Automation and AI: Challenges and Opportunities

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Businesses across the globe are fascinated with the idea of AI and automation because this advanced technology promises operational efficiency, enhanced processes, and substantial cost savings. However, AI and its allied technologies have also created uncertainties, confusion, and doubts about the human capability for adopting, deploying, and executing these magical systems in actual business situations — simply because the business leaders and operators are still all humans.

Today, it is widely acknowledged that automation and AI technologies will gradually transform the global workplace, with intelligent machines performing human tasks in some cases and aiding the human in other cases. The presence of robotic machines in the workplace will ultimately increase efficiency and reduce costs. As a result, many human occupations will disappear, while others will adapt to technology-enabled roles.

A European Commission Report on AI states that the:

“Global spending on robots will be USD 188 billion in 2020, up from less than half that amount in 2016. By 2025, the worldwide AI market is forecasted to grow to USD 59 billion, a significant increase from the USD 1.8 billion spent in 2016.”

The Towards Data Science article Artificial Intelligence Opportunities & Challenges in Businesses, states:

  • Accenture has confirmed that AI technology has the potential to increase business productivity up to 40 percent
  • The number of AI startups has grown 14 times between 2000 and 2019

Although businesses have shown a recent trend of hiring AI developers at a breakneck speed to fulfill their in-house automation needs, few understand the fundamental challenges that this technology brings with it. As a result, the “AI comfort zone” is still missing in enterprise business circles, and business operators are still doubtful about the cost benefits associated with AI.

The Current Status of Automation: Year 2020

Everywhere you look today, you come across automated machines or systems driven by powerful computers, multi-channel data, and very smart algorithms. The modern society is grappling with chat bots, PDAs, self-driving vehicles on roads, and automated check-outs in grocery stores.

From automated healthcare assistants to sensor-driven devices, there is no industry sector untouched by AI. Artificial Intelligence Applications: Revolutionizing Data Management discusses how advanced automated technologies are transforming Data Management.

A Forbes Council post on AI and business automation reveals the following:

  • Robotic process automation now matches or exceeds human performance levels across business processes.
  • Companies should delegate “tedious, repetitive tasks to RPA bots,” so that human brains can focus on important challenges.
  • Companies must focus on developing the right “culture” around AI and automation technology implementations to aid large-scale adoption.
  • Right now, scaling use cases is a problem, which may be linked to an absence of the right technology “culture.”
  • A study from Goldsmiths, University of London, reported that “72% of 4,000 workers surveyed across North America, the United Kingdom, Japan and India” believe that AI and automation will enhance their work performance.

Impact of AI

The Gartner Hype Cycle for Artificial Intelligence reveals how AI is impacting global businesses as newer, allied technologies like augmented intelligence, edge AI, and explainable AI continue to surface. Augmented intelligence is a perfect example of human-machine collaboration (teamwork) that aids “cognitive performance.”

Although Data Governance is still a concern among most business operators, it is widely accepted that augmented intelligence has the capability of emulating the human decision-making process. This trend has opened up a whole new stream of intelligent solutions, with in-built algorithms to replicate or replace human activities via data-driven insights.

The sudden growth of the algorithm economy has given a boost to complementary business models like “AI platform as a service” or “AI Cloud services.” Today, the most viable commercial prospect is “embedded AI” in enterprise systems such as ERM or CRM.

Future of AI, and the Challenges quotes Gartner Director Analyst Peter Krensky:

“Only 40 percent of top performers view artificial intelligence as a gamechanger. So, there’s a lot of greenspace within a lot of different types of organizations, even whole industries that are just dipping their toes into what’s possible with machine learning.”

The article paraphrases Krensky, saying he believes that most organizations are making huge investments instead of choosing “cheaper alternatives like pre-trained models and cloud infrastructure, when the results would be similar.” Thus, the AI benefits are not fully realized from costly implementations.

Secondly, the skill-development challenge is probably the most formidable barrier to wide-scale AI adoption in enterprises. In many cases, in-house AI or ML projects are driven by data engineers when in reality, diverse skills like Data Science, ML, and domain expertise are also equally critical to the success of a project.

Gartner’s observations reported in theAI Hype Cycle may be summed up as:

  • Organizations need to find cheaper alternatives to costly, in-house AI setups to realize the full benefits of process enhancements, performance efficiency, and cost savings.
  • The AI or ML project teams should be more well-rounded with a variety of talents rather than Data Engineers running the show.

Business Automation in 2020: Predictions by Gartner and Forrester

This Analytics Insights article takes a look at predictions made by Gartner and Forrester about the status of business automation in 2020 from three reports: Predictions 2020: Automation by Forrester; Beware the Automation Paradox by Forrester; and 2020 Technology Trends by Gartner.

Here’s a rundown on some of the major predictions:

  • According to Forrester, over a “million knowledge-work jobs” are likely to be replaced by robots, automated systems, virtual agents, and chat bots in 2020.
  • Forrester also states that “automation in untrained hands” can be hazardous
  • Another important observation made by Forrester is that 80 percent of enterprises are threatened by the prospect of hyper-automation
  • According to Gartner, physical devices, in conjunction with AI technology (autonomous things) will replace human functions in 2020
  • Both Forrester and Gartner report that AI and automation will create new jobs. The European Centre for the Development of Vocational Training (CEDEFOP) predicts that “between 2016 and 2030 there will be over 151 million job openings, with 91% being created due to replacement needs and the remaining 9% due to new job openings.”

Forbes, on the other hand, makes its opinions quite clear in Artificial Intelligence: Key Challenges and Opportunities. According to Forbes, here are the key challenges surrounding AI solution implementation today:

  • Data privacy issues and General Data Privacy Regulation (GDPR) and its derivative regulations
  • Limitations of “justifications” in automated decisions, and the role of explainable AI is combating that limitation
  • Next-generation AI, such as transfer learning, active learning, semi-supervised learning and so forth

Biggest Opportunities and Challenges of AI

According to Tesla’s Elon Musk, a futurologist and visionary, “robots and AI will be able to do everything better than us, creating the biggest risk that we face as a civilization.” Here are the biggest opportunities that even the most skeptical human can dream of:

  • AI technology will reverse the declining, labor-productivity growth rate
  • AI automation as business solutions that aid but do not intrude or control
  • AI automation delivering “ethical” decisions. Possible? Maybe with explainable AI
  • AI systems replacing humans in routine tasks
  • AI systems enhancing human efficiency in human-machine team tasks

And here are the biggest challenges facing global businesses:

  • AI technology adoption rates are uneven between countries and across industry sectors. Some countries like U.S. and U.K., and specific sectors like BFSI an automotive are far ahead of others in technology adoption and deployment.
  • Will AI really be cost effective for large businesses with in-house AI development setups?
  • Right training facilities for AI. The author of Robot and I: Future of the Workforce explains why AI training efforts need to be enhanced.
  • Do enterprise businesses with high ambitions have the necessary skilled staff for AI solution development?
  • Are future AI prospects becoming more of a threat than a relief for business leaders and operators?
  • How are privacy laws like GDPR and CCPA affecting AI system deployment and technology implementation in actual business scenarios?
  • Language issues in human-machine interactions. Can bots interpret colloquial language?
  • Overcoming gender bias in AI professions.

Closing Remarks

The McKinsey report AI, Automation, and the Future of Work: Ten things to Solve For states that the most noticeable change in future businesses will be “human workers working alongside machines in the workplace.” The biggest cultural benefit you can hope for from widespread AI and automation is close integration of AI, robotics, and automation — giving rise to flat, collaborative organizational structure as opposed to “traditional top-down hierarchal structures.”

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