# AI's hardest problems are people problems. Change budgets say otherwise. > BCG's 2024 survey attributes about 70 percent of AI implementation challenges to people and process. Monitor Deloitte's 2025 study finds change management and communications get an average of 9 percent of transformation budgets. The Langford Institute, October 4, 2026. AI Adoption; Analysis, AI Adoption Desk. 4 min read. Web page: https://www.thelangfordinstitute.org/articles/ai-hardest-problems-are-people-problems/ Boston Consulting Group surveyed 1,000 senior executives in 2024 and found that about 70 percent of the challenges companies face in implementing AI stem from people and process issues. Technology problems accounted for 20 percent of the challenges in BCG's survey, and the algorithms themselves for 10 percent. Transformation budgets are weighted differently. Monitor Deloitte's 2025 Chief Transformation Officer Study found that change management and communications received an average 9 percent of transformation budgets, the smallest of six spending areas. Technology received 26 percent in one of the Deloitte report's charts and 28 percent in another. The two findings do not measure the same thing. Deloitte's figure covers transformation budgets in general, not AI programs specifically, so the comparison is suggestive, not like for like. BCG's figure describes where problems arise, not where money is spent. Still, the pairing raises a fair question for any AI program: whether its budget is aimed at the problems it is most likely to meet. ## What the evidence supports BCG's 2024 survey covered CxOs and senior executives in 59 countries and more than 20 sectors. It found that 74 percent of companies had yet to show tangible value from AI, and it classed 26 percent as leaders. BCG's release names change management among the people and process capabilities that set those leaders apart. The 70 percent figure needs careful handling. BCG also presents its 10-20-70 split as a rule for allocating resources, one it says leaders follow, and describes it as a long-held belief. It is best treated as a BCG heuristic backed by survey data rather than an independently validated measurement. It is also often misquoted as saying that 70 percent of AI's value comes from people. BCG's claim is about where the challenges lie. Newer surveys point the same way. In McKinsey's 2026 State of AI survey, about 6 percent of respondents qualified as AI high performers, meaning they attribute 5 percent or more of EBIT to AI and describe its value as significant. Nearly three-quarters of those high performers reported fundamentally redesigning workflows because of their AI use, up from 55 percent in 2025. Among other respondents, the share was one-quarter. McKinsey also found high performers twice as likely to say senior leaders demonstrate commitment to AI and to have defined processes for measuring its impact. Workers tell a similar story. BCG's 2026 AI at Work survey of 11,749 workers in 14 markets associates a clear strategy with a 25 percentage point lift in measurable business impact from AI, against about 5 points for better tools alone. Respondents in companies pursuing workflow redesign were 24 points more likely to see measurable improvement. > If the issue log and the budget disagree, the budget is the one to change. ## The strongest case against The best argument against this reading comes from Deloitte's own sample of 200 transformation executives. More than 90 percent had overseen three or more transformations, and more than 80 percent led programs that met all their targets. On average, these leaders reported giving 9 percent of their 2024 transformation budgets to change management and communications, and only 33 percent would increase that share in hindsight. If 9 percent were enough to sink a program, it would be odd to find it as the average in a sample dominated by leaders who met all their targets. The budget line is also narrow. Deloitte counts talent as a separate spending area, and some people and process work, such as training or workflow redesign, may be booked under other lines. The 9 percent may understate what organizations spend on the human side of change. The evidence that people and process work pays off has limits too. The McKinsey and BCG findings are self-reported associations, not controlled experiments. Companies already getting value from AI may find it easier to redesign workflows, rather than the reverse. McKinsey's high-performer group is small, so its percentages carry wide margins. BCG's measure of business impact is what employees perceive, not audited results. BCG, McKinsey and Deloitte are all consulting firms with an interest in this conclusion. These objections weaken any claim of proof. They do not reverse the direction of the evidence. The AI-specific research points to people and process as the harder part of adoption. Deloitte's experienced leaders lean the same way in hindsight for transformations in general: 42 percent would raise investment in talent, and 33 percent would raise spending on change management and communications, the smallest line in the budget. ## What to check in your own program Industry averages matter less than an organization's own numbers. Four checks can show whether an AI program is funded for the problems it is likely to meet. - Split the AI budget the way Deloitte does, separating technology from change management and communications, and from talent. If the split cannot be produced, that is a finding in itself. - Tag each open problem in the program's issue log as people and process, technology or algorithm, the categories BCG used. Then compare the share of problems in each category with the share of money. - Count the workflows that have changed because of AI, and name an owner for each redesign. Report that count alongside the number of tools deployed. - Agree on how impact will be measured before the program scales. McKinsey's high performers were twice as likely to have defined processes for measuring AI's impact, and a measure set early lets later budget debates rest on results. Run the first two checks side by side. If the issue log and the budget disagree, the budget is the one to change. ## Sources - Boston Consulting Group. "AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value." BCG, October 24, 2024. https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value - Monitor Deloitte. "2025 Chief Transformation Officer Study: Six things to know about transformations today." Deloitte, 2025. https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2025/us-2025-chief-transformation-officer-survey.pdf - McKinsey & Company. "The state of AI in 2026: On the road to ROI." McKinsey & Company, August 25, 2026. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai - Boston Consulting Group. "AI Is Reshaping Jobs Faster Than Companies Are Reshaping Work." BCG, June 3, 2026. https://www.bcg.com/press/3june2026-ai-reshaping-jobs-faster-than-companies-reshaping-work --- Cite as: The Langford Institute, "AI's hardest problems are people problems. Change budgets say otherwise.," October 4, 2026, https://www.thelangfordinstitute.org/articles/ai-hardest-problems-are-people-problems/ Editorial standards: https://www.thelangfordinstitute.org/standards/