The Role of 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 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.

Understanding AI business process automation

AI business process automation (AI BPA) involves using artificial intelligence technologies to automate repetitive tasks and streamline business operations. By integrating AI into traditional automation processes, businesses can enhance efficiency, reduce errors, and make insights-driven decisions.

For example, an AI-driven system can automate customer service inquiries through chatbots, handling common questions without human intervention. 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): uses algorithms to analyze data patterns and make predictions.
• Natural language processing (NLP): allows machines to comprehend and interact with human language, enabling more intuitive and efficient communication.
• Robotic process automation (RPA): mimics human actions to automate routine tasks, such as data entry.
• Data analytics: tools that analyze data to provide insights and support 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 AI's value in automating business processes?

1. Data management and analytics

AI plays a crucial role in transforming how businesses handle data. It automates data entry to generate insights 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 helps in data management:

• It automatically removes duplicates and ensures data quality.
• It analyzes historical data to predict future trends and customer behavior.
• It processes large data sets instantly, delivering actionable insights.

Moreover, AI BPA offers advanced data analytics capabilities that provide insights into operational performance. By analyzing large volumes of data, AI systems can predict outcomes and recommend improvements.

2. 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. These systems reduce the need for human intervention, 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 helps in customer support:

• AI chatbots 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.

3. Sales and marketing

AI-driven automation enables companies to streamline sales and marketing efforts, offering personalized experiences at scale. AI helps analyze customer data, optimize campaigns, and automate repetitive tasks like lead nurturing.

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.

4. Workflow and task automation

As businesses implement AI BPA, they also experience a cultural shift. Teams begin to embrace a more innovative mindset, where technology and human creativity coalesce. This collaboration between AI and human workers fosters a more dynamic work environment, where employees are empowered to contribute to strategic initiatives rather than getting bogged down by routine tasks. Indeed, 72% of workers trust AI to bring value to their work processes.

How AI helps in workflow automation:

• AI 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.
• Real-time tracking of workflow progress ensures tasks are competed on time, allowing managers to identify and resolve issues quickly.

Besides, AI BPA automates 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 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 and low-code solutions boost efficiency:

• Faster development cycles: Teams can deploy applications in weeks instead of months, accelerating digital transformation.
• Cost-effective solutions: Low-code tools reduce reliance on expensive developers, cutting costs significantly.
• Customizability: Businesses can easily adapt workflows and applications to meet evolving needs without having to start from scratch.

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 for business automation: what are the risks?

AI business automation has already started changing workflows upside down. Yet, even high-performing organizations face significant challenges when integrating AI into their business processes. Operational issues, such as monitoring and retraining AI models, and strategic concerns, like establishing a clear AI vision that aligns with business goals, can create roadblocks. Additionally, some companies struggle to obtain the necessary resources or adopt AI best practices. These obstacles, though surmountable, demonstrate that the journey toward full AI adoption is far from straightforward.

While business process automation with AI can enhance productivity and streamline operations, businesses should be aware of the potential risks. Below, we explore some of the key risks associated with AI adoption in BPA:

1. 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 focus on openly sharing these insights to reassure and motivate their employees about AI's role in shaping the future workplace. Investing in reskilling and upskilling programs aimed at helping employees adapt to AI-driven environments can be a good solution.

2. 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. This issue is particularly risky in areas like recruitment, lending, and customer service.

Here are 2 important considerations on the subject:

• Biased training data can lead to unfair outcomes, particularly in sensitive areas like hiring.
• AI models, especially those utilizing machine learning, can be difficult to interpret, creating concerns around accountability and fairness.

What companies can do: focus on transparent AI models and carefully monitor their training data to mitigate bias risks.

3. Security concerns

AI systems can become a target for cyberattacks, where attackers could exploit weaknesses in automated processes or manipulate AI algorithms for malicious purposes.

• 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: prioritize robust cybersecurity strategies, including regular monitoring and updating of AI systems.

4. Lack of AI expertise and vision

For AI business process automation to deliver real value, companies need a well-defined AI strategy that aligns with business goals. However, many organizations lack a clear roadmap or the expertise to fully realize AI's potential.

The truth is that companies implementing AI without a clear understanding of its business value are at risk of delivering ineffective solutions. Without the proper AI expertise, businesses may struggle to scale AI operations effectively.

What companies can do: invest in AI education and training, develop a clear AI strategy, and start with small, scalable projects.

Conclusion

Having the right technology is only part of successful AI implementation; 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 in your business operations, Akveo can help. Our team offers business process automation services, including workflow automation, low-code development, and RPA to ensure you get the most out of automation while minimizing risks. Let’s work together to future-proof your business!