Debunking 3 Common AI Myths

Much of the recent news about artificial intelligence (AI) offers a little fact and a little fiction. Let’s sort one from the other.

Did you know that a few areas of technology spending by businesses have grown during the economic slowdown caused by the ongoing COVID-19 pandemic? Examining what customers are spending money on is a logical strategy as we all look for ways to replace revenues lost to the decline in print volumes and softening demand for hardware and equipment. Numerous analysts report continued strong demand for artificial intelligence among business buyers, but AI-enabled technologies aren’t yet a standard part of many information management reseller’s product lines.

Perhaps you’re one who has been putting it off because you believe one of these three common AI myths. If so, read on to better understand what’s really happening in the world of AI-enabled technology.

Myth 1: All Software is AI

In the past, we all occasionally referred to all software as AI simply because it replaced human effort with automation. However, today’s AI is much more sophisticated.

Some of you who are as old as I am may remember one of the original tech acronyms: GIGO. It stood for Garbage In, Garbage Out and reflected a rule of software for decades. Older software was limited in that it could only execute tasks that were specifically programmed into the code. There was no creativity, no deductive reasoning, no analysis, no conclusive reasoning either — just accepting inputs, executing established steps, and producing outputs, which represents the simplest cognitive functioning. This is not what is meant by today’s use of the term artificial intelligence, but some software manufacturers are using the term AI and hiding behind this older, broader definition.

Today’s AI leverages the dramatic rise in computational power over the last couple of decades and the immediate availability of today’s vast quantities of data. Software developers are able to more quickly develop and test innovative code that solves problems and performs actions that go beyond traditional software design, which could only do exactly what it was coded to do. Now we see artificial intelligence that can sort through giant data sets and recognize patterns and categories in the data, a type of AI known as machine learning. These patterns and categories don’t have to be pre-programmed into the application itself. The AI “learns” as it accepts more and more information, analyzes what it’s receiving and eventually begins to draw conclusions.

Myth 2: AI Will Eliminate Jobs

I’m going to trust you to hang in through the hype on this issue. Alarmists have been postulating job loss due to artificial intelligence for many years. However, there is a compelling pattern to similar past news cycles. During the industrial revolution, the news exclaimed over jobs lost to new farm equipment and manufacturing machinery. And, as computers became a standard part of our offices, the doomsayers again warned of mass unemployment, because technology would take over. Yet, the Bureau of Labor and Statistics reports that nonfarm employment rose from 27.1 million in 1919 to 143.1 million in December 2015.

Don’t buy into stories about job loss! AI allows technology to reduce the human effort required to perform a task, but it works best in controlled settings. Humans still outperform artificial intelligence in higher cognitive functions such as evaluating, analyzing, and conceptualizing as well as creative endeavors such as design, artwork, and writing. In practical terms, AI will replace mundane and repetitive tasks rather than whole jobs in most cases. This allows workers to simply shift toward tasks that require higher-level thinking. Gartner calls this augmented intelligence and explains that it’s a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences. According to McKinsey, only 14% of the global workforce will need to switch occupational categories by 2030 to compensate for this realignment of job tasks. Further good news: this shift creates more meaningful work, so we should see job satisfaction ratings rise as AI becomes more prevalent.

Myth 3: I Don’t Need an AI Product Strategy

As companies who sell technology to businesses, it’s important that we pay attention to fundamental shifts in buyer preferences as they happen, and artificial intelligence is making waves. If you’ve been ignoring AI or avoiding adding these technologies to your product lineup, you’re no longer competing effectively for new business.

According to Markets and Markets, the total market for AI-enabled applications was more than $62 billion in 2020, and its annual growth rate will outpace virtually all market sectors at an impressive 40.2% through 2028. Clearly, customer interest is high. Why? AI can be part of an offering with transformative value. Take a look at what happens when companies invest in AI-driven technologies.

  • They build better products. 35% find that AI enhances R&D leading to faster innovation and next-generation products.
  • Customers love them more. After implementing AI, 75% of companies have seen customer satisfaction scores improve by at least 10%.
  • They make better decisions. 87% of CIOs report either substantial or transformative value to decision-making at their organizations.

Across the board, companies save money. Reducing administrative burden improves job satisfaction, boosts productivity, and leads to better results for employees. Organizations can expect to spend less on operating overhead, analytics, and payroll. In fact, Accenture reports that automating core administrative functions using AI unlocks $15 million in operating income for every 100 full-time employees. That’s $150,000 per person!

It’s less complicated than you think to embed AI in your offerings. Modern artificial intelligence algorithms, typically machine learning, already exist in some information management technologies. For example:

• Many data analytics applications leverage AI to sort through vast quantities of information and locate patterns in the data that may be important to decision-making.
• Some forms processing applications consider more data points than the simple OCR-based recognition of older technologies, resulting in much higher accuracy in the extracted data and fewer exceptions requiring manual handling.

It’s great news, because your organization can stick to its strengths in selling, implementing, and supporting information management.


The time is now! If you haven’t already added AI-enabled products to your lineup, you’re missing a significant opportunity that will gain traction in the years to come. Every day you put this off, you’re falling further behind competitors who are already gaining recognition for helping customers achieve their AI and automation goals

Christina Robbins is Director of Strategic Communications for Digitech Systems LLC, one of the most trusted choices for intelligent information management and business process automation worldwide. Celebrated by industry analysts and insiders as the best enterprise content management and workflow solutions on the market, Digitech Systems has an unsurpassed legacy of accelerating business performance by streamlining digital processes for organizations of any size. For more information visit