The vast amounts of unstructured text is leaving knowledge workers struggling to comb through the sheer volume of information available. This has created a need for humans and artificial intelligence (AI) to work side by side to create a true natural language-enabled enterprise, which allows the organization to deliver business outcomes with an effectiveness that far surpasses what can be achieved when either is deployed independently. As a result, expect to see the following trends in the coming year:

2022: The year when humans and AI work together to drive enterprise performance. Organizations will look for AI technologies that remove the barriers of traditional supervised learning models so that knowledge workers can more easily and quickly turn these troves of data into usable information. AI vendors will flip the script and deliver solutions that do not require the time, resources, and expense required for supervised learning models. They will deliver solutions that provide highly relevant and context-driven information with unprecedented speed and precision so that humans are empowered to do their most meaningful work. Rather than replacing human intervention, these modern – and evolving – AI technologies will allow people to analyze and use unstructured as well as structured data in a smarter, faster, and more natural way.

Today, approximately 80% of the information in both public and private sector organizations is unstructured text. As a result, companies cannot gain actionable insights from, and find the hidden intelligence in, text needed to ensure effective decision-making. Worse, most struggle to analyze and leverage the growing volumes of data they’ve already collected. In 2022 and beyond, businesses will move on from the supervised learning approach to a natural language-enabled model that enables humans to identify opportunities and threats and to take more immediate action.

Organizations will focus on AI initiatives that augment human performance, not replace humans with machines. Up until now, the goal of machine learning for most applications has been the replacement of human effort with machine effort. 2022 will see machines performing tedious, tactical tasks such as information retrieval, etc., which will enable humans to focus on higher-level strategic tasks and decision-making.

Single-use-case machine learning models will give way to the centralization of institutional knowledge for use in business processes across multiple subject domains as well. Historically, supervised learning models have required training the AI, often with a finite set of content, with a specific vocabulary, and for a specific use case. The time and effort required to do so are both time- and cost-prohibitive for the average user. In the coming year, expect AI providers to focus on delivering platforms that centralize data/content for use across multiple business processes. For example, a sales account executive, product manager, and customer support representative might all draw upon centralized intelligence repositories to solve their business problems (i.e., using the same institutional knowledge for different purposes).

Businesses will expect vendors to deliver comprehensive AI-enabled solutions for line-of-business teams instead of focusing on developer tools and technologies for IT. Much, if not most, of the AI industry has focused on developing robust tools for internal IT teams or consulting organizations to apply the technology for a specific use case in an enterprise application. In 2022, organizations will demand AI vendors begin developing specific AI-enabled solutions that can be implemented immediately without coding. By focusing on providing human-centered solutions to business users, vendors will enable individuals to immediately generate insights that drive decision-making. Consequently, organizations will shift their investments in AI, moving away from highly customized solutions in favor of configurable (off-the-shelf) options. 

Businesses will also expect AI solutions to be flexible so they can leverage solutions to address their specific needs. To date, the AI industry has largely focused on developing tools for data cleansing, annotation, labeling, and training of machine learning models by AI experts and developers. In 2022, expect to see AI vendors focus on moving up the technology stack by providing user-facing solutions on their platforms. Not only will this provide an opportunity to configure an AI-enabled app to a company’s specific need, workflow, and content, it will do so without the organization having to involve IT resources.