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Although for many organizations artificial intelligence may be a work in progress, its rapid spread across the enterprise has led Gartner to predict that by 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI). This in turn will drive an increase in streaming data and analytics infrastructures by up to 500%.

It is likely that the current health crisis has sped up the rate of deployment. During the pandemic and with millions working from home, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) have been able to provide insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. It has also enabled many organizations to continue doing business where they might otherwise have had to slow down or even shut down.

AI and Business Value

New data from the  Epicor Software Corporation contained in their recently launched 2020 Global Growth Index Survey, a report generated using responses and data from business leaders and everyday workers across industries, shows that a majority of businesses surveyed felt AI offered business value.

Epicor is an Austin-based developer of industry-specific business software designed around the needs of manufacturing, distribution, retail and services organizations. The report surveyed 2002 business decision maker across 23 countries and looked at the constantly changing state of growth along with the trends that impact the bottom line. Already, it is showing that AI and machine learning (ML) are having a major impact on the workplace. Some of the findings include:

  • Artificial intelligence is driving growth and proving worth the investment for most organizations, with more than 80% of respondents reporting that AI delivered business value in 2 years or less.
  • Big data analytics is driving growth and proving worth the investment for most organizations, by optimizing operations (42.7%), increasing sales (44%) and improving profitability (45.3%).
  • 90.5% of respondents said the use of big data analytics was driving business growth while 89.7% said artificial intelligence was driving business growth.

Related Article: A Look at the Downsides of Artificial Intelligence

Developing AI

There is no clear-cut winner in AI over the past ten years because the technology is still evolving and is still learning, growing and becoming smarter. Black box-like machine learning approaches might not be sufficient in an increasingly AI-influenced world, Manish Kothari, president of Menlo Park, Calif.-based SRI International and head of SRI Ventures Explainable, said. Emotional AI, he added, is clearly the future — AI that humans can understand, trust, and manage —  and can lead to more effective automation of knowledge work.

Right now, Siri, Amazon, Alexa and systems like that are very good. Siri, the first virtual personal assistant, arose from decades of research into AI. These types of solutions have become good at handling tasks like search as well as performing simple tasks and commands like play this music, turn on the lights, set up an appointment for me. But if somebody is going to do something a little bit more complicated, different technology is required. “While AI systems have become core to many commercial and government applications, they are not able to handle new scenarios that they are not trained on,” he said.  “AI systems today can repeatedly make the same mistakes. Even with retraining, today’s systems are prone to “catastrophic forgetting” when a new item disrupts previously learned knowledge.

Intentional and Inherent AI

AI adoption can be divided into intentional and inherent, Timothy Marsh, an Australia based system designer and business owner, said.  He told us that as solutions providers include AI as part of their capabilities, clients will be using it without even considering it AI. So many businesses that would currently respond to a survey saying they do not use AI but intend to in the future might already be using AI extensively.

Sensei by Adobe is used throughout their creative cloud. Google users depend on it for grammar checking. This sort of AI usage increases the use of streaming data and analytics.

As for businesses adopting AI into their business models in a deliberate way, this will continue to increase. “But do not expect any surprises. Mapping the current curve onto the adoption of similar technologies such as the shift to outsourced cloud infrastructure will give you a good idea of what rate to expect. Look for the inflection point,” he said. “The reason I say don't expect any surprises is because this is in many ways mature technology, being worked on by thousands of teams.” There is a lot of knowledge sharing and collaboration going on. There are many different avenues for R&D, and there's already a steady stream of investment supporting it.”

He also points out that AI does not depend on critical mass to be effective. The adoption rates of technologies that depend on critical mass can be much harder to predict. The fax machine needed enough people to send faxes to before it was worth buying.

That said, just because it is wonderful, cheap, and makes you lots of money, does not mean people will use it. “Change is hard. The way AI impacts a business, and what it requires of a business to be beneficial require change,” he added.

Related Article: How AI Enables Enterprises to Get the Most Out of Their Data

The Role of AGI

While a traction rate of 75% sounds like an incredible leap towards the general adoption of a newly re-energized specialization, the figure is, in fact, probably on the low side. The reason for this is simple, Colin Truran, technology strategist at Aliso Viejo, Calif.-based Quest Software, said. AI is a very broad discipline in which we have already seen a split where cognitive studies more relating the modeling and understanding the brain are called Artificial General Intelligence (AGI). “Even keeping to the limits of AI and not encroaching on AGI we see an already huge application-driven around understanding complex data types such as video, audio, and common written language,” he said.

Very few organizations develop their own AI based solutions as it can take a great deal of time, investment and expertise only to find it does not work or has a fundamental flaw. The pandemic had an odd effect, it caused the reduction of investment in development of new AI applications but caused a dramatic uptake in technologies and platforms built to use it and provide it.

As organizations re-evaluate where they are in the light of recent decisions, they may well accelerate their general adoption of machine learning and natural language processing based on their newly acquired platform readiness, data interoperability and burst computing in a drive to solve hurdles such as security and compliance in a dynamic environment.

Driving AI Adoption

What is really pushing forward the adoption of AI within organizations is not the technology itself but the way in which it is consumed. In recent years and at an exponential rate, we have seen solution providers build in AI to enable them to remain market leaders or to even break into new markets thus making AI accessible.

The second important step forward is usability; when we look back at the big data trend, we saw many projects fail to complete because they did not grasp the level of expertise involved in running it. To be a good data analyst is a lifetime dedication and is not something you can ask to say your Security Operations Centre to become experts in overnight. “We are seeing more and more AI, especially machine learning middleware solutions coming to market that greatly reduce, if not eliminate altogether, the data analytics learning curve thus placing ML and analytics right in the hands of those that have a business need today and not a subset,” Truran said.