AI Doesn’t Need to Be Perfect at Everything to Deliver Real Value
Artificial Intelligence has become synonymous with productivity in the corporate world, but most companies are still using this technology in a pretty limited way.
You know that feeling where AI helps here and there, but the overall results don’t really change that much? Yeah, you’re not imagining it. The way most organizations adopt AI today follows a very similar pattern: pick a task, plug in a tool, and hope productivity goes up. Writing emails faster, summarizing documents, generating code. All of that works, but none of those gains come close to the real potential this technology has to offer.
New research from the MIT Sloan School of Management has shown exactly where the flaw in this logic lies. The study, titled Chaining Tasks, Redefining Work: A Theory of AI Automation, argues that the true value of Artificial Intelligence doesn’t show up when it improves a single task in isolation, but rather when it transforms how different tasks connect and flow into one another within a complete workflow. 🔗
The paper was written by Peyman Shahidi, a doctoral candidate at MIT Sloan, alongside professors Mert Demirer and John Horton, also from MIT Sloan, as well as Nicole Immorlica from Yale University and Microsoft, and Brendan Lucier from Microsoft Research. It represents a real paradigm shift in how we think about automation, and the concepts these researchers introduced significantly change the conversation about how companies should be integrating AI into their processes.
The Problem with One-Off AI
When a company decides to adopt Artificial Intelligence, the most natural move is to identify a specific pain point and apply the technology there. An overwhelmed customer service team gets a chatbot. A legal department drowning in contracts gets a text analysis tool. A development team starts using a coding assistant. Each of these cases has merit, and the individual gains are real, but the MIT research points out that this approach has a very low ceiling when the goal is to genuinely transform organizational efficiency.
The reason is simple: corporate work rarely happens in isolated tasks. A business proposal doesn’t start with the writing itself — it depends on a client analysis, which depends on CRM data, which depends on a meeting brief, which depends on a sales rep’s notes. They’re links in a chain. When you improve just one link, the chain as a whole doesn’t get much stronger. The bottleneck simply moves to the next point. And that’s exactly the dynamic that makes so many AI implementations look promising on paper but disappointing in practice.
The study uses the concept of complementarity between tasks to explain this phenomenon. When AI automates one step within a chained process, it frees up time and energy for the following steps. But if those following steps are still manual, slow, and dependent on constant human intervention, the benefit of the earlier automation dissolves. The workflow as a whole remains stuck — just at different points now.
The Logic of Task Chaining
The central concept of the research is what the authors call the task chain, or task chaining. The idea is that work within an organization isn’t a collection of independent activities, but rather a structured sequence where each step depends on the previous one and feeds the next. When Artificial Intelligence is applied with this perspective in mind, the potential for transformation changes scale entirely. Instead of improving a single step, the automation propagates along the entire chain, because adjacent tasks begin to be executed as a continuous flow by AI. 🚀
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The researchers use a pretty interesting example to illustrate this: classroom teaching versus individual tutoring. Both activities involve similar tasks — explaining concepts, preparing material, and answering questions. But the workflow is completely different. A teacher prepares content in advance, in a sequence that makes it easier to automate parts of the process. A tutor, on the other hand, operates in a continuous back-and-forth with the student, which severely limits automation opportunities.
As Shahidi explained, the ability to automate workflows using AI is very limited in that second type of occupation. What matters isn’t just which tasks exist in a profession, but how those tasks emerge and organize themselves within the flow. That difference in arrangement is what determines whether AI will produce a marginal gain or a real transformation.
For another practical example, think about a monthly financial reporting process. Traditionally, it involves collecting data from multiple systems, consolidating it into spreadsheets, analyzing variances, writing commentary, and formatting for presentation. If AI only steps in at the writing stage, the gain is modest. But if it starts handling automated data collection, anomaly detection, data-driven narrative generation, and final formatting, the entire flow is transformed. The analyst who used to spend three days on this process now dedicates a few hours to review and interpret, instead of mechanically executing each step.
Not All Chains Work the Same Way
A crucial point in the research is that not all task chains are equally automatable. When adjacent tasks within a flow are well-suited for AI, they can be grouped and executed efficiently. However, when a single step in the middle of the sequence is too difficult for AI, it breaks the entire chain.
Shahidi put it pretty bluntly: if one of the tasks in the middle of the process is extremely difficult for AI, that single task will compromise the whole operation. It’s like a clogged pipe in the middle of brand-new plumbing. It doesn’t matter if the other sections are perfect — the water won’t flow.
This leads to a work design principle the research highlights: how tasks are grouped matters just as much as which tasks are automated. In other words, reorganizing the sequence of activities within a process can be just as impactful as the technology itself.
AI Doesn’t Need to Beat Humans at Every Step
One of the most counterintuitive findings in the study is that AI doesn’t need to outperform humans at every individual task to generate value. In fact, it may make more sense for an organization to delegate an entire chain of tasks to AI, even if humans could execute some steps with higher quality.
The reason comes down to coordination costs. Every time work passes from AI to a human and back to AI, there’s a cost involved: review, validation, adjustment, context-switching. These checkpoints slow down the system as a whole. When AI takes over a sequence end to end, those friction points disappear. The final result may have slightly lower individual quality at certain steps, but the process as a whole becomes faster and cheaper.
