AI in Business Process Automation: What You Need to Know

Less time, less effort, and less cost — the pillars of today's business. Indeed, companies are constantly looking for ways to achieve more with less. This is where artificial intelligence steps in.

But what happens when you combine business process automation (BPA) with artificial intelligence (AI)? You can expect streamlined operations, fewer manual processes, lower risks of human error, even in complex tasks, and quicker, smarter decision-making. However, there are also potential pitfalls to consider.

In this article, we'll explore how AI is changing the way businesses operate, where it can be most beneficial, and what companies of all sizes need to be aware of. We will also analyze some of the most promising trends to rely on for building the right intelligent automation strategy.

Understanding AI business process automation

AI business process automation (AI BPA) involves using artificial intelligence technologies to automate repetitive tasks and streamline business operations.

Bringing AI into the mix with traditional automation helps businesses work smarter. It cuts down on repetitive tasks, reduces mistakes, and makes it easier to base decisions on real insights. Take customer service, for instance—AI-powered chatbots can step in to answer routine questions, so teams can focus on more complex issues that actually need a human touch. In finance, AI can quickly analyze large datasets for fraud detection, flagging suspicious transactions faster than manual methods.

Key components of AI business automation

Machine learning (ML)

This technology uses algorithms to analyze data patterns, learn from data, and make predictions. The key idea is that ML systems improve automatically through experience, without the need for explicit programming.

ML and AI in business can be applied to predict, optimize operational costs and supply chains through demand forecasting, personalize marketing content based on past behaviors, as well as automate fraud detection based on patterns and data analysis in financial transactions.

Natural language processing (NLP)

Natural language processing enables machines to understand, interpret, and interact with human language in a way that is both meaningful and efficient. This allows for smoother and more intuitive human-computer interactions.

It is used in building AI process automation solutions for:

• sentiment analysis of customer feedback and social media posts;
• optimizing business processes related to working with documents, like contracts or invoices;
• enhancing search functions by allowing users to search using natural language queries.

Apart from this, natural language processing is an important technology for customer service automation.

Robotic process automation (RPA)

RPA, or robotic process automation, uses software bots to handle repetitive, rule-based tasks that people would normally do by hand. These bots copy the way humans interact with digital tools—clicking, typing, moving files—just faster and with fewer mistakes.

Data analytics

Data analytics involves using tools and techniques to process, interpret, and visualize large volumes of data. Data analysis and insights help businesses make informed decisions, optimize operations, perform complex tasks without wasting resources, and predict future trends.

Such AI technologies also play a crucial role in predictive analytics and reducing operational costs.

Together, all these technologies create a powerful ecosystem that can drive intelligent automation across all aspects of a business, from customer service and marketing to operations and decision-making.

AI business process automation at its best: key areas for maximum impact

After nearly two years of enthusiasm for generative AI, businesses are transitioning beyond the initial “excitement” to focus on what truly matters: harnessing this technology to generate value. Consequently, 71% of organizations are either experimenting with or increasing the adoption of generative AI.

Meanwhile, 67% of AI decision-makers say their organization plans to increase investment in generative AI in the coming year.

What key areas should be considered for maximizing the value of AI business process automation?

Data management and analytics

AI plays a crucial role in transforming business processes powered by data. AI automation can revolutionize a lot of tasks, from data entry to insight generation in real time.

For instance, machine learning algorithms can “sift” through customer data to identify purchasing trends, predict future demand, and optimize data management.

Check out how our low-code engineers built a Retool billing tool to tackle the complexities of hierarchical license management. The solution includes enhanced data management, automated reporting, and dashboard features. Learn more about the case study.

How AI Business Process Automation helps in data management:

• It automatically removes duplicates and ensures data quality.
• AI-driven automation systems can analyze historical data to predict future trends and customer behavior.
• It processes large data sets instantly, delivering actionable insights.

On top of that, using AI in automation gives businesses access to powerful data analysis. Instead of just collecting numbers, AI can sift through huge amounts of information, spot patterns, make predictions, and even suggest ways to improve how things run day to day.

Customer support

AI-powered chatbots and virtual assistants have changed customer service by enabling faster and more accurate responses to customer queries. They deliver immediate responses, aligning with the expectations of 82% of consumers who seek instant resolution to their issues.

Such AI process automation systems reduce the need for human intervention and optimize processes by offering seamless support 24/7. Gartner projects that by 2025, 80% of support teams will incorporate generative AI to boost agent productivity and enhance the customer experience.

