The pandemic has forced many businesses to speed up their digital shift. How can artificial intelligence help?
As resources become more scarce and distancing complicates their day-to-day, many SMEs have had to review their business plans and speed up their digital transformation while continuing to serve their clients. As long as they have the right approach, they can improve their efficiency by automating their business processes.
Business process automation; yes, but…
Before the pandemic, many players in the financial sector had already lined up their digital transformation projects by adopting a traditional business process automation (BPA) approach for their business lines. They even applied that to more complex projects.
Those projects always required a long, detailed roadmap and the process came with a great deal of challenges. Performance evaluation was often based on hypotheses and data could vary significantly in the short and long terms, thereby invalidating the target solution.
In the last two years, artificial intelligence solutions have gradually made their way into the BPA process. For example, the banks tried to predict consumer behaviours, plan their next steps, speed up document processing or increase their membership.
Many businesses tried to jump into exceedingly complex and costly transformation processes without seeing tangible results. And promoters and operational teams were getting frustrated. They believed that advanced analytical and AI solutions didn’t fit in with the teams’ day-to-day operations. That’s what stopped small businesses from investing in their digital transformation.
However, the current situation has forced businesses to demand more and to take control of their short-term performance. All of our clients are saying it; when it comes to modernizing their infrastructure, streamlining their business processes and data leveraging, they no longer have a choice. Business process automation is the way to go.
Breaking down business process automation for SMEs
The approach has evolved and businesses now want to put in place automation projects in a reasonable, iterative manner. They want to see every dollar invested generate tangible results in their teams’ day-to-day.
Here are the key success factors:
- An iterative, transferable process-by-process approach;
- A simple roadmap that describes the existing process and what will be automated and why;
- A quick visualization of the end result by the promoter through a feasibility demo that includes business data presentation;
- An AI-enhanced automation solution that’s developed and tested according to the regulations in place and rolled out within a few months.
For example, an insurance company can choose whether they want to start by automating policy processing, follow-ups and updates rather than automating their claims process end-to-end.
In an effort to improving efficiency and reducing human errors, certain clients—including law firms and HR departments in large businesses—are choosing to automate the repetitive portion of their paper documentation verification, research or processing process.
They use optical character recognition (OCR) techniques to improve robotic solutions in order to streamline and accelerate the processes and ensure reliability. This allows the teams to spend more time on value-added tasks.
Digital transformation projects in SMEs are successful when there’s a balance between process-focused automation and data-reliant artificial intelligence. They yield tangible results and can facilitate the operational integration of automated processes.
In short, access to data and how it is interpreted and structured, and automating repetitive human tasks help optimize previously specified business processes. Automation will be logically coded according to applicable business rules. This data leveraging will then make it easier to apply the learning model.
An AI model requiring specific training will then help develop this automation process which will allow for less complex analyses, including document reading and the extraction of relevant information from semi-structured data. The model will accurately decode the information to be extracted using various document types and formats.
Uniting and training the teams to work with the new models
The new models are extremely effective but only if the operational team accepts and welcomes the new players into their day-to-day. They will not be successful without proper training and learning and increased efforts to ensure the solution is fully operational and used and maintained daily.
Everything must be known and included: team learning, knowledge of what the robot can and cannot do, execution programs, error management, new actions from team members, etc. A routine must be established both with the marketing teams and the IT and support teams.
A project of that scale can only work if we look at the big picture. Automation is applied where needed and where its value can be targeted and rapidly delivered. At the same time, it helps integrate more complex technologies such as artificial intelligence—machine learning, natural language processing, interactive capabilities—and makes those involved in the process more efficient. Choosing the type of process to implement is not necessarily complicated; what businesses need to focus on is how to integrate the enhanced automated process and secure buy-in from their team.
Even if businesses had to review their business strategies to survive the pandemic, they had to refocus on what’s important and on getting measurable results. Navigating the digital shift by integrating artificial intelligence into their business processes is an effective, viable solution not only for large businesses, but for SMEs in particular.