What Tasks Should You Automate With AI?

Why isolated AI improvements fall short

Many organizations treat AI as a plug‑in that speeds up a single activity—such as transcription, sentiment scoring, or data entry. The result is a modest time saving that is often erased by the extra coordination required when a human must pick up the next step. When the handoff cost exceeds the automation gain, the overall process becomes slower, not faster.

That mismatch appears in call centers that automate call routing but still require agents to manually verify caller identity before the next action. The routing algorithm saves a few seconds, yet the verification step adds the same or more time because the agent has to switch context, check records, and re‑enter data. The net effect is a negligible improvement.

MIT Sloan’s "Chaining Tasks" theory

The paper Chaining Tasks, Redefining Work: A Theory of AI Automation argues that AI’s impact is maximized when it handles a chain of interdependent tasks. The researchers model a workflow as a graph where nodes represent tasks and edges capture handoffs. If AI can occupy a contiguous sub‑graph, the coordination cost drops dramatically.

Two key findings emerge:

• Task adjacency matters more than the speed of any single task.
• Even a modest AI performance on each step can outweigh a human‑only process if the chain eliminates multiple handoffs.

In practice, this means looking beyond "what can AI do faster" and asking "which series of tasks can AI do without handing off to a person".

Adjacency in action: lecture‑based teaching vs. tutoring

Consider two educational settings. A lecture professor prepares slides, records video, and uploads the material. All preparation occurs before students engage, so the entire sequence can be automated—script generation, video editing, captioning, and publishing.

A tutor, however, works in real time, answering questions as they arise. The tutor must listen, interpret, respond, and then possibly log the interaction. Because the steps are interleaved with the learner’s input, a single AI module cannot cover the whole flow without frequent human handoffs. The study shows that the same AI tool that excels at slide creation may add little value to live tutoring unless the workflow is re‑engineered to batch questions or pre‑define response templates.

Implications for call‑center operations

Call centers share the tutoring pattern: agents receive a call, verify identity, diagnose the issue, propose a solution, and record the outcome. Each of these five steps is often assigned to a different system—CRM, verification service, knowledge base, and ticketing platform. When AI is inserted into only one system, the agent still toggles between interfaces, preserving the handoff penalty.

By contrast, an end‑to‑end AI chain could ingest the incoming voice, transcribe, extract intent, query the knowledge base, draft a resolution, and update the ticket—all before the agent speaks. The human then only confirms or overrides the suggested reply. This reduces the number of required handoffs from four to one, cutting average handling time (AHT) and lowering error rates.

Identifying task clusters suitable for chaining

Start with a process map. List every task that occurs during a typical interaction and note the system or person responsible. Then, draw arrows to show handoffs. Look for segments where the same data moves from one task to the next without a decision point that requires human judgment.

Three practical filters help narrow the list:

1. Data continuity: Tasks that use the same data fields (e.g., caller ID, account number) are prime candidates.
2. Decision simplicity: If the rule for moving to the next step can be expressed as a clear condition (e.g., confidence > 85%), AI can handle it.
3. Volume: High‑frequency task chains yield the greatest ROI because the time saved multiplies.

Apply the filters to the call‑center map and you will likely surface two to three clusters, such as "call intake → identity verification → eligibility check" or "issue classification → knowledge‑base lookup → response draft".

Redesigning workflows to reduce handoffs

Once clusters are identified, restructure the workflow so the AI engine owns the entire sub‑graph. This often requires three changes:

• Data‑layer consolidation: Store the fields needed for the whole chain in a single record that the AI can read and write.
• Interface simplification: Replace multiple UI screens with a unified dashboard that presents the AI‑generated outcome and a single confirmation button.
• Governance rules: Define when the AI must defer to a human (e.g., low confidence, regulatory flag) and embed that rule in the chain, not at its boundaries.

These adjustments turn a fragmented process into a compact pipeline. The human role shifts from "step‑per" to "oversight manager"—a change that often improves job satisfaction because agents spend less time on rote data entry.

Projected efficiency gains

Research from the MIT team estimates that replacing four handoffs with a single AI‑driven chain can reduce overall processing time by 30‑45 % and cut error propagation by up to 60 %. The table below illustrates a simplified comparison.

Numbers vary by industry, but the pattern is consistent: the more tasks an AI can execute in one uninterrupted flow, the larger the aggregate benefit.

Common pitfalls and how to avoid them

Organizations often rush to label a single AI tool as the solution for an entire department. That mindset creates two problems. First, it leads to fragmented pilots that never scale. Second, it leaves the handoff cost untouched, so the pilot’s KPI improvements disappear when the tool is rolled out broadly.

A disciplined approach mitigates these risks. Begin with a pilot that covers a complete task chain, measure the handoff reduction, and then expand to adjacent chains only after the first proves its ROI. Secure stakeholder buy‑in early by sharing the chain‑level metrics rather than isolated task scores.

Next steps for leaders

1. Map your end‑to‑end processes and flag every handoff.
2. Apply the data‑continuity, decision‑simplicity, and volume filters to isolate clusters.
3. Choose an AI platform that can ingest the full data set for each cluster.
4. Redesign the user interface to present a single AI‑generated outcome per cluster.
5. Define clear governance thresholds for human escalation.
6. Run a pilot on one high‑volume cluster, track AHT, error rate, and agent satisfaction, then iterate.

By treating automation as a workflow‑design problem rather than a technology‑add‑on, you align AI investment with measurable business outcomes.

Evaluating AI-driven workflows or integration tools for your team? Reach out to our specialists to help you evaluate the right platform for your tech stack. For more on workflow redesign, see our guide on optimizing call center workflows. Explore AI task clustering strategies for deeper insights.