How AI Process Automation helps in customer support:

• By implementing AI chatbots, businesses can handle customer inquiries instantly, reducing response time.
• AI learns from customer data to provide personalized support, anticipating customer needs.
• Automating routine tasks allows companies to reduce customer service costs.

Sales and marketing

AI-powered automation makes it easier for companies to fine-tune their sales and marketing. It helps teams tailor customer experiences for different audiences, take care of routine tasks like lead follow-ups, and make sense of all the customer data so campaigns hit the mark more often.

How AI helps in sales and marketing:

• It prioritizes leads based on their likelihood to convert, helping sales teams focus on high-value opportunities.
• AI tailors marketing messages based on individual customer behavior and preferences.
• From sending follow-up emails to managing social media campaigns, AI reduces manual work, allowing teams to focus on strategy.

If you want to learn more about how Akveo developed solutions to automate lead generation processes for a venture capital company, read this case study.

Workflow and task automation

Changing how everyday tasks get done often brings a shift in the way teams work together. People start finding new ways to solve problems and think more creatively. With less time spent on boring, repetitive work, there’s more space to focus on the things that actually matter—like coming up with ideas, improving processes, and working on long-term goals. Indeed, 72% of workers trust AI to bring value to their work processes.

How AI helps in Business automation:

• AI automation software can route documents and requests for approval, eliminating bottlenecks in processes like procurement or contract management.
• It helps allocate tasks based on employee availability and skill, improving resource utilization.
• Being able to track progress as it happens helps make sure everything stays on schedule—and if something goes off track, managers can spot it early and step in before it becomes a bigger problem.

Besides, AI process automation streamlines repetitive and mundane tasks, allowing employees to focus on higher-value and creativity-driven activities. For instance, AI can undertake data entry, process invoices, or respond to customer queries with chatbots.

Low-code development for rapid process automation

How can businesses build custom applications without deep technical skills? Low-code development platforms make this possible by enabling companies to design tailored solutions quickly and efficiently.

These platforms empower teams to develop applications with minimal (or no) coding. These solutions can be a top option for quickly creating automation solutions that can be customized to the specific needs of your company.

How AI process automation and low-code solutions boost efficiency:

• Build faster: Instead of waiting months, teams can get apps up and running in just a few weeks—speeding up progress across the board.
• Save money: These tools help cut down on the need for high-cost development, making projects more budget-friendly.
• Easy to tweak: Workflows and apps can be adjusted as things change, without having to rebuild everything from the ground up.

The combination of low-code development with artificial intelligence capabilities allows organizations to innovate faster and automate processes even with limited technical resources. What's more, by leveraging low-code/no-code solutions, organizations can empower non-technical staff to contribute to the automation process, increasing agility and reducing reliance on IT departments.

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AI in business automation: What are the risks?

Smarter tools are already shaking up the way companies work—but even the most efficient teams can hit bumps when trying to bring new tech into their processes. Some of the biggest challenges are behind the scenes, like keeping systems running smoothly or making sure everything lines up with the company’s bigger goals.

Getting new systems in place isn’t always easy. Some teams are still figuring things out, and others just don’t have the time or resources to fully dive in. These bumps in the road are normal, but they show that change doesn’t happen overnight.

Even so, streamlining how work gets done can really pay off—freeing up time and helping people focus on the things that move the business forward. That said, it’s worth being mindful of the downsides too. Here are a few common challenges companies should be aware of.

Job displacement

One of the most commonly discussed risks of AI automation for business is the potential loss of jobs. Approximately 60% of jobs may be impacted by AI. And some employees are worried. PwC's annual global workforce survey reveals that 30% of employees worry their jobs may be replaced by technology in the coming years.

What should be considered:

Impact on lower-skilled jobs

Positions in industries such as manufacturing, logistics, and customer service are especially vulnerable to automation, leading to workforce anxiety.

New roles emerging

While AI might reduce certain roles, it also opens up opportunities for workers to shift to more value-driven tasks. Forrester predicts that by 2030, just 1.5% of jobs will be lost due to generative AI, while 6.9% will be impacted by it.

What companies can do:

Leaders should be open about what’s changing and why—it helps build trust and keeps people motivated. One way to support teams through the shift is by offering training that helps them grow their skills and feel confident working with new tools.

