Recently artificial intelligence (AI) has been the focus of a lot of speculation, anticipation, and excitement for businesses. A similar trend regarding big data occurred several years ago, however in 2015 big data was dropped from the Gartner Hype Cycle for Emerging Technology. This was theoretically a response to how rapidly big data proliferated, however, perhaps a more accurate perspective is that big data as a concept began to develop along several varied applications and business tools. Advanced analytics and other business intelligence categories filled the void left by the departure of big data from the Hype Cycle. Nowadays, concepts such as AI, deep learning, and neural networks have claimed the spotlight, promising to finally deliver, and further build on, the value first proclaimed by big data. To get to AI, however, it all starts with big data.
When Gartner pulled big data as a concept, the idea was that it was no longer an emerging technology, that it had spread to be commonplace enough to be found in most companies. While many adaptable forward-thinking organizations have instituted some measure of big data capabilities, many still fail to make full use of its potential. In some regions and industries, a lack of useful data has precluded the uptake of big data. In other cases, despite collecting and analyzing big data, a lack of focus on business directives can lead to an underappreciation of big data’s capabilities, as it fails to sufficiently improve profits and business processes. Often this results from a lack of understanding or communication of the resulting analyses. This is no small task, requiring cross-departmental cooperation, and the support of upper management.
However, there is another explanation for big data’s full value not being unlocked by many organizations. Arguably, many of the implied advantages have been reattributed to the next generation of data tools: deep learning, neural networks, and AI. Building on the promises of big data, deep learning and AI have been heralded to provide us with improved business intelligence, support for product iteration and improvements, and advanced research and development capabilities. Fundamentally, AI promises new products, services, and expansion opportunities. Given the appeal of AI, what are the prerequisites for organizations to capture this value when it becomes available, and how can they best spread the benefits throughout their organizations?
At its most fundamental level, AI requires data to act on. Data that will be used to drive the creation and training of deep learning networks and other machine learning technologies. Ideally, this is captured and understood in-house. Not only does this create defensible expertise resulting in a competitive advantage, it allows personnel throughout the organization to creatively iterate on how data could be used to develop and change the products and services offered. Additionally, with the data in-house, this allows senior management to develop a full understanding of both the data, and its resulting implications. This enables senior management to capitalize on their industry knowledge, and keep the organization focused on the customer experience.
Beyond building out an organization’s collection and understanding of data, successful implementation of machine learning and AI leverages industry knowledge and experience by directing it towards well-defined business processes. With readily measured outcomes, the organization can better refine its use of data tools, gradually incorporating machine learning in a more involved manner. Initial implementation models may utilize deep learning to create systems to support employee’s decision-making capabilities. Gradually, it can also be tasked with taking on more and more complex decisions, acting as a specialized strategic advisor, or even given full autonomy in conducting core business processes, eventually becoming a full AI.
Strong leadership is required to manage the transition towards involving algorithms in service delivery. Granting an algorithm oversight or operational capacity in core business functions inherently implies granting the algorithms responsibility. More critically, the algorithms will be working alongside employees, potentially directing them to act or setting goals for human personnel. This can create a lot of tension. Even in standard organizations with people managing and working alongside each other, internal tension is generated, and requires leadership to resