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As automation becomes increasingly capable, people fear its potential impact: job losses and displacements. While these advancements in technology eliminate the need to perform tasks manually, humans still play a critical role in determining what to do. Despite concerns, we have every reason to be optimistic about AI and automation and their impact on overall productivity and efficiency. Find out how robots can augment the capabilities of today’s workforce and reduce the need for manual processes to a great extent.

Robots streamline day-to-day work, eliminate human error, and create more time to improve the customer experience and the bottom line. Not that long ago, the process of filling orders or assembling a product involved mind-numbing tasks done repeatedly, to the point the worker really didn’t have to think. Building a car, for instance, was essentially a hands-on endeavor. Axles were cut and installed by hand, seat covers were sewed and manually installed and even engines were assembled right there on the assembly line.

Now, manufacturing robots have taken over those and other repetitive tasks that used to be performed by human workers — installing tires, applying paint, and welding frames — allowing automakers to drastically scale up their production while cutting costs and freeing up their workforce to take on higher-value tasks.

But far too many of today’s information workers perform tasks that are the 21^st-century equivalent of installing a tire on a new car repeatedly. Workers at several small and medium-sized businesses, from call centers, and financial services, to other professional service providers, instead of answering more calls or attending to the needs of customers while they have them on the line, are copying and pasting basic information into templates that should be automatically populated.

This rework is monotonous — sending follow-up emails after sales calls, processing invoices from contractors, logging phone calls, and hundreds of other small things — and inefficient use of a seasoned workers’ time. They’re living in the information age, but they’re more analogous to early factory workers.

The natural progression, then, is to automate those tasks just as the manufacturing industry has done. This is where Robotic Process Automation (RPA) comes in, and it’s becoming more widely used than ever. That’s because the tools that used to be limited to those with advanced computer science backgrounds are being democratized thanks to new platforms and infrastructure that make it easier to incorporate RPA into organizations without heavy development efforts.

Call it “citizen development” or whatever you want, but the fact is access to robotic process automation has gained traction in recent years alongside the growth of artificial intelligence. And there are important benefits of this kind of automation technology:

At many companies, there is a ticket for everything. If something in the kitchen breaks a repair ticket is created, the service department comes out to fix it and the ticket is closed after the work is completed.

The process works seamlessly until the end, when someone must go through all of the tickets manually to verify that the job is finished and the ticket can be closed, even without knowing whether or not it was done right or actually completed. RPA can simplify this entire process by automating approvals based on true job completion.

Believe it or not, plenty of work still requires an immense amount of non-value-added copy and paste or re-entry from one system to another. A great example is consumer loan processing. For many banks and credit unions whose core banking and customer relationship management systems do not communicate with each other, employees need to manually look at both systems to pull necessary information that isn’t stored in both. This problem is more common than anyone would imagine. An RPA-based system would replace human involvement in this entire process, pulling the correct data elements every time and feeding everything directly into the system for review and next steps.

Accounting is still a very manual process in many companies. Invoices must be printed, and reviewed and then the proper information from each invoice needs to be entered into the accounting database. This opens up the process to “fat-fingered” data entry mistakes, backlogs, and other issues, all problems that RPA can address better. These improvements free up staff time for workers to spend on solving customer problems and deepening their engagement with your company.

RPA does still have its limitations. As early robotics adopters learned 30+ years ago, these technologies fail when asked to work on complex tasks. It doesn’t work for shades of gray and processes involving a lot of individual decisions, but rather excels on simple tasks or a series of tasks where there is no interpretation required.

Why aren’t all companies doing this already? For one thing, those companies that don’t have solid processes in place already run the risk of automating bad processes and making things worse. That won’t save them anything. The other issue is getting over the inertia of never having embraced robotics technology. Too often, leaders can’t see the benefits of automating redundant tasks until they see them in action.

But RPA use is snowballing. As more and more information businesses embrace automation it’s like opening the floodgates – those that adopt these tools gain a competitive advantage and those that don’t risk falling behind. Those that can automate and spend less time on redundant activity open themselves up to take on higher-value work. They have more capacity to do new things, helping keep their backlog from growing.

In particular, achieving this level of efficiency means cost savings as well as quality improvements. Embracing RPA to streamline the day-to-day work, eliminate human error, and create more time to improve the customer experience will lead to higher quality service, better data to make decisions about productivity and inventory control, and a better bottom line.

Via Robots Aren’t Stealing Jobs — They’re Making Them Better

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Artificial intelligence is shaping digital life and augmenting human capacities all while presenting previously unimagined opportunities. While some see it as a threat to human autonomy, businesses implement AI to operate smarter and faster. As emerging algorithm-driven artificial intelligence (AI) proliferates, are we better off than we are today?

Artificial intelligence has made its way into businesses in nearly every industry, and it’s only continuing to rise in popularity. When utilized correctly, AI can be an incredible productivity booster and provide valuable insights to business leaders. However, many leaders are unsure of the best way to leverage it, and a misdirected AI initiative may cause more harm than good.

