Using AI to enable automations

Companies embarking on their first investments in Artificial Intelligence-led projects aim to use the new technology to automate tasks and processes. Boards seek to measure new AI investments in terms of realising productivity gains.

Heads of operations, similarly, are hoping to use AI to reduce the friction involved in executing everyday work. That frees up expert capacity to get on with new work, which adds more value to customers and ensures the smooth running of the business.

In this piece, we uncover the characteristics which determine  the success or  failure of an AI pilot designed to automate tasks. It does not necessarily lie in the sophistication of the model selected for the job. Instead, it is more about the detailed analysis of the nature of the work that you are trying to automate and the data controls put in place at the outset.

You must understand and map tasks within processes and then , link and cross-check the activities involved.. In other words, through careful analysis of the workflows within a process upfront, it is possible to reliably deliver automations using AI.

What type of work should you automate with AI?

When we look at these sorts of projects for clients, we search for workflows where variability, unstructured inputs, judgment, or exception handling dominate. AI is a better fit than technologies such as Robotic Process Automation (RPA) in several cases.  For example, if the work requires interpretation rather than deterministic ‘yes/no’ type rules, or if exception rates are high.

It’s important to measure and map the ‘cognitive load’ required to handle the manual processes you are hoping to automate. The most valuable opportunities for AI to unlock productivity gains sit within tasks that are both time-consuming and mentally taxing.

It is also important to map real workflows, not idealised ones. Many processes are messy when you begin picking them apart. Break down workflows, task by task. Understand and describe each task, whether it be about discovery, triage, classification, summarisation, data retrieval, recommendation or decision.

The workflows we are ideally looking to automate with AI are characterised by judgement-heavy tasks. These involve ambiguity and accountability. We analyse decision frequencies, outcome variance, reversibility (ability to return to the original state if something goes wrong), and measure risk associated with automated decision-making.

Task mining

To understand how work really happens, analysts use several approaches together. They analyse system logs to spot patterns in how tasks move through systems. This technique is called ‘process and task mining’. Usage data shows where work sits idle or builds up in queues.

Reviewing documents, tickets, and files shows where information is copied, reworked, or passed between people. Observing teams at work reveals informal hand-offs, workarounds, and escalation routes. These are rarely captured in official process maps.

Hidden manual work often emerges at system boundaries where ownership is unclear or tools fail to integrate cleanly. These are often areas where productivity is silently lost. It is where AI can have a disproportionately high positive impact.

Prepare for exceptions

It’s also important to plan for ‘exception paths’. These occur when a standard, automated process encounters an error, ambiguity or data anomaly.

A ‘straight-through’ path handles routine repeatable tasks working with clean data. However, the exception path might manage up to 20 per cent of cases that do not conform to expected rules. This ensures the process does not halt completely while handling these exceptions in rework loops.

With AI projects, exception paths have evolved from simple ‘try-catch’ error handling into intelligent, adaptive and potentially fully autonomous processes.

Building trust

Isolating AI-driven productivity improvements from parallel gains such as new training regimes, tooling upgrades, process improvements, or policy changes requires careful planning. It’s important to recognise that well-designed projects should show productivity gains in just a few weeks.

However, sustained gains depend on adoption levels and confidence-building. The people interacting and overseeing the new system must feel any improvements from its use from their perspective. It is common to have early productivity losses when users are forced into new workflows before trust is established.

Mitigate this by running AI assistance in parallel with existing processes during early phases. Encourage rather than mandate behaviour change until confidence in the new system builds. Clear communication of positive intent, role-specific training and visible company leadership sponsorship are all essential.

Keep humans firmly in the loop, and explicitly define and act on decision boundaries, approval thresholds, and escalation triggers. Human review of new system’s outputs must be a designed-in component of the workflow.

Design your AI assistance to help reduce cognitive load for workers. To this end, the system must be able to surface recommendations with context, confidence signals and clear next actions. Raw model output alone is never good enough in terms of building confidence in new AI systems.

GaiaLens prioritises controllability first, then explainability, and finally trust. Users only trust systems they can override and understand in terms of the decisions and recommendations they are producing.

We build further guardrails into our AI systems by focusing on event-driven architectures and the use of orchestration logic. AI can be used safely inside workflow steps, by keeping orchestration explicit and deterministic, rather than as the controller of an entire process.

Measuring productivity gains

Productivity gains must be real, measurable and attributable. Agree on the metrics for measurement with the AI project sponsor upfront. Establish baselines by using valid measurements. For example, how long does work take now? How much is completed, on average? How often does it need correcting over a specific period of say one month or one quarter?

Metrics will vary by function. In customer service, typical measures include average handling time, first contact resolution, deflection rates, backlog reduction, and agent utilisation levels. In software development, you are likely to be tracking cycle times, throughput, defect escape rate, and the quantity of rework needed.

Summary

Any use of AI to automate processes needs to begin with a thorough understanding of the nature and type of work you are looking to automate. Some processes are much better suited to AI automation than others.

It is important to break processes down into the constituent parts and understand properly how long each process takes pre-automation, as well as how many systems the people involved in the tasks have to draw on and cross check.

Without all this work, it is impossible to accurately calculate the productivity gains from automation. Building AI systems which provide controllability and explainability together engenders vital trust, which drives up adoption.

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GaiaLens began life as an AI platform built for some of the most complex, regulated datasets and frameworks in finance. Today, the company is bringing that experience to the wider enterprise market including the utilities sector. It helps organisations transform fragmented and incomplete data sets into structured, high integrity data they can actually use, whether to serve customers better, meet tightening data governance rules, automate reporting and workflows, or run business processes more efficiently.

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