crossed the gen AI divide provides advisors with information to guide customers as they enter the realm of AI or shift to the next wave of AI technologies. Getting Across For starters, organizations that successfully crossed the divide approached AI procurement differently. Top buyers acted less like SaaS customers and more like clients for business process outsourcing (BPO), holding vendors to benchmarks as they would a consulting firm or BPO. These organizations, said MIT researchers, demanded deep customization aligned to internal processes and data; benchmarked tools on operational outcomes, not model benchmarks; and partnered through early-stage failures, treating deployment as a co-evolution. “The most successful buyers understand that crossing the divide requires partnership, not just purchase,” said the report. Likewise, strategic partnerships achieved a significantly higher share of successful deployments than internal development efforts. Although researchers observed far more “build” initiatives than “buy” initiatives in their sample, success rates favored external partnerships. Pilots built via strategic partnerships were twice as likely to reach full deployment as those built internally, while employee usage rates were nearly double for externally built tools. “[P]artnerships often provided faster time-to-value, lower total cost and better alignment with operational workflows,” argued the report. “Companies avoided the overhead of building from scratch, while still achieving tailored solutions. Organizations that understand this pattern position themselves to cross the GenAI Divide more effectively.” Successful organizations also tended to decentralize the sourcing of AI initiatives, relying on a type of bottom-up sourcing versus a central lab. Rather than relying on a centralized AI function to identify use cases, winners allowed individual contributors, budget holders and team managers to surface problems, vet tools and lead rollouts. “Many of the strongest enterprise deployments began with power users, employees who had already experimented with tools like ChatGPT or Claude for personal productivity,” said the report. “These ‘prosumers’ intuitively understood GenAI’s capabilities and limits and became early champions of internally sanctioned solutions.” The most effective AI-buying businesses also did not wait for perfect use cases or central approval. “Instead, they drive adoption through distributed experimentation, vendor partnerships and clear accountability,” continued the report. “These buyers are not just more eager; they are more strategically adaptive.” Concerns among your customers of ending up with “more pilots than Lufthansa” are certainly justified, as are feelings of “seeing AI everywhere but in our P&L statement.” But customers can take heed; organizations that successfully cross the gen AI divide do three things differently, MIT researched advised. They buy rather than build, empower individuals and line managers rather than central labs, and they select tools that integrate deeply while adapting over time. “For organizations currently trapped on the wrong side,” they concluded, “the path forward is clear: stop investing in static tools that require constant prompting, start partnering with vendors who offer custom systems and focus on workflow integration over flashy demos.” o 22 CHANNELVISION | FALL 2025 “Would you assign this task to AI or a junior colleague?” Source: MIT Project NANDA Perceived Fitness for High-Stakes Work Source: MIT Project NANDA AI Preferred Human Preferred Complex projects (multi-week work, client management) Quick tasks (emails, summaries, basic analysis) In your organization’s view, how soon will quantum computers achieve the capability to break current encryption methods? Source: Cap Gemini survey of early adopters Within 1-2 years (i.e., the threat is imminent) Within 2-5 years (i.e., threat is not imminent but in the medium term) Within 5-10 years (i.e., the threat is in the longer term) Uncertain More than 10 years (i.e., quantum computing breakthroughs are not on the horizon) 16% 24% 44% 14% 3% 90% 10% 70% 30%
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