While enterprise automation is not a new phenomenon, the use cases and the adoption rate continue to increase. This is reflected in the global market for business automation, which is projected to grow at a CAGR of 12.2% to reach $19.6 billion by 2026.
As technology evolves, so does business process automation – charting its own path of innovation. Moving on from focusing on ROI obtained from automating individual tasks and simple processes, the industry is now taking a holistic view of automation as a mechanism to achieve business targets and goals in alignment with ever-shifting goalposts, organizational KPIs and executive mandates.
In the past, despite all efforts, over 50% of business transformation projects have failed to achieve the desired outcomes with traditional automation approaches. Automation has lacked a critical element – cognitive AI. As AI advances in leaps and bounds, its infusion into automation has led to the emergence of cognitive automation, which comes with the promise of facilitating committed business outcomes.
In a landscape where adaptability and efficiency are paramount, those businesses collaborating with trusted partners to embrace cognitive automation are the most successful in meeting and exceeding their committed business outcomes.
Introducing cognitive automation
Cognitive automation leverages cognitive AI to understand, interpret and process data in a manner that mimics human awareness and thus replicates the capabilities of human intelligence to make informed decisions. By combining the properties of robotic process automation with AI/ML, generative AI and advanced analytics, cognitive automation aligns itself with overarching business goals over time.
To implement cognitive automation effectively, businesses need to understand what is new and how it differs from previous automation approaches. The table below explains the main differences between conventional and cognitive automation.
| Feature | Conventional automation | Cognitive automation |
|---|---|---|
| Task complexity | Better suited for automating routines that have clear and predefined instructions. | More complex tasks that involve human-like thinking, reasoning and decision-making are possible. |
| Data handling | Relies on structured data and predefined rules to execute tasks. | Uses ML and AI to work with unstructured data and is able to learn from, and adapt to, new data sources. |
| Decision-making | Follows predefined rules and instructions. Does not make independent decisions. | Achieves complex decision-making by analyzing data and identifying patterns, among other factors. |
| Flexibility and adaptability | Is less flexible and requires manual adjustments to adapt to evolving tasks and processes. | Learns and adapts to changing circumstances and is able to handle tasks that were not explicitly programmed. |
But where do businesses implement cognitive automation? The simple answer is: Everywhere! In fact, let’s look at a few common use cases.
| Use cases | Solutions |
|---|---|
| Customer service |
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| Data extraction |
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| Healthcare |
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| Predictive maintenance |
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Due to its nature, cognitive automation inherently generates tangible benefits for businesses, irrespective of where they are applied. Its capability highlights include:
- Handling complex tasks by processing unstructured data and natural language to help businesses automate a wider range of processes
- Improved decision-making, better customer service and more effective resource allocation with enhanced data-driven decisions and predictions
- Enhanced customer engagement with personalized and context-aware interactions
- Scalability without a significant increase in operational costs, which is particularly beneficial for businesses experiencing unplanned growth
- Reduced error rates by automating cognitive tasks, leading to higher-quality outputs and improved compliance
- Enhanced productivity by freeing employees from repetitive and time-consuming tasks
- Cost savings in the long run through improved efficiency, reduced error rates and optimized resource allocation
- Competitive advantage for businesses by staying agile with changes, making faster and more informed decisions and delivering better customer experiences
It is hardly surprising that the global market for cognitive automation is expected to spiral between 2023 and 2030 at a CAGR of 27.8%, valued at $36.63 billion.
Taking the right step forward
The transformative power of cognitive automation is evident in today's fast-paced business landscape. Cognitive automation presents itself as a dynamic and intelligent alternative to conventional automation, with the ability to overcome the limitations of its predecessor and align itself seamlessly with a diverse spectrum of business objectives. This makes it a vital tool for businesses striving to improve competitiveness and agility in an ever-evolving market.
Implementing cognitive automation successfully requires the expertise of a seasoned technology partner: HCLTech has the proven capabilities and track record to help businesses navigate the complexities of this transformative technology and ensure its seamless integration across the organization.
In a landscape where adaptability and efficiency are paramount, those businesses collaborating with trusted partners to embrace cognitive automation are the most successful in meeting and exceeding their committed business outcomes.
