2023 brings political and economic uncertainty but also technological innovation. As businesses look for ways to ensure they use their resources with maximum efficiency while being mindful of offering great experiences to customers and employees alike, automation will likely take center stage.
As such, we want to show you what automation trends will majorly impact enterprises this year and how businesses can leverage them to create more robust and efficient workflows that will bring long-term benefits.
Wider Adoption of Artificial Intelligence across the Board
Artificial Intelligence (AI) and Machine Learning (ML) have made plenty of headlines this previous year with the release of fantastic apps such as ChatGPT, Stable Diffusion, and Midjourney proving their potential as creative instruments and business tools with great potential.
Still, it’s important to remember that ML’s primary strength is analyzing data and recognizing patterns, which has many applications in today’s business environment. With the even greater interest generated this year in AI, we believe that 2023 will mark a considerable increase in AI tool usage across many industries.
For example, more and more companies aim to automate their financial processes. Gartner reported that 55% of financial functions want a touchless close by 2025. The prospect of generating faster and more accurate reports with less time invested by the team sounds tempting. To reach this goal, however, businesses need to start investing in automation, training bots, and rethinking their processes to leverage their capabilities. 2023 will mark the start of the AI journey for many of these companies.
Artificial intelligence enables businesses to adopt effective predictive maintenance on critical processes in the manufacturing industry. Instead of performing regular check-ups (preventive maintenance) that can tie up maintenance teams or wait for the machinery to fail and then repair it (run-to-fail maintenance), resulting in unplanned downtime, specialized sensors and ML algorithms can predict when equipment is close to failure. This way, production lines have less downtime without overworking maintenance teams.
ML has been used for a few years in medical fields such as healthcare (pathology and radiology) and pharmaceuticals (screening proposed compounds to predict success rate). Still, it’s starting to see adoption in more and more areas:
- Streamlining medication production. Besides using Artificial Intelligence to screen potential compounds to predict their effects, the pharma industry can scape up to also apply it to quality assurance processes and production line analysis. By implementing AI, companies can produce more medicine with greater economic efficiency.
- Diagnosis & personalized patient care. Once trained on medical data, ML algorithms can filter relevant data and offer diagnostic suggestions to doctors, cutting down on repetitive tasks and improving employee satisfaction. Both effects result in a higher quality service to patients, which can be further enhanced by using chatbots – offering a more personal touch without overworking the staff.
- Patient risk identification. Prevention is the best treatment, and Machine Learning can help here too. For example, the risk of adverse effects after surgery, such as infections, can result in further illness, extended hospital stay, or re-hospitalization. The challenge with anticipating these detrimental effects lies in reading large amounts of information and correlating data points to identify risks. Fortunately, this is the kind of task where Artificial Intelligence excels.
Artificial Intelligence algorithms have made great strides, and we’re confident in their growth, but don’t look at them as a silver bullet. The best systems combine the processing power of AI software and human supervision.
ML programs need training and setup. So, using them cost-effectively means rigorous planning and analysis of your current workflows.
Robotic Process Automation Becomes More Accessible
Robotic Process Automation, or RPA for short, has also grown as a market, and it’s expected to reach $20.1 billion by 2030. While RPA solutions are being used to meet a wider variety of challenges, a big trend is a focus on accessibility.
Before going into more detail, we must outline the difference between RPA and AI. Robotic Process Automation is meant to simulate human behavior, working with clearly defined rules to reach expected results. Artificial Intelligence is focused on analyzing information and finding patterns to generate beneficial insights. So in a way, RPA is about doing the work humans don’t want to do, and AI is about doing the work humans can’t do.
RPA software brings value over time by saving companies time previously spent on simple but repetitive tasks. A study by Forrester found that RPA’s return on investment is 97% over three years, after which it starts generating profit.
The only major hurdle is the initial setup, which is why RPA developers and service providers are working on offering more integrations out of the box. The goal is to shorten setup time and start generating client benefits as soon as possible.
Another way the RPA industry pushes the envelope is through more low-code or no-code solutions. The idea is to make automation more accessible by implementing simple drag-and-drop visual interfaces. With the technical requirements lowered, companies interested in RPA can:
- Prepare and deploy bots more easily.
- Scale up their automation projects more quickly.
- Train their workforce faster on how to leverage bots.
Given the labor shortages and inflation companies are dealing with and will continue to face in 2023, easier RPA integration means more businesses can take pressure off their teams. Automating repetitive and generally unengaging tasks means saving time and creating a more engaging and rewarding work environment without sacrificing efficiency.
Another vital component in the increasing accessibility of RPA in 2023 will be process mining, a computer-assisted, data-driven method of analyzing a business’ processes to identify bottlenecks, deviations, and automation opportunities.
Only some workflows are worth automating, and finding the right processes through human-driven workshops runs the risk of missing opportunities due to unavailable data or best-case scenario assumptions.
Process mining leverages enterprise IT platforms’ ability to log all interactions. It generates a wealth of data and then processes it into easy-to-understand reports which showcase the biggest blockers in day-to-day activities. As a result, these disruptive processes become prime candidates for automation or even rethinking.
While on the topic of process mining, we want to address another important trend for 2023, one that may have the most widespread impact:
End-to-end Automation, aka Hyperautomation
The technologies we mentioned earlier have proven their effectiveness as standalone tools in a company’s arsenal, but what would it look like if we were to combine them all?
The answer is end-to-end automation, made up of:
- A robust data collection engine. You can collect a treasure trove of data through enterprise IT systems that log interactions, natural language processing (NPL) algorithms that understand human communication, and Internet of Things (IoT) sensors embedded in specialized hardware to get reading on physical objects and equipment.
- Artificial Intelligence software to process the information. Process mining is an example of this analysis procedure. Another example of leveraging AI to increase efficiency and innovation is digital twins, virtual copies of physical objects or areas that use real-time data to gather insights and generate improvement suggestions.
- An automation platform. If you only look at the process level, this part of the automation process is the same as any other use case where RPA is employed. Zoom out, though, and the difference becomes apparent – with all workflows analyzed, the most significant automation opportunities often involve several systems and processes and drive cumulative optimization across the whole organization.
A big advantage of hyperautomation is that it creates an environment for continuous improvement and innovation. Constant monitoring gives you an overview of your processes and their efficiency even as the business expands and changes.
As this automation system and its individual components see more use, developers and service providers gain, more insight into the various challenges and pain points clients experience. Then, they can work on addressing them and offering a better automation experience. This process has been happening for years, and we will see impressive progress in 2023 as the market’s need for automation meets solutions based on years of accumulated experience.
Do you have plans to invest in Artificial Intelligence or Robotic Process Automation in 2023? Then, find out how we can help you innovate!