If you’re looking to implement artificial intelligence in your business, make sure you go in with a well-considered plan. Below, 13 experts from Forbes Technology Council share common mistakes to watch out for when implementing AI.

Source and Photo Credit: Forbes.com – Members of Forbes Technology Council share common mistakes businesses make when implementing AI initiatives. PHOTOS COURTESY OF THE INDIVIDUAL MEMBERS.

1. Adopting Too Many Tools At Once

The biggest mistake I see tech business owners make when implementing AI is trying to adopt too many different tools at once. AI is a delicate tool that can provide tremendous value to your business, but you have to be attentive and improve it. Some people think AI is “set it and forget it,” so they implement many different AI programs at once and ultimately don’t see positive results. – Thomas Griffin, OptinMonster

2. Not Having A Clear Objective

You must first define the problem you are trying to solve and how you will measure the impact of a solution. I’ve seen too many companies start AI initiatives without clear objectives, hoping to find something. This is usually a recipe for failure. – David Vasko, Rockwell Automation Inc.

3. Not Having A Single Source Of Truth

As data-driven solutions proliferate, the thing that is most needed is a single source of truth that the data can be funneled into. You can then use AI to drive models and build solutions. More often than not, this is forgotten amid the enthusiasm to build AI-driven models and solutions in silos. – Lydia Miller, TATA Consultancy Services

4. Not Analyzing Enough Data

It’s important to remember that AI is only as good as the data it ingests. If you aren’t analyzing a sufficient amount of data with your AI engine or if the data isn’t reliable, the outputs won’t add much value. Another challenge is continuously training the algorithm in production and providing feedback. Doing this manually on huge amounts of data isn’t possible. Automation is needed. – Hed Kovetz, Silverfort

5. Incorrectly Structuring Datasets

The foundation of any successful AI initiative is a well-structured dataset. If you’re an entrepreneur or small business, you likely won’t start off with sets of data large enough to make AI effective. Avoid making the mistake of not structuring your datasets correctly from the beginning, as it will be difficult to do so when you have a sufficient volume of data. – Maddison Long, CloudOps

6. Implementing Siloed Solutions

Some businesses implement siloed AI solutions or proofs of concept before aligning on a broader transformation strategy and cultural readiness. Such an approach significantly limits scalability and return on investment, even if it does not cause direct harm. – Didem Un Ates, Microsoft

7. Not Having The Right Size Team

Most businesses know that AI solutions pack a lot of power, but many still forget about the complexities it brings. Implementing AI requires the right-sized team to always keep your algorithms in their best shape. That’s why many companies prefer to outsource their AI development projects or scale their AI development teams via on-demand staff augmentation services. – Nacho De Marco, BairesDev

8. Not Doing The Necessary Groundwork

AI is often seen as a “silver bullet,” but any AI initiative requires a solid data foundation. Working with Fortune 500 enterprises, we see leadership pushing to implement emerging technology. However, any team needs to go back to basics first and make sure their data is clean and structured in a way that will be useful to the model and real-world application. – Michael Paladino, RevUnit

9. Assuming AI Is A Catch-All Solution

Too many businesses assume that AI is a panacea for all of their problems. Consequently, many of them erroneously jump on generalized tech trends without thinking it out. I’d recommend the opposite approach. Focus on a particular purpose for your AI. Strive to address a specific problem. Then decide if you can employ an off-the-shelf solution or if a custom one is really required. – Marc Fischer, Dogtown Media LLC

10. Misidentifying Both The Problem And The Best Solution

“Automation,” “machine learning” and “AI” are often used interchangeably. Leaders may misidentify the business problem; further, whether an issue can be solved via ML, AI or the best automation candidate within the business is still foggy for many business leaders. Identifying the right business problem, choosing the best tool or platform, inputting the required data sets, and finding the key partners to deliver are the four pillars of AI success. – Soumen Chatterjee, Wipro

11. Implementing AI For Its Own Sake

AI is a technology like any other. We’ve seen many projects fail because businesses wanted to implement “buzzy” technology and used that as a starting point. A more effective way to begin an AI initiative is with a really clear view of the fundamental business problem you’re looking to solve and work backward from that. Focus on a specific solution and use AI in service of that. – Konrad Feldman, Quantcast

12. Implementing Solutions Without Sufficient Data

Lots of data is required for most AI solutions to work well. I also see people assuming that a generic AI solution is appropriate when an industry-specific or use-case-specific solution is the right answer. Implementing AI can be hard, but it can provide some incredible value when it’s done right. – Michael Fulton, Expedient

13. Thinking AI Is ‘One-Size-Fits-All’

AI is not “one-size-fits-all.” It is a buzzword, and too many people think it will make them look advanced on the tech front. Utilizing AI for data-driven projects is where the success is. – Bhavna Juneja, Infinity, a Stamford Technology Company

Source: Forbes.com 13 Common Mistakes That Can Derail Your AI Initiatives