While traditional robotic process automation (RPA) has often fallen short of expectations, intelligent process automation (IPA) is transforming business and serving as the foundation for AI at scale.
This is according to Harpreet Randhawa, Head of Capabilities and Innovation at Exaze, an IT consulting and solutions company.
Randhawa says that most organisations that have experimented with process automation find that the technology works exactly as promised, but that the results fall short of expectations.
“Bots ran their scripts, tasks were completed faster and costs came down on paper. But the underlying processes remained fragile, the exceptions still required human intervention and the hoped-for transformation never quite arrived,” he says. “This is the central challenge with traditional RPA: it automates what exists rather than improving it. It is efficient at executing repetitive, rule-based tasks in stable environments, but brittle when processes change, when data is unstructured or when genuine judgment is required. For many businesses, the result is an automation estate that demands constant maintenance and delivers diminishing returns.”
Traditional automation shortcomings
Randhawa says Exaze regularly speaks to customers who have invested significantly in first-generation automation programmes and are now grappling with their limitations.
The most common issues include:
- Process fragility: Bots built on brittle screen-scraping logic break every time an interface changes, creating ongoing maintenance overhead that erodes the original cost saving.
- No exception handling: Rule-based automation cannot cope with the edge cases and exceptions that inevitably make up a significant proportion of real-world process volume.
- Siloed implementation: Automation deployed in isolation, without integrating into broader operational workflows, creates new handoff problems rather than solving existing ones.
- Compliance risk: In regulated sectors like banking and insurance, automation without embedded audit trails, controls and governance frameworks introduces risk rather than reducing it.
Evolution to IPA
Randhawa says Exaze makes a deliberate distinction between automation and IPA.
“The difference is not simply one of technological sophistication – it is a fundamentally different philosophy about what automation should achieve,” he explains. “Traditional RPA replaces human effort. IPA augments human capability. The outcomes are different, and so is the long-term value.”
IPA combines RPA with artificial intelligence, machine learning and natural language processing to create an ever-evolving balance of human and machine collaboration. Rather than simply scripting existing processes, IPA starts with a rigorous analysis of what the process is actually trying to achieve, and then engineers a solution that automates what should be automated, augments where human judgment adds value, and surfaces the right information at the right moment to support faster, better decisions.
Randhawa says Exaze’s IPA practice is built around a principle it applies across all of its engagements: “We only take on automation work if we are confident we can add genuine value. That means we will not automate a broken process – we will first help the customer understand whether the process itself needs to change. We believe our role is to solve the customer’s actual problem. Sometimes that means building an automation solution. Sometimes it means telling them they’re asking the wrong question.”
The Exaze approach
In practice, Exaze’s IPA engagements typically begin with an AI strategy and advisory phase: a structured assessment of the customer’s current processes, a mapping of high-value automation and AI opportunities, and the construction of a prioritised roadmap aligned to business objectives. This phase prevents the most common failure mode in automation programmes – automating the wrong things.
“From there, we deploy a combination of RPA, AI-assisted workflows and natural language processing to address the full process spectrum – including the unstructured data and exception handling that traditional automation cannot touch,” Randhawa says. “Our intelligent virtual assistant capability, for example, uses NLP to handle customer and internal interactions across multiple languages, reducing manual query resolution while improving the quality of the customer experience.”
Results that matter
Exaze’s IPA practice serves customers across banking, insurance, healthcare, retail, hospitality and travel – sectors where process efficiency directly impacts revenue, compliance and customer retention. The outcomes and practice targets are not theoretical: reduced manual cycle times, lower operational risk, faster time-to-market for new products and services, and measurable improvement in customer experience metrics.
“In financial services, for instance, IPA is enabling customers to automate complex document-processing workflows – from onboarding and claims processing to reconciliation and reporting – with AI-driven exception handling that keeps straight-through processing rates high without sacrificing accuracy or audit integrity,” he says. “The organisations getting the most value from automation are the ones that treat it as a strategic capability, not a cost-cutting exercise.
“As AI continues to mature, the boundary between process automation and intelligent decision support is blurring. The organisations that invest now in building the right IPA foundations – clean process design, integrated data flows, robust governance – will be the ones best positioned to deploy the next generation of AI capabilities at scale. At Exaze, that is the conversation we are having with our customers today,” Randhawa concludes.
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