Might Habib, co-founder and CEO of Author AI, delivered one of many bluntest assessments of company AI failures on the TED AI convention on Tuesday, revealing that just about half of Fortune 500 executives consider synthetic intelligence is actively damaging their organizations — and inserting the blame squarely on management's shoulders.
The issue, in line with Habib, isn't the expertise. It's that enterprise leaders are making a class error, treating AI transformation like earlier expertise rollouts and delegating it to IT departments. This strategy, she warned, has led to "billions of {dollars} spent on AI initiatives which are going nowhere."
"Earlier this yr, we did a survey of 800 Fortune 500 C-suite executives," Habib informed the viewers of Silicon Valley executives and traders. "42% of them mentioned AI is tearing their firm aside."
The prognosis challenges standard knowledge about how enterprises ought to strategy AI adoption. Whereas most main corporations have stood up AI process forces, appointed chief AI officers, or expanded IT budgets, Habib argues these strikes replicate a basic misunderstanding of what AI represents: not one other software program software, however a wholesale reorganization of how work will get accomplished.
"There’s something leaders are lacking after they evaluate AI to simply one other tech software," Habib mentioned. "This isn’t like giving accountants calculators or bankers Excel or designers Photoshop."
Why the 'previous playbook' of delegating to IT departments is failing corporations
Habib, whose firm has spent 5 years constructing AI techniques for Fortune 500 corporations and logged two million miles visiting buyer websites, mentioned the sample is constant: "When generative AI began exhibiting up, we turned to the previous playbook. We turned to IT and mentioned, 'Go determine this out.'"
That strategy fails, she argued, as a result of AI essentially modifications the economics and group of labor itself. "For 100 years, enterprises have been constructed round the concept execution is dear and exhausting," Habib mentioned. "The enterprise constructed complicated org charts, complicated processes, all to handle folks doing stuff."
AI inverts that mannequin. "Execution goes from scarce and costly to programmatic, on-demand and considerable," she mentioned. On this new paradigm, the bottleneck shifts from execution capability to strategic design — a shift that requires enterprise leaders, not IT departments, to drive transformation.
"With AI expertise, it will probably now not be centralized. It's in each workflow, each enterprise," Habib mentioned. "It’s now a very powerful a part of a enterprise chief's job. It can’t be delegated."
The assertion represents a direct problem to how most giant organizations have structured their AI initiatives, with centralized facilities of excellence, devoted AI groups, or IT-led implementations that enterprise models are anticipated to undertake.
A generational energy shift is occurring primarily based on who understands AI workflow design
Habib framed the shift in dramatic phrases: "A generational switch of energy is occurring proper now. It's not about your age or how lengthy you've been at an organization. The generational switch of energy is in regards to the nature of management itself."
Conventional management, she argued, has been outlined by the flexibility to handle complexity — massive groups, massive budgets, intricate processes. "The identification of leaders at these corporations, folks like us, has been tied to old fashioned energy buildings: management, hierarchy, how massive our groups are, how massive our budgets are. Our worth is measured by the sheer quantity of complexity we may handle," Habib mentioned. "In the present day we reward leaders for this. We promote leaders for this."
AI makes that mannequin out of date. "When I’m able to 10x the output of my workforce or do issues that would by no means be doable, work is now not in regards to the 1x," she mentioned. "Management is now not about managing complicated human execution."
As an alternative, Habib outlined three basic shifts that outline what she calls "AI-first leaders" — executives her firm has labored with who’ve efficiently deployed AI brokers fixing "$100 million plus issues."
The primary shift: Taking a machete to enterprise complexity
The brand new management mandate, in line with Habib, is "taking a machete to the complexity that has calcified so many organizations." She pointed to the layers of friction which have amassed in enterprises: "Good concepts dying in memos, the limitless cycles of approvals, the dying by 1,000 clicks, conferences about conferences — a dying, by the best way, that's taking place in 17 totally different browser tabs every for software program that guarantees to be a single supply of fact."
Reasonably than accepting this complexity as inevitable, AI-first leaders redesign workflows from first ideas. "There are only a few legacy techniques that may't get replaced in your group, that gained't get replaced," Habib mentioned. "However they're not going to get replaced by one other monolithic piece of software program. They’ll solely get replaced by a enterprise chief articulating enterprise logic and getting that into an agentic system."
She supplied a concrete instance: "We’ve got prospects the place it used to take them seven months to get a artistic marketing campaign — not even a product, a marketing campaign. Now they will go from TikTok development to digital shelf in 30 days. That’s radical simplicity."
The catch, she emphasised, is that CIOs can't drive this transformation alone. "Your CIO can't assist flatten your org chart. Solely a enterprise chief can take a look at workflows and say, 'This half is important genius, this half is official scar tissue that has to go.'"
The second shift: Managing the concern as profession ladders disappear
When AI handles execution, "your people are liberated to do what they're wonderful at: judgment, technique, creativity," Habib defined. "The previous management playbook was about managing headcount. We managed folks towards income: one enterprise growth rep for each three account executives, one marketer for each 5 salespeople."
However this liberation carries profound challenges that leaders should handle immediately. Habib acknowledged the elephant within the room that many executives keep away from discussing: "These modifications are nonetheless scary for folks, even when it's turn out to be unholy to speak about it." She's witnessed the concern firsthand. "It reveals up as tears in an AI workshop when somebody appears like their previous ability set isn't translated to the brand new."
She launched a time period for a typical type of resistance: "productiveness anchoring" — when staff "cling to the exhausting method of doing issues as a result of they really feel productive, as a result of their self-worth is tied to them, even when empirically AI could be higher."
