According to Google Trends , the phrase “digital transformation” reached “peak” popularity the week of January 13 this year, meaning that during no prior week was the term as popular. Google indexes the peak at 100 on a scale of 0-100, so to put this popularity in perspective using this same scale, the week of December 2016 registered as only a three. Though the buzzword has lost some of its meaning in its incredible rise to ubiquity, it’s still important for business and nonprofit leaders to know the fundamental technologies that spawned the broad use of the term.
To that end, we thought we’d check in on three core digital transformation technologies we’ve been following; How data infrastructures are evolving, how artificial intelligence (AI) is being used in the enterprise, and what’s next in learning technology . These technologies are the fundamental building blocks that accelerate business transformation and are helping companies scale innovation in ways that give true meaning to the popular phrase digital transformation .
Evolving data infrastructure
Substantial interest and investment in cloud computing and AI continues at an accelerated pace, and we are seeing machine learning on the same growth curve. Organizations are interested in evaluating tools and technologies to strengthen their data analytics capabilities, and equally, in attracting skilled talent to advance their use of AI and machine learning. They are also starting to build core foundational infrastructures designed to move them forward in utilizing analytics and machine learning.
Companies are inclined to apply AI and machine learning to familiar and simpler uses cases like business intelligence and analytics, where there is an infrastructure already in place. New tools and technologies enable these companies to apply deep learning techniques to a broader range of business use cases including personalization, named entity recognition, self-service solutions, converting paper work to digital data and object recognition.
To sustain the rising demand for data, deep leaning and analytics, the trend is toward building the core foundational components needed to support AI and machine learning and to develop solutions for ingesting, validating and using more data throughout the enterprise.
AI adoption in the enterprise
Reports from Gartner, McKinsey and others point to the rapid adoption of AI across businesses. The uptake in AI interest and investment is not surprising, as it sometimes seems like AI and algorithms are everywhere you turn in technology and society. The noteworthy aspect of this development is understanding the sectors leading the way, the business use cases being applied and the challenges to adoption.
Most organizations have already begun adopting AI technology to some degree. At one end of the adoption scale are organizations in an evaluation stage or who have at least one AI capability embedded in their business processes. At the other end are organizations, especially in the technology, finance and telecom sectors, that have revenue bearing AI projects in production. Other business functions with significant AI investment are customer service operations, product development, marketing, sales and finance. Machine learning is by far the most-deployed AI capability, followed by process automation, natural language text understanding and computer vision.
Like the big data and cloud transformations preceding it, AI adoption faces roughly the same challenges: changing company culture to accept the need for AI, data quality issues, the lack of data, attracting skilled people, difficulty identifying appropriate business use cases. Just as they did in the earlier stages of their digital journey, companies are gradually removing these barriers. One of the more telling factors in an organization’s use and effectiveness of AI is its progress on transforming core parts of its business through digitization.
The course of learning technology
The training needs of companies have shifted dramatically, driven by the pace of change in business, technology and tools. The availability of new, more powerful technologies and a workforce especially motivated by learning new skills and knowledge are key demand drivers for more effective learning technologies.
Newer approaches emphasize the use of learning resources embedded within the workflow. The most common use case for this is in customer service groups where a sizable number of an agents’ decisions, actions and visualizations are controlled by automated agents. This “in-the-workflow” learning model and other performance support tools are highly effective – they deliver relevant information in context when it’s most needed. The downside of embedded workflow tools is that they are customized solutions, tuned precisely to a specific context.
Another way of providing performance support is through a leaning ecosystem, an environment with fast and easy access to information resources and diverse content types, where users can quickly and efficiently search to solve problems, answer questions, build on idea or solution, then return to the workflow. This is a nonlinear type of learning behavior which has been shown to be among the best ways of developing more proficient learners and subject matter fluency.
The next wave
Each of these technologies is a core component that helps organizations accelerate business transformation. Each one offers capabilities that will characterize the next wave of digital transformation—new tools, techniques and platforms to help businesses scale innovations and progress from prior transformation investments.
The days of data science experimentation and a “fail fast” mentality have given way to a more holistic approach to people, process and platforms. Technology is now much more deeply entrenched in organizations and their culture. A key outcome is that companies are beginning to take advantage of “multiplied innovation,” where the value of innovations over the past few years – including cloud computing, APIs, big data, AI and natural interfaces – is being leveraged and compounded.
As the phrase “digital transformation” becomes increasingly mainstream, it’s important to periodically revisit the real technology advances that are making transformation possible and meaningful for businesses that truly embrace the trend.