Bias and ethical concerns

AI systems are only as effective as the quality of the data they are trained on. If an AI model is trained with “false” data, it can perpetuate or even amplify biases in decision-making. Problems can really show up in areas like hiring, lending, and customer service—where the stakes are high and fairness matters.

There are a couple of things worth thinking about:

• If the information used to guide decisions is already biased, the outcomes can be unfair—especially when it affects who gets hired.
• Some systems are so complicated that it’s hard to explain how decisions are made, which makes it tricky to know who’s responsible if something goes wrong.

What companies can do:

Businesses should focus on transparent AI models and carefully monitor their training data to mitigate bias risks.

Data security concerns

AI can be vulnerable to cyberattacks—hackers might try to mess with automated processes or twist the way decisions are made for their own gain.

• Data breaches: AI systems often handle vast amounts of sensitive data, making them attractive targets for hackers.
• Model manipulation: attackers could attempt to manipulate AI models to alter business outcomes or compromise system integrity.

What companies can do to ensure data security:

Companies should focus on strong cybersecurity practices—like keeping systems updated and regularly checking for anything suspicious—to stay ahead of potential threats.

Lack of expertise and vision for AI process automation

To really get value from automation, companies need a clear plan that ties back to what the business is trying to achieve. But the reality is, many teams jump in without a solid strategy or the right know-how to make it work.

When there’s no clear direction, efforts can fall flat—tools get put in place without a real purpose, and things don’t scale the way they should. Without the right experience behind it, it’s easy to end up with solutions that look good on paper but don’t actually solve the problems they were meant to fix.

What companies can do:

It is recommended to invest in AI education and training, develop a clear AI process automation strategy, and start with small, scalable projects.

What is the future of AI in business process automation?

Business automation is one of the most critical tasks for organizations aiming to stay competitive in a fast-evolving digital economy. Today, there are a lot of advanced AI process automation solutions in place. But does it mean that AI tools have already reached the final point of their development? Absolutely not. Tech progress is ongoing, and the trend for AI-powered automation is just gaining momentum.

Let's take a closer look at the tendencies that are shaping the future of AI automation.

Hyperautomation

AI is driving hyperautomation. This concept presupposes automating not just simple tasks but whole complex business processes and workflows end-to-end. Companies won't just automate isolated tasks anymore, as it is not always feasible and efficient. They will focus on connecting AI, robotic process automation, and low-code platforms to automate entire operations across all business functions.

Personalized automation for higher customer satisfaction

Automation isn’t something you can just copy and paste across every business. The tools will adjust on the fly, shaping workflows to fit what’s actually happening in the moment. For instance, customers with different needs might get different responses, onboarding steps, or support experiences—automatically. It’s a smarter way of working that not only helps things run more smoothly but also makes the experience better for customers.

Democratization of automation

User-friendly AI process automation platforms powered by machine learning and natural language processing will allow non-technical experts to automate parts of their work without relying on IT teams. This trend is mainly driven by the growing popularity of the low-code approach and will massively speed up innovation inside companies.

Self-healing processes

Traditional automation systems greatly depend on human intervention. If any complex processes are not executed in the right way, IT specialists (or, in some cases, citizen developers) need to introduce updates. Intelligent process automation systems will monitor automated workflows, detect when something breaks, and successfully self-correct them without human intervention.

In other words, AI-powered process automation and business process management will be more "self-sufficient". Machine learning and AI tools will get insights from the running business processes and improve the workflows for better operational efficiency.

IoT and edge computing integration

In areas like manufacturing, logistics, and energy, connected devices are already sending in real-time data that helps make smarter decisions. This kind of info is key—it gives automation systems exactly what they need to keep things running smoothly, spot issues early, and improve how everything works day to day.

Automated systems will make decisions instantly at the "edge", very close to the source of data.

Ethics and governance

Business automation with AI tools is getting smarter. In this context, companies will need strong frameworks to ensure AI-powered automation systems operate transparently, avoid biases, comply with regulations, and remain auditable.

Conclusion

Having the right technology is only part of the successful introduction of AI technologies. It's just as crucial to have the right people and processes in place. It is essential to balance technological advancement with human oversight, strategic planning, and ethical considerations.

If you're looking to implement AI process automation technologies in your business operations, Akveo can help. Our team offers business process automation services, including workflow automation, low-code development, and robotic process automation to ensure you get the most out of digital transformation while minimizing risks.

Let's work together to future-proof your business!

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