The answer isn't to look away. "We’ve got to design new pathways to affect, to indicate your folks their worth just isn’t in executing a process. Their worth is in orchestrating techniques of execution, to ask the subsequent nice query," Habib mentioned. She advocates changing profession "ladders" with "lattices" the place "folks must develop laterally, to develop sideways."
She was candid in regards to the disruption: "The primary rungs on our profession ladders are certainly going away. I do know as a result of my firm is automating them." However she insisted this creates alternative for work that’s "extra artistic, extra strategic, extra pushed by curiosity and affect — and I consider much more human than the roles that they're changing."
The third shift: When execution turns into free, ambition turns into the one bottleneck
The ultimate shift is from optimization to creation. "Earlier than AI, we used to name it transformation once we took 12 steps and made them 9," Habib mentioned. "That's optimizing the world as it’s. We are able to now create a brand new world. That’s the greenfield mindset."
She challenged executives to establish assumptions their industries are constructed on that AI now disrupts. Author's prospects, she mentioned, are already seeing new classes of development: treating each buyer like their solely buyer, democratizing premium companies to broader markets, and coming into new markets at unprecedented velocity as a result of "AI strips away the friction to entry new channels."
"When execution is considerable, the one bottleneck is the scope of your personal ambition," Habib declared.
What this implies for CIOs: Constructing the stadium whereas enterprise leaders design the performs
Habib didn't depart IT leaders with no position — she redefined it. "If tech is everybody's job, you could be asking, what’s mine?" she addressed CIOs. "Yours is to offer the mission vital infrastructure that makes this revolution doable."
As tens or lots of of 1000’s of AI brokers function at numerous ranges of autonomy inside organizations, "governance turns into existential," she defined. "The enterprise chief's job is to design the play, however it’s a must to construct the stadium, it’s a must to write the rule ebook, and it’s a must to be sure that these performs can win at championship scale."
The formulation suggests a partnership mannequin: enterprise leaders drive workflow redesign and strategic implementation whereas IT offers the infrastructure, governance frameworks, and safety guardrails that make mass AI deployment protected and scalable. "One can't succeed with out the opposite," Habib mentioned.
For CIOs and technical leaders, this represents a basic shift from gatekeeper to enabler. When enterprise models deploy brokers autonomously, IT faces governance challenges not like something in enterprise software program historical past. Success requires real partnership between enterprise and IT — neither can succeed alone, forcing cultural modifications in how these capabilities collaborate.
An actual instance: From multi-day scrambles to prompt solutions throughout a market disaster
To floor her arguments in concrete enterprise affect, Habib described working with the chief shopper officer of a Fortune 500 wealth advisory agency throughout latest market volatility following tariff bulletins.
"Their cellphone was ringing off the hook with prospects attempting to determine their market publicity," she recounted. "Each request kicked off a multi-day, multi-person scramble: a portfolio supervisor ran the present, an analyst pulled charts, a relationship supervisor constructed the PowerPoint, a compliance officer needed to evaluation every little thing for disclosures. And the chief in all this — she was forwarding emails and chasing updates. That is the highest job: managing complexity."
With an agentic AI system, the identical work occurs programmatically. "A system of brokers is ready to assemble the reply quicker than any variety of folks may have. No extra midnight deck evaluations. No extra days on finish" of coordination, Habib mentioned.
This isn't about marginal productiveness features — it's about essentially totally different working fashions the place senior executives shift from managing coordination to designing clever techniques.
Why so many AI initiatives are failing regardless of large funding
Habib's arguments arrive as many enterprises face AI disillusionment. After preliminary pleasure about generative AI, many corporations have struggled to maneuver past pilots and demonstrations to manufacturing deployments producing tangible enterprise worth.
Her prognosis — that leaders are delegating quite than driving transformation — aligns with rising proof that organizational components, not technical limitations, clarify most failures. Corporations usually lack readability on use circumstances, wrestle with knowledge preparation, or face inside resistance to workflow modifications that AI requires.
Maybe probably the most hanging facet of Habib's presentation was her willingness to acknowledge the human value of AI transformation — and demand leaders handle it quite than keep away from it. "Your job as a pacesetter is to not look away from this concern. Your job is to face it with a plan," she informed the viewers.
She described "productiveness anchoring" as a type of "self-sabotage" the place staff resist AI adoption as a result of their identification and self-worth are tied to execution duties AI can now carry out. The phenomenon means that profitable AI transformation requires not simply technical and strategic modifications however psychological and cultural work that many leaders could also be unprepared for.
Two challenges: Get your palms soiled, then reimagine every little thing
Habib closed by throwing down two gauntlets to her government viewers.
"First, a small one: get your palms soiled with agentic AI. Don't delegate. Select a course of that you simply oversee and automate it. See the distinction from managing a fancy course of to redesigning it for your self."
The second was extra bold: "Return to your workforce and ask, what may we obtain if execution have been free? What would work really feel like, be like, appear to be should you're unbound from the friction and course of that slows us down at this time?"
She concluded: "The instruments for creation are in your palms. The mandate for management is in your shoulders. What is going to you construct?"
For enterprise leaders accustomed to viewing AI as an IT initiative, Habib's message is obvious: that strategy isn't working, gained't work, and displays a basic misunderstanding of what AI represents. Whether or not executives embrace her name to personally drive transformation — or proceed delegating to IT departments — might decide which organizations thrive and which turn out to be cautionary tales.
The statistic she opened with lingers uncomfortably: 42% of Fortune 500 C-suite executives say AI is tearing their corporations aside. Habib's prognosis suggests they're tearing themselves aside by clinging to organizational fashions designed for an period when execution was scarce. The remedy she prescribes requires leaders to do one thing most discover uncomfortable: cease managing complexity and begin dismantling it.
