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In my work with finance, retail and public‑sector teams across Latin America, we’ve watched approval cycles shorten from days to minutes—quiet proof that ‘hyperautomation’ is already here. Ideas turn into shipped products faster than we update PowerPoints, because tasks we once called ‘busy‑work’ are now handled by AI‑powered digital coworkers.
Gartner summarises hyperautomation neatly: “a business‑driven, disciplined approach to rapidly identify, vet and automate as many business and IT processes as possible”. However, with many companies looking to automate wherever they can, it is necessary to take a step back and consider the risks as much as the benefits.
Today, let’s delve into what hyperautomation is, what it is doing for businesses, and how it is evolving in 2025.
Defining hyperautomation
Hyperautomation is not necessarily one single thing; it encompasses a range of technologies that usually work together in tandem to automate business processes end-to-end.
Hyperautomation notably differs from standard automation through the intelligence of the orchestrated systems. It is not just about doing things faster but doing them smarter, with an ecosystem of technologies that learn, adapt, and make data-driven decisions. Picture a manufacturing plant where machines do not just repeat tasks, but predict maintenance needs, optimise workflows, and communicate seamlessly across entire production lines.
Back in the 1990s and 2000s, Robotic Process Automation (RPA) as we know it began to emerge, allowing software-driven robots to complete automated, repetitive, rule-based tasks. This was groundbreaking for production lines, especially in the manufacturing sector, but it was not until the 2010s that the concept of intelligent automation (IA) really laid the groundwork for hyperautomation. The idea behind IA was to combine RPA with artificial intelligence (AI) and other advanced technologies to automate more complex processes with the capabilities to learn beyond simple tasks.
With the AI boom of the 2020s, hyperautomation has essentially become the natural evolution of IA, expanding the scope of what can be automated and how things can be automated to an incredible extent. The emergence of Industry 4.0, the rising demands for digital transformation after COVID-19, and the constant pursuit of higher efficiency have driven hyperautomation to gain significant momentum. The 2023‑24 leap came when large‑language models (LLMs) learned to read PDFs, emails and screenshots, ripping out the last bastion manual bottlenecks.
Core technological enablers for hyperautomation
Hyperautomation is the amalgamation of many different technologies and tools that come together to create a stronger system than any one specific part. The core enablers for automation are:
- AI and machine learning – They are integral to hyperautomation, providing what is essentially the cognitive backbone that allows systems to learn, predict, and make adaptive autonomous decisions.
- Robotic Process Automation – RPA, especially in manufacturing environments, provides the mechanical execution capabilities of a hyperautomated process.
- Advanced analytics – These offer deeper data-driven insights, learning, and predictive capabilities.
- Natural language processing – This is essentially the branch of AI that enables systems to compute and generate natural human language. It helps to bridge the gap between humans and machines, as well as allowing non-IT experts to use advanced systems more easily and efficiently.
- Integration platforms – Integration platforms are what they sound like: platforms that ensure seamless interactions between the different technological components of a system.
- Agentic screen automation – Vision‑plus‑LLM models (e.g., Microsoft Copilot Studio ‘Computer Use’) that operate software like a human and keep working even when a button moves.
Agentic UI automation – the next leap
Agentic UI automation lets an AI operate a computer the same way you do: it looks at the live screen with a vision model, reasons over the next best action with a large‑language model, then acts by clicking, typing or scrolling. Microsoft’s new Computer Use mode inside Copilot Studio can already fill web forms or navigate legacy desktop software with no APIs, adjusting if a button moves or a pop‑up appears. OpenAI and Anthropic offers similar “computer‑use” tooling in their platform.
This shift matters because it turns automation from rule‑based scripts (“click X, wait 2 s”) into instruction‑based agents: you can simply say “Log in to my bank, check whether Invoice #71321 was paid, then update the balance in our ERP.” The model understands the instruction, plans the steps and executes them across multiple apps—unlocking tasks that were previously off‑limits to classic RPA.
It's not free of risk, of course, and if you are planning to use it you should watch out for:
- Identity and the Principle of Least Privilege: Agents should run under dedicated service accounts and inherit only the rights and information access they need to perform their duties.
- Audit and replay: Store a screen recording plus a JSON action log for every run.
- Model drift and prompt integrity: Use version prompts and retest flows after each model update to catch subtle behaviour changes.
Handled well, agentic UI automation expands the automation surface from API‑ready systems to virtually any software a human can open, all driven by plain‑language instructions instead of brittle rules.
The business benefits of hyperautomation
With the ability to automate complex processes, hyperautomation sets the stage for a new world of cost reduction, improved efficiency, and more. The perks of hyperautomation benefit organisations across many different industries and sectors. From healthcare, banking, and finance to retail and supply chain management, let's take a look at some of what hyperautomation promises for businesses.
Operational efficiency improvements
Perhaps the most well-known benefit of any automated process is the efficiency gains. By automating repetitive, time-consuming tasks, hyperautomation streamlines an organisation's processes, freeing up time and resources for other, more strategic, impactful initiatives requiring a more human touch. According to IBM, a trade consultancy saved 800 hours per analyst after implementing a hyperautomated system.
The time freed from more menial tasks can also allow employees to focus on more fulfilling and meaningful work, boosting productivity, engagement, and overall happiness.
Cost reductions
As a consequence of operational efficiencies, businesses could also expect a significant drop in operational costs. Depending on what is being automated, the streamlined processes could also reduce the potential of costly errors more likely to come from human error.
Hyperautomated systems also have great scalable growth potential. With more efficient processes that can handle increased workloads, organisations could expand without the proportional costs of expansion. Because these new agents can work on any screen (Computer Use), you could retire some bot‑server licences and pay only runtime minutes.
Enhanced agility through data-driven insights
One potential feature of hyperautomation that sets it apart from regular automated processes is the idea that it can learn and adapt to shifting parameters. AI has the capacity to process and analyse huge data sets that could include market trends, requests, company data and more. Coupled with machine learning and RPA, the potential for data-driven automations is there and could revolutionise business as we know it.
This decision-making capacity means that an automated system can adapt to changes rather than being locked into one specific repetitive process. An intelligent system woven throughout an organisational structure can provide unlimited potential and competitive advantages, allowing companies to move quicker than they ever could before.
Of course, this is not an exhaustive list of all the benefits hyperautomation can bring to businesses; the potential benefits are near limitless, and with hyperautomation only in its infancy, who knows what else it will become capable of?
What is next?
We are only at the beginning of what will likely become an AI-driven era. It was only a few years ago that no one had heard of things like ChatGPT, Gemini, or Perplexity, but now they have become an everyday function of many businesses and occupations around the world. Hyperautomation is only at the dawn of its potential and seeing how fast other AI tools have been developing and improving, it feels safe to say that the next evolution of automation is only a stone’s throw away from becoming the norm.
However, as with most things, hyperautomation has its fair share of risks and drawbacks. AI-driven decision-making is not necessarily what every business will want, and it can incur some serious issues if implemented incorrectly. Screen‑native agents are blurring the line between RPA and AI coworkers; every 2026 roadmap should plan for both.