Shahidi summed up this logic by pointing out that you save on the cost of human time. Removing repeated oversight at every micro-step can offset marginal performance differences. It’s like choosing a nonstop flight that takes a bit more total time in the air instead of making a connection that theoretically uses faster routes on each leg but adds hours of waiting at the airport.
This perspective changes how leaders should evaluate AI implementations. The focus should be less on whether the technology is excellent at each individual step and more on understanding whether it improves the efficiency of the workflow as a whole. Task adjacency takes center stage: when AI-compatible tasks are grouped together, the flow works. When they’re scattered and interrupted by steps AI can’t handle, the benefits drop dramatically. 💡
How This Changes Automation Strategy
What the MIT research proposes in practice is a shift in the starting point of automation strategy. Instead of asking which task can be improved with AI, the right question becomes which workflow can be redesigned with AI. It seems like a small difference, but it completely changes the project scope, the success metrics, and the expected outcomes. A company that starts from the workflow can see where the real bottlenecks are, which steps benefit most from automation, and how a gain in one part of the process ripples through the rest.
This also has direct implications for how technology teams and business leaders should collaborate on these initiatives. When the focus is on an isolated task, the project can be handled almost entirely by the IT team or a software vendor. But when the focus is on the complete workflow, there’s no way to move forward without deep involvement from the people who execute the process day in and day out. These are the people who know the invisible dependencies, the frequent exceptions, the rework loops, and the shortcuts that never appear in any official diagram. Ignoring this knowledge is one of the top reasons why AI automation projects stall before delivering results.
The research also points out that organizations adopting this chain-based view tend to develop a more lasting capability for Artificial Intelligence integration. Instead of accumulating a disconnected collection of AI tools — each solving a different problem without talking to the others — they build an infrastructure where models and systems feed into each other. The outputs of one automated step become the inputs for the next, creating a cycle of data and decisions that self-reinforces and improves over time. This level of integration is what separates an organization that uses AI from one that is truly powered by it.
Redesigning Roles and Expectations
Historically, roles within a company were defined by sets of tasks that made the most sense for a single person to handle. AI changes this equation by reducing the cost of certain activities and enabling new combinations of work. If the technology can automate several routine tasks within a role, the professionals who held those positions can take on additional responsibilities — usually work that requires more judgment, context, and decision-making.
The MIT researchers also highlight that this task chaining approach has an important effect on the workers involved. When AI takes over the more repetitive and mechanical parts of a flow, the people who previously handled those steps don’t disappear from the process — they reposition. Human work migrates to the parts that require creativity, relationship-building, and interpretation, which are exactly the areas where the technology still has significant limitations. This shifts the conversation from job replacement to role reconfiguration, which is a very different and far more productive perspective.
For business leaders, this transforms AI adoption from a purely technological decision into a broader organizational design challenge. And that challenge requires patience. Many companies expect quick returns on their AI investment, but the researchers point out that significant gains usually only appear after organizations have adapted their workflows and built enough capacity to operate differently.
Shahidi was straightforward in stating that until you hit that tipping point, the costs of AI adoption dominate the gains. Only after the organization crosses that threshold does restructuring work around AI start delivering measurable benefits. Ignoring this maturation curve is one of the most common mistakes organizations make when investing in Artificial Intelligence.
Organizational Efficiency as an Outcome, Not an Isolated Goal
One point the MIT researchers emphasize throughout the study is that organizational efficiency isn’t a number you chase directly. It’s an outcome that emerges when processes are well-structured, the right tools are in the right places, and people are focused on the activities where they truly make a difference. Artificial Intelligence applied across task chains creates the conditions for that efficiency to show up naturally, rather than being forced through cost-cutting targets or headcount reduction.
Another interesting aspect the study raises is the question of measurement. When AI is applied to isolated tasks, measuring the impact is relatively straightforward: the time to write an email dropped from 10 minutes to 3. But when automation acts on an entire flow, the relevant metrics change. What matters is no longer the time at each step, but rather:
- The total end-to-end process time
- The rework rate and errors that reach the end customer
- The ability to scale operations without increasing headcount at the same rate
- The reduction in handoffs between humans and machines throughout the flow
These are business metrics, not tool metrics, and they require a different conversation between technology and operations leadership.
Redesign the Flow, Don’t Just Plug In the Tool
Shahidi summed up the study’s main takeaway pretty clearly: it’s not about how to introduce AI into the existing workflow, but about how to redesign the workflow in a way that’s more compatible with AI.
Organizations that treat Artificial Intelligence as a plug-in — something layered on top of current processes — tend to see incremental improvements. Those gains are valid, but they rarely change the game. The ones that rethink how work is structured — grouping AI-compatible tasks, reducing unnecessary handoffs, and redesigning complete workflows — are far better positioned to capture the technology’s transformative potential.
At the end of the day, what the MIT research delivers is a solid conceptual framework for organizations that want to stop using Artificial Intelligence as a set of shortcuts and start using it as a real lever for transformation. The difference between the two approaches isn’t in the technology itself — which in many cases is exactly the same. It’s in how companies look at their own processes, identify the connections between steps, and decide where and how automation can make work flow better from end to end. That shift in perspective, seemingly simple, is what separates the use cases that impress in presentations from the ones that actually change business outcomes. 🎯
