AI in Automation: A Growing Influence on Business Processes

AI is going through sectors and processes and transforming them as it goes along. 

A famous example is ChatGPT and its effect on content creation. However, aside from the headline-grabbing applications, AI is having a keen effect on automation and its use across various industries, driving efficiency, reducing costs and enabling new applications that redefine how businesses operate. 

What was once a system capable of automating processes in a preprogrammed and rigid fashion is now able to handle complex, data-driven tasks previously only capable with a human operator, with implications stretching from customer service to project management.

This AI landscape presents exciting possibilities if utilised, but comes with its own set of challenges. So how should enterprises looking to add intelligence to their automation processes go about aiming to integrate this?

The current state of AI automation

AI’s role in automation is rapidly expanding as businesses see the potential for increased efficiency and productivity. 

Urmila Kukreja, Director of Product Management at Smartsheet, explains how we are experiencing a “significant moment for automation in our industry as generative AI tools and technologies rapidly evolve.

“Automation itself is not new,” Urmila continues, “but with the help of AI, automation workflow building has levelled up by adding intelligence to tasks and processes.

Smartsheet, for example, utilises AI to streamline repetitive tasks, such as categorising incoming feature requests, which reduces manual intervention and increases overall efficiency. 

The drive towards automating these repetitive processes is reshaping sectors that require precision, such as human resources and product management, by accelerating task completion and reducing errors.

Incorporating AI into automation means businesses can leverage its predictive power and adaptability. 

Nick Magnuson, Head of AI at Qlik, highlights that many organisations are “under pressure to incorporate AI”, as automation is now central to industries like healthcare, manufacturing and sales. 

“The current landscape is full of hope, scepticism and high expectations — fundamentally altering how businesses utilise technology,” explains Nick.

AI-powered automation helps optimise human workflows, supporting decisions that improve productivity and reduce operational costs. 

This shift is not just enhancing the speed of business processes; it is fostering a transformative approach that elevates routine operations to strategic initiatives, aligning with business goals for agility and scalability.

AI automation has already proved invaluable in communications, particularly in industries like healthcare and finance. 

Luiz Domingos, CTO at Mitel, emphasises that AI-driven automation tools, such as intelligent assistants and real-time transcription services, are “delivering fast, efficient and contextually accurate customer service.

“AI automation tools include intelligent assistant chat- and speech-bots, automated speech recognition (ASR), Text to Speech (TTS), real-time transcription, summarisation, sentiment analysis and virtual contact centre agents,” says Luiz.

The adoption of AI in automation is creating new possibilities, enabling faster responses, personalised interactions and streamlined workflows, which enhance user experiences while optimising operational costs. 

Challenges in adoption

Yet despite its benefits, it is this journey to integrate AI into automation processes that presents distinct challenges, particularly around data quality, governance and trust. 

Data quality is key as it is the fuel that feeds the process of automation. However, getting the amounts of data needed, in a clean and structured way, is a task in itself (and one that automation would ironically help). 

Nick points out that “automation on top of low-quality, ungoverned data is an untenable proposition”, stressing that poor data management can stall AI projects. 

“High consequence, low confidence use cases are obviously not ideal proving grounds for use of automation in AI,” he says. “Conversely, high confidence and high frequency use cases may prove better situated to benefit from automated processes.”

In sectors like manufacturing and finance, where the stakes are high, companies must ensure the robustness of their data infrastructure before implementing AI in automation. 

Ethical and regulatory compliance also complicate the adoption of AI in automation, particularly in sectors handling sensitive data. 

One way to tackle such ethical aspects and thus build trust, as Urmila highlights, isto build trust in AI outcomes: “Challenges in the field include gaining trust in the AI results, understanding areas where a human touch is still needed and evaluating the costs and evolving capabilities of large language models (LLMs) as they continue to develop,” she says. “Explaining and understanding AI results, as well as the ability to govern and monitor AI execution, can go a long way toward building trust.”

Trust in AI automation is necessary for its adoption and transparency is crucial in enabling organisations to rely on these systems. Moreover, data governance and cost considerations remain critical as businesses weigh the benefits of AI against the resource demands of adopting such technology and the regulatory punishments for not complying.

Luiz highlights, however, that this is challenging, with many enterprises operating in hybrid environments, causing compatibility issues due to a mix of premise-based, private and public cloud applications. 

“Organisations must navigate concerns around data privacy and AI, and regulations like GDPR and the AI Act in the EU and the California Consumer Privacy Act (CCPA) in the US will make data handling a compliance issue,” he explains.

As AI becomes more embedded in automation, organisations must navigate these regulatory complexities, ensuring data protection and ethical standards are upheld across all automated processes.

Moving forward to AI-driven automation

The argument for AI in automation is clear. But what is not yet decided is the shape it will take. 

With advances in large language models, Urmila anticipates that automation will move towards optimisation for “higher-quality experience”, which will provide users with more relevant, personalised interactions. 

This evolution will allow AI not only to complete automated tasks but to suggest optimisations within workflows, further enhancing productivity. Small businesses, in particular, stand to benefit from these advances, as they gain access to sophisticated automation tools without requiring expert intervention.

In addition to quality, the concept of ‘human-in-the-loop’ will play a prominent role in the future of automation. Nick stresses the importance of involving human touchpoints in AI processes, noting that organisations need “stepping stone” workflows that empower humans to monitor and adjust autonomous systems. 

This practice is already evident in emerging Agentic AI applications, which allow for real-time adaptations and adjustments in response to complex business challenges. 

And in preparing for the future of AI in automation, Luiz advises that organisations “develop a clear AI strategy” and “focus on proven technology” to drive successful AI adoption. 

This approach includes setting up pilot programs and training employees to work effectively with AI tools. By building a robust foundation for AI-driven automation, companies can mitigate the risks associated with rapid implementation while enhancing employee buy-in and user trust. 

AI-driven automation is becoming integral to business operations, allowing them to improve operations and free up resources to focus on further value-added services. 

Aligning how to implement it with a clear strategy will allow organisations to harness its full potential while fostering a culture of trust and adaptability.

To read the full article in the magazine, click HERE.

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