As enterprises implement more complex AI workflows, they are moving more toward a “mostly platform” model for their application procurement as opposed to buying mostly points solutions or single integrated platforms. This approach can be complicated by a piecemeal approach to software, suggest findings from Futurum. According to its survey of 830 IT decision-makers, the best-of-breed procurement philosophy has fallen to 20.7 percent, down 3.6 percentage points from 2H 2025, as enterprises consolidate onto integrated platforms to meet the data demands of AI. All the while, the “mostly platform + point solution” model surged from 60 percent to 65.9 percent. Reinforcing the trend, 41 percent of organizations are actively planning to reduce or consolidate their application count, said Futurum researchers, with the most common strategy targeting the elimination of one to four applications in favor of a suite or platform. “What is driving platform consolidation in 2026 is not cost-cutting, but the increasing use of AI and agentic workflows,” said Keith Kirkpatrick, vice president and research director for The Futurum Group. “Effective AI deployment requires clean, consolidated data that flows across business functions, and organizations that stitch together 10-to-15-point solutions face an integration tax that makes enterprise-wide AI strategies nearly impossible. Platform consolidation eliminates data silos and creates the unified data fabric that AI models need to deliver actionable results.” Consolidation timelines are aggressive, showed Futurum’s data. Among organizations planning to reduce their application stack, 50.9 percent are targeting a four-to-six-month implementation window, reflecting urgency rather than gradual migration. U.S. companies routinely keep funding transformation and AI projects that are failing, often long after the warning signs are clear, according to new research from management consultancy Emergn. The study of 700 senior leaders found that politics, sunk costs and a reluctance to admit failure keep failing projects alive when leaders know they should be stopped. The waste is substantial – organizations lose an average of 2.3 percent of annual revenue to transformation and AI work that fails to deliver – but it is not inevitable, the research suggests. The problem is not ambition; it’s a failure of governance. “Organizations find it hard to spot failing programs early, harder still to stop them and harder again to hold an honest view of how the work is really going,” said the study. Less than a third of U.S. leaders said that stopping an underperforming program is a normal part of how their organization works. More than four in ten (43 percent) act only after significant time and money have already gone in, while more than one-fifth (22 percent) have watched a program carry on for no reason other than the amount already spent on it. A quarter admit that decisions to stop a project are driven more by politics than by evidence. The pattern starts at the front end, with just 16 percent of new ideas, on average, formally rejected before real money is committed. U.S. organizations run an average of 6.8 transformation and AI initiatives at once, and more than half (51 percent) are running seven or more. One in 10 operates with no formal tracking or governance at all. “None of this is a technology problem, and none of it is solved by spending more. The organizations that get real value from AI share one habit,” Emergn researchers concluded. “They have introduced a product-centric operating model that gives them the discipline to focus on the most valuable ideas and, from there, allows them to maintain a live view of every initiative.” The Move to ‘Mostly Platform’ Buying Lack of Governance Funds Failures AI & AUTOMATION Enterprise Application Procurement Strategy 2H 2025 1H 2026 Mostly platform + point solutions 60% 65.9% Best-of-breed (point solutions) 24.3% 20.7% Single integrated platform 15.7% 13.4% 0% 20% 40% 60% 70% SOURCE Futurum Research, February 2026 8 CHANNELVISION | SUMMER 2026
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