survey<\/a> of 951 companies out of Bain and Company suggests.<\/p>\nNearly 40% of companies that measured AI cost savings landed below 10% savings, despite their initial targets of gains of at least 11% to 20%, the consultancy finds in its research. Still, yet 90% are ramping up their budgets again, this time to build and deploy agents \u201cthat will operate with even greater autonomy, complexity, and consequence.\u201d<\/p>\n
It\u2019s an annual ritual to see boards expand their automation budgets, the study\u2019s authors, Michael Heric, Purna Doddapaneni, and Antoine Debarre, all with Bain, point out. \u201cEvery year, CEOs sign off on the next wave\u2014robotic process automation, then machine learning, then generative AI, now agents. And every year, the savings fall short.\u201d<\/p>\n
This falling short isn\u2019t enough to trigger alarms, they add. The gap is \u201cnot enough to kill the programs, but consistently, quietly, and by a margin that should be making executives uncomfortable.\u201d <\/p>\n
But hey, it\u2019s AI, right? \u201cThe technology worked. The value didn\u2019t arrive,\u201d they write. <\/p>\n
What\u2019s at issue? Heric and his co-authors point out the following roadblocks to AI value:<\/p>\n
AI automation isn\u2019t autonomous \u2013 human workforces are still required. \u201cOnly 7% of companies are running fully autonomous agents in production today.\u201d Companies are making circular bets on automation. \u201cWhen asked how they plan to fund generative AI and agentic AI investments, 44% of companies\u2014the largest group\u2014cited savings from prior automation programs. Self-funding the next wave from past returns sounds like discipline. In reality, it is a circular bet with a structural leak. The prior wave underdelivered. The savings pool is smaller than assumed.”Data is still the wall\u2014and it\u2019s not coming down. \u201cData access and integration remains the top barrier to AI progress, despite heavy investments in data modernization,\u201d the researchers find. It\u2019s the single biggest barrier to AI progress, cited by 41% of respondents.<\/p>\n
The Bain team makes the following recommendations to get ahead of the curve with AI return on investment:<\/p>\n
Don\u2019t pave cowpaths with AI. \u201cThe question to ask before any AI program is approved is not \u2018Where can we apply AI?\u2019 but \u2018If we were designing this process from scratch today, what would it look like?\u2019\u201dLook at previous tech ROI. \u201cCFOs should audit actual returns from prior automation programs, not projected returns. If the previous program delivered 60% of its targeted savings, size the current investment accordingly.\u201dPut someone in charge. Governance tends to be \u201csplit almost evenly between IT, business functions, and central teams, with no clear owner in most organizations,” the Bain co-authors point out. “When an agent makes a consequential error in a production system, accountability cannot be improvised in the moment. It must be established in advance.\u201dUse AI itself to solve the data problem. Automate “one repeatable, high-value workflow where humans are currently pulling data manually, consolidating spreadsheets, and producing reports, and replacing that entire sequence with AI.\u201dRedesign employees\u2019 roles. \u201cIn an agent-led operating model, employees are no longer moving work along a process; they are orchestrating, supervising, and making the high judgment calls that agents cannot. It requires deliberate investment in role redesign, new ways of working, and change management.\u201dMeasure outcomes at the enterprise level, not the program level. \u201cWhat matters for the enterprise is whether AI investment is producing better decisions, faster responses, and stronger customer outcomes.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Show me the money, AI getty Yes, artificial intelligence is a huge business. Worldwide spending on AI is…\n","protected":false},"author":2,"featured_media":88184,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[179,24,25,22197,46834,3545,46835],"class_list":["post-88183","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-agentic-ai","tag-ai","tag-artificial-intelligence","tag-bain","tag-bain-automation","tag-gartner","tag-return-on-investment"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/88183","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=88183"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/88183\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/88184"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=88183"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=88183"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=88183"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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