Ryan Welsh, founder and CEO of Kyndi, recently compiled the following list of artificial intelligence (AI) and data-related topics that he believes will be important in 2023.
- The world reaches the era of “peak data scientist.”
The shortfall of data scientists and machine learning engineers has always been a bottleneck in companies realizing value from AI. Two things have happened as result: 1) More people have pursued data science degrees and accreditation, increasing the number of data scientists, and 2) vendors have come up with novel ways to minimize the involvement of data scientists in the AI production rollout.
The coincident interference of these two waves yields “peak data scientist,” because with the advent of foundational models, companies can build their own applications on top of these models rather than requiring every company to train their own models from scratch. Less bespoke model training requires fewer data scientists and MLEs (machine learning engineers) at the same time that more are graduating. In 2023, expect the market to react accordingly, resulting in data science oversaturation.
- The AI industry will offer more tools that can be operated directly by business users.
Companies have been hiring more and more data scientists and MLEs, but net AI adoption in production has not increased at the same rate. While a lot of research and trials are being executed, companies are not benefiting from production AI solutions that can be scaled and managed easily as the business climate evolves.
In the coming year, AI will start to become more democratized such that less technical people can directly leverage tools that abstract all of the machine learning complexity. Knowledge workers and citizen “data scientists” without formal training in advanced statistics and/or mathematics will be extracting high-value insights from data using these self-service tools, allowing them to perform advanced analytics and solve specific business problems at the speed of the business.
- Chatbots will chat less and answer questions more.
Humans don’t want to spend more time interacting with machines as if they were talking to people; they really just want their questions answered quickly and efficiently from the start without lengthy wait times or having to choose from myriad options. Although many chatbots accurately execute the specific tasks they were designed to do, they fall far short of end-user expectations because they rarely answer their actual questions.
In 2023, organizations will finally be able to complement chatbots with Natural Language Search capabilities. Because Natural Language Search understands human language and can process unstructured text-based data (documents, etc.), individuals can phrase questions using their own words—as if they were speaking to a person—and receive all of the relevant answers back instantly.
- Line of business leaders will take matters into their own hands.
Twenty years ago, companies had two choices in the CRM (customer relationship management) space: They could pay millions for a Siebel Systems CRM, or they could pay a fraction of that amount monthly on a per user basis … which ushered in the cloud era. The same thing is happening now for business users when it comes to AI.
In 2023, if the use case provides exceptional value, business users will decide whether it makes sense to hire expensive and difficult-to-recruit data scientists and MLEs, label thousands of datapoints, train and re-train models over months, and repeat this process as the underlying data changes. Alternatively, if the value of this AI project does not justify the significant upfront and ongoing cost, then the organization will find a vendor that can remove all of the complexity for business users.
- Businesses will finally benefit from their unstructured data.
IDC estimates that by 2025, 80% of all data will be unstructured, or free-form, making it difficult to assess and derive insights. Organizations struggle to extract relevant insights when they search for answers in text data, mainly because the search tools they are using are not capable of effectively and efficiently processing unstructured data.
Recognizing the immense value that is being left on the table, organizations in 2023 will apply practical methods to dramatically improve efficiency and unlock the value that has been elusive for so long. Remote and hybrid work has exacerbated the pain of unsatisfying search outcomes because so many employees work from their own locations and access information at different hours, making information sharing within an organization a major challenge. You can’t simply reach out to your colleague sitting next to you for answers whenever you think necessary. In the coming year, expect to see employees turning to Natural Language Search tools to find relevant information across all structured and unstructured sources.
Article: Five artificial intelligence and data predictions for 2023