Artificial Intelligence
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Business process automation is a multi-billion-dollar industry where new services crop up every day under the “automation” heading. Nearly 20% of U.S. enterprises use some form of this automation, and almost all refer to it as AI, but that’s leading some to complain that they aren’t getting access to all the miracles promised by smarter services.

Unfortunately, the boom in offerings hasn’t always come with an equally large outpouring of explanations about the differences in AI, machine learning, process automation, and much more. Two big camps are fighting for business investment right now — AI and RPA — so I thought it might be good to share a little bit about what sets the two apart and how you can make a quick determination about what your business needs.

The good news is that discovering the difference will make it easier to set expectations and budgets when you go for the all-important executive buy-in for your next project.

AI for Your Business

Artificial intelligence is exploding in the business space. You can find AI tools for just about any solution you use or activity your business performs — this even extends to services like Grammarly that can help keep your internal and external content error-free. There are also other low-impact solutions that can manage internal meetings or answer customer questions via chatbots.

New tools that I’ve seen being adopted by companies of all sizes are lead-generation tools. These are moving beyond data scraping and are now starting to review your past leads for what was a successful pitch and then ranking current leads on likelihood success. They also pull in a variety of existing contact and personal data to provide recommendations for how to approach these leads.

There’s also a slew of industry-specific tools that you might want to investigate for your business. Progress’ DataRPM has tools that can reduce downtime for manufacturing equipment by as much as 90%, while WorkFusion has healthcare tools that can reduce admin times for claims processing by up to 80%.

AI for business is like having another team member who focuses on the reasons behind a task (while often undertaking that task) to optimize your efforts. The other end of the spectrum is robotic process automation (RPA) that simply automates a task and needs you to generate any business intelligence from the results.

Why You Might Consider RPA

Robotic process automation provides a brief, controlled entrance to automation and small forms of AI that most of your customers, end-users, and staff feel comfortable with — most of them are already interacting with and using RPA even if they don’t know it.

RPA is a tool that can automate small steps in ensuring your billing is correct before it goes out to a customer or helps manage your database and other activities. Some marketing firms are starting to use RPA for screen scraping with free tools that learn what to do by watching you, so there’s no coding needed.

At its core, RPA mimics the activities that your team does via software and systems, automating tasks much in the way of the chatbots you use or have seen pop up on websites that come with a few predefined questions and answers. Estimates on efficiency improvements vary, but the middle is around a 50% to 60% bump.

The Choice

The comparison here is if you want to automate tasks, usually at a lower cost, or if you want a system that can be engineered to learn about your business and start delivering business insights.

AI is a learning tool that can continue to optimize and work faster while reducing errors and potentially moving into new locations. Machine learning can power it to advance and learn based on what you say are correct and incorrect items — such as discovering if an invoice is potentially wrong based on customer business.

RPA is smart when you’re looking to automate very manual processes. It typically costs less to implement but is more limited in its capabilities. Thinking about the same invoice, RPA can be used to check and make sure the invoice number and totals are correct, where it can flag math that doesn’t add up — it won’t always be able to flag when a customer’s order “looks wrong” based on past experience.

It can often be a discussion about cost as well as complexity levels. A nice benefit of starting with RPA is that you’ll have the right conversations and create a baseline understanding that can make advanced AI implementation more successful, especially if you’re diligent with change management requirements.

How to Get Started Right

Whether you’re looking into a complex AI rollout or a smaller RPA push, success comes from matching the need to the solution and the right state in your current digital transformation bandwidth.

Here are a few steps to help begin a process of adopting the right automation and other tools in the digital world:

  1. Nominate a point-of-contact to shepherd the pilot and secure buy-in from executives and all involved teams, especially your IT and the business unit who will use or rely on the automation.
  2. Create a change management plan that looks at the current efforts as well as what tools and people will be impacted.
  3. Build out the infrastructure side of your strategy and explore methodologies that support the need, desire, and your existing hardware and software.
  4. Flesh out reasoning and needs to match your model. Operational models and control requirements will help you get the right solution and vendor for your needs.
  5. Find out what you need to perform a robust cost-benefit analysis and include this in your RFPs and initial planning. This way, you’re prepared and ready to implement a solution with all the right people, processes, budgets, and company considerations in place.

Whether you’re choosing RPA or AI, a dedicated team who can flesh out these steps is your best chance for success. Let the scope of the project and your existing operations guide implementation and more, and you’ll put your company in its best position for top ROI with automation.