AI has become a key competitive advantage for businesses. The technology is considered instrumental in the success enjoyed by large companies like Amazon and Walmart which have effectively used AI to gain a superior understanding of their markets and bring unprecedented efficiency in their processes.

Dr. Yaniv Altshuler, CEO of predictions platform Endor, shares, "AI is not just a buzzword anymore, it's becoming a 'must have' for companies to stay competitive. With the massive amount of data companies of all sizes, large and small, collect within their organizations, companies who access insights from all their data sources with the highest speed and accuracy will yield a huge competitive advantage in their competitive landscape."

Unfortunately, barriers to access to AI remain fairly high so only larger enterprises with the expertise and resources get to maximize AI's benefits.

Mind AI chief and founder Paul Lee comments, "AI adoption by businesses is fairly low. Though many smaller enterprises are starting to claim to use AI technology to improve various aspects of their operations, the uses are often insignificant, doesn't provide a huge value-add to the business, and/or are in its experimental trials."

It's a view shared by Diane Rogers, Chief Product Officer of IT management platform Cloud Management Suite (CMS). "Natural language processing (NLP) is one of the more exciting areas of AI," she says. "It's gained prominence thanks to the growing adoption of chatbots and virtual assistants. However, most of its applications are focused on conversational commerce or for basic hands-free commands in offices."

CMS is changing that by introducing NLP-powered functionalities as part of its new real-time security solution. This use case of NLP makes sense given the way IT managers operate. In cases where someone learns about a new malware threat making rounds online, he or she can instantly instruct CMS to shut the threat down, on a hands-free basis, using any connected device.

Although AI adoption seems like an obvious choice, it should be approached with care. The cost of integrating AI into a product isn't guaranteed to be offset by associated revenues, but on the other hand, staying off the AI train can effectively widen the competitive gaps with bigger players. Those that do adopt AI need to ensure that they aren't simply smitten by "shiny object syndrome" and are incorporating it in a way that makes sense to their product's value proposition and user experience.

AI Brings Enhanced Capabilities

Given the successes of early adopters, it's hard to argue against the advantages AI brings. As such, businesses have been trying to apply and integrate technology into various areas of business including:

Personalization. AI can be used to build accurate customer profiles and buying personas which fuel targeted advertising and marketing campaigns. Trained well enough, machines may even be able to predict and anticipate individual needs.

Customer Engagement. Improvements in natural language processing (NLP) have resulted in chatbots that are increasingly capable of carrying out nuanced conversations with humans. They are now being used to manage customers in tasks such as qualifying leads and processing transactions such as orders and queries.

Business Intelligence. AI can also crunch big data in order to uncover trends and generate unique, timely, and actionable insights. AI is even empowering predictions platforms that provide users accurate forecasts instantly.

Automation. Other businesses are letting AI take over routine business processes. Logistics companies are using it to find optimal routes in real time. Manufacturers and retailers let their systems to guide supply chain to ensure sufficiency of stock and minimize downtime.

An Issue of Access

Successful AI projects typically require data, tools, computing resources, and expertise to perform, all of which come at significant costs. Many smaller ventures already find basic digital transformation challenging so experimenting with AI can be a tougher task to deal with. They tend to prioritize digitizing other business areas that they believe could bring immediate and tangible value.

Fortunately, AI experts and platform developers are aware of such issues. They're already making AI not only affordable and but also easy to use. For instance, Mind AI is introducing new data structures called canonicals that make AI processing tasks less reliant on computational power, taking away the need for projects to source expensive supercomputing power.

Aside from these, new approaches to AI are also being pursued. To power its predictions protocol, Endor uses Social Physics--a field of study that combines big data and principles from natural sciences--to make sense of human behavior. This augments the limitations of conventional machine learning when dealing with human-influenced information such as market data.

Speaking of human behavior, the development of third-wave AI can usher in machines that are able to learn and respond like humans. This truly adaptive AI can be used across all areas of business. Lee shares his excitement regarding this prospect, noting, "An artificial intelligence that's capable of generalizing knowledge and contextualizing information will definitely change the way AI tools are adopted and utilized by businesses, both large and small."

Getting in the Game

Given the potential roadblocks in AI adoption, it does seem understandable why small businesses have extreme reluctance to dabble in the technology. However, it would also be a great opportunity missed if they dismiss it entirely. Larger enterprises are already aggressively investing in pursuing their AI efforts.

Lagging behind in digitization could prove disadvantageous for smaller ventures in the long run. They could, instead, take these steps to at least be poised to commit to AI adoption:

Understand what AI is. A fundamental problem for small businesses is their lack of understanding of what AI is and what it could bring. Business owners should take time to comprehend how AI can be used to improve the various areas of their operations.

Start experimenting. Despite the seeming inaccessibility, various business applications and IT tools are already integrating AI to enhance their own capabilities. Businesses could start adopting these tools to gain an understanding of how tasks can be made much more effective and efficient through AI.

Continue digital transformation efforts. If experimenting in AI would stretch capabilities and resources, the least small businesses can do is to continue in their digitization efforts. Digitizing their processes should prepare them for future integrations and enable them to collect valuable business data that can be used to fuel AI efforts once they're ready.

Leveling the Field

While many businesses put off adoption, tech giants are already forming an AI elite. It's a concern that echoes historian Yuval Noah Harari's concerns regarding how AI can enable elitism. Combined with the data they have on users, the use of AI by big businesses gives them the ability to accurately predict outcomes and potentially manipulate situations to their advantage. No ordinary consumer would want oligopolies to happen.