Rethinking organizational design in the age of agentic AI | MIT Technology Review

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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioSponsoredArtificial intelligenceRethinking organizational design in the age of agentic AIFor agentic AI to deliver material benefits to organizations, it can’t be layered onto existing operations. Instead, enterprise leaders must approach it as a systems-level change.
By MIT Technology Review Insightsarchive pageMay 26, 2026In partnership withEma Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.  Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.  The sticky tape problem The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures and "this is like adding sticky tapes to parts of an operating model that is breaking" Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance. 
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change. Growing the AI vocabulary  Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology. 
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It's the integration of AI agents into the fabric of the organization.”  For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.” According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.  AI agents as connective tissue The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”  As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.” To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don't wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.” The workforce, redesigned As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT. Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.

In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah. The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.  From output to outcome Success metrics are the third and final pillar of ABT.  As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.  “When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you'll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.  For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee. Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents. This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers? 
Laying the groundwork for systems-level change Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.  This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review. by MIT Technology Review InsightsShareShare story on linkedinShare story on facebookShare story on emailPopularA woman’s uterus has been kept alive outside the body for the first timeJessica HamzelouWant to understand the current state of AI? Check out these charts.Michelle KimInside the stealthy startup that pitched brainless human clonesAntonio Regalado10 Things That Matter in AI Right NowAmy NordrumDeep DiveArtificial intelligenceWant to understand the current state of AI? Check out these charts.According to Stanford’s 2026 AI Index, AI is sprinting, and we’re struggling to keep up.
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Agentic AI requires a fundamental, systems-level change in organizational design rather than merely layering AI agents onto existing operations, as noted by insights from MIT Technology Review. A disconnect exists between the ambition to adopt agentic AI and the organizational readiness to execute it, as most organizations recognize that their current infrastructure cannot support this change due to deficiencies in people, processes, and workflows. This challenge is often characterized as the "sticky tape problem," where AI agents are embedded into existing human operating models, which risks preventing organizations from unlocking the full potential of agents, which lies in their capacity to execute complex workflows autonomously, coordinate tasks, and iterate based on performance.

To frame this transformation, Ema Amid coined the term agentic business transformation (ABT), which serves as a new framework for understanding AI agent adoption. ABT contrasts with previous transformations, such as digital transformation (moving from paper to software) or AI transformation (adding AI to existing processes), by focusing on the integration of AI agents into the entire fabric of the organization. Prasun Shah suggests that the dedicated term ABT drives the necessary redesign of the operating model, workflows, decision rights, and performance management systems to ensure agents function as active participants in value creation rather than mere productivity aids.

ABT is built upon three core pillars. The first pillar concerns the technology stack, which must evolve from supporting human-operated, application-centric workflows to acting as connective tissue that allows AI agents to operate at machine speed across multiple systems simultaneously. This architectural shift demands prioritizing access to diverse datasets and applications to enable agents to contextualize information, thereby facilitating high-quality decision-making and creating competitive differentiation. This shift allows for faster development cycles, enabling leaders to configure AI employees using natural language and connect them to necessary systems, drastically reducing workflow time from months to days.

The second pillar involves the workforce, which requires significant redesign. As AI agents assume execution roles, traditional hierarchical structures blur. Managers must transition from supervising execution tasks to managing hybrid teams, necessitating focus on managing trust, explainability, psychological safety, and status dynamics. Furthermore, McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, compelling organizations to revise their recruitment, retention, and remuneration strategies.

The third pillar concerns success metrics. Traditional metrics focusing on individual activity or output, such as calls handled or reports filed, become meaningless when AI agents handle massive volumes of work. As Surojit Chatterjee points out, measuring success by activity can be misleading because it fails to capture whether those activities drove actual customer satisfaction, retention, or revenue. Therefore, enterprises must shift to outcome-based metrics that measure the broader benefits achieved rather than individual deliverables. For instance, measuring the percentage of contracts reviewed without human escalation, as opposed to cost per query, demonstrated a tripling of ROI when implemented by one large enterprise customer. Integrating outcome metrics also necessitates reconfiguring reward systems and accountability structures, as operational accountability becomes more diffused in human-AI teams, raising critical questions about who is responsible for AI-driven outcomes and necessary guardrails.

Ultimately, achieving agentic AI benefits requires laying the groundwork for this systems-level change by initiating internal dialogue about these three pillars: the workforce, the technology stack, and the metrics of success. This gradual inquiry allows leaders to bridge the gap between their ambitions and the execution required to successfully embrace AI agents.