Perhaps the underlying reason AI, CI, and other technologies will be disruptive factors in 2020 is digital transformation.
It has been an amazing year for all things related to real-time analytics. While there were many developments throughout the year, perhaps the most important trends to emerge were the growing need for explainable artificial intelligence (AI), commercial AI and machine learning (ML), and the expanding use cases for continuous intelligence (CI).
Gartner prominently cites all three in its list of top data and analytics trends. These areas, along with a few others, will have significant disruptive potential in 2020 and for years to come.
See also: Using the AI Ladder to Prevent the New Digital Divide
One might ask, why? There are several reasons.
Expanded use of CI
Probably the most significant factor allowing the expanded use of CI is that it is finally practical to implement CI on a much broader scale because of the cloud, advances in streaming software, and growth data from the Internet of Things (IoT) sensors. These factors are accelerating the adoption of CI. For example, Gartner notes that by 2022, more than half of major new business systems will incorporate CI that uses real-time context data to improve decisions.
Commercial AI and ML
Most AI and ML implementations today use open-source platforms, algorithms, and development environments. As has been the case with the use of open-source in many other applications (server software such as Linux, clustering software, databases, and more) over the years, there is now a transition underway to commercial solutions.
To understand the interest in commercial solutions, consider what happened with server software. While companies always the option of using open-source Linux, many opt for solutions such as Red Hat Enterprise Linux, which is a Linux distribution with enterprise features designed for businesses by Red Hat.
Similarly, in the near future, businesses will likely look for commercial AI and ML solutions that offer enterprise features necessary to scale, optimize, and secure AI and ML. Increased use of commercial AI and ML will help to accelerate the deployment of models in production.
Explainable AI
The growing necessity for explainable AI is being driven by the hesitation of businesses to deploy AI and ML algorithms, given their characterization as “black boxes.”
Often, the models are created and run without knowing the details about the strengths and weaknesses of the algorithms, what data was used to train models, whether that data skews results or is limited, and whether that data is still valid for current business conditions. Common areas of concern include a lack of transparency, data expiration issues, data completeness, and bias. These are all very broad issues that need attention. Embedding AI and ML into business processes will only be successful if a company knows what goes into the algorithms. In years to come, businesses will need to adopt best practices in addressing AI transparency and bias issues. If they do not, regulators may step in and potentially disrupt the market.
Digital transformation’s role
Perhaps the underlying reason AI, CI, and other technologies will be a disruptive factor in 2020 is digital transformation.
Many companies have made major investments in Big Data solutions and coupled these investments with marketing automation, IoT, and B2B commerce platforms. With such investments, analytics helps rationalize ROI, illustrate business benefits, and discover how to optimize data investments moving forward.
Specifically, businesses want to leverage the ever-growing amounts of available data to improve operational efficiencies, decision making, customer reach, customer satisfaction, and profitability. As a result, advanced analytics, CI, and ML will be at the core of today’s enterprise, from the formation of business strategy to powering operational excellence.
Whether it is a single department making use of customer data to improve one aspect of their operations or an enterprise-wide undertaking, the guiding principles are the same. By quickly identifying patterns and analyzing vast amounts of data, businesses hope to derive insights to drive revenue growth, increase efficiencies, and improve customer engagements.
This is why explainable AI, commercial AI and ML, and the expanded use of CI will be critical in 2020 and beyond.
