Why a Divide If you ask employees directly why their companies get stuck on the wrong side of the divide, you’ll hear some familiar complaints that come with emerging technologies, such as lack of executive buy-in and resistance to change. MIT analysis of implementations, however, suggested organizations aren’t investing in the right places. In terms of functional focus, investment in gen AI tools is heavily concentrated, said MIT researchers, with sales and marketing functions capturing approximately 50 to 70 percent of AI budget allocation across organizations surveyed. In general, front-office tools such as those for sales and marketing get the attention because their outcomes are highly visible, impacts are measured easily and the gains are board-friendly. Metrics such as demo volume or email response time, for instances, align directly with board-level KPIs, said MIT. While bias reflects easier metric attribution, “some of the most dramatic cost savings we documented came from back-office automation,” said MIT researchers. “While front-office gains are visible and board-friendly, the back-office deployments often delivered faster payback periods and clearer cost reductions.” Organizations that focused AI investment on back-office functions such as legal, procurement, operations and finance experienced more substantial, although more subtle, efficiencies such as fewer compliance violations, streamlined workflows or accelerated month-end processes. Real cost savings came when organizations were able to replace BPOs and external agencies with AI-powered internal capabilities – and not from cutting internal staff. “This investment bias perpetuates the GenAI Divide by directing resources toward visible but often less transformative use cases, while the highest-ROI opportunities in backoffice functions remain underfunded.” A similar scenario emerges surrounding investment in general versus specific tools. Consumer-grade tools such as ChatGPT and Copilot are widely used and widely praised among respondents for their flexibility, familiarity and immediate utility. At the same time, generic tools such as LLM chatbots appear to show high pilot-to-implementation rates of more than 83 percent. But again, these tools were used to make changes that were more visible than transformative, said MIT researchers, largely applied to quick tasks while leaving complex projects requiring customization or sustained attention to humans. Yet users of consumer-grade and generic gen AI tools were “overwhelmingly skeptical of custom or vendor-pitched AI tools,” said the MIT study, describing them as brittle, overengineered or misaligned with actual workflows. “Users prefer ChatGPT for simple tasks but abandon it for mission-critical work due to its lack of memory,” said the study. Herein lies what MIT researchers called the “learning gap” that is the primary factor keeping organizations on the wrong side of the gen AI divide: static tools that don’t learn and can’t evolve, integrate poorly into workflows and fail to deliver context. “The core barrier to scaling is not infrastructure, regulation or talent. It is learning,” stated MIT researchers. “Most GenAI systems do not retain feedback, adapt to context or improve over time.” A corporate lawyer at a mid-sized firm exemplified this dynamic. Her organization invested $50,000 in a specialized contract analysis tool, yet she consistently defaulted to a $20-per-month general-purpose tool for drafting work. “Our purchased AI tool provided rigid summaries with limited customization options. With ChatGPT, I can guide the conversation and iterate until I get exactly what I need” this lawyer told MIT researchers. “The fundamen20 CHANNELVISION | FALL 2025 0 1 2 3 4 5 6 7 8 9 10 Source: MIT Project NANDA How executives select GenAI vendors Derived from interviews and coded by category “Would you assign this task to AI or a junior colleague?” 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Source: MIT Project NANDA Flexibility when things change The ability to improve over time Clear data boundaries Minimal disruption to current toots Deep understanding of our workflow A vendor we trust 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% “This investment bias perpetuates the GenAI Divide by directing resources toward visible but often less transformative use cases, while the highest-ROI opportunities in back-office functions remain underfunded.”
RkJQdWJsaXNoZXIy NTg4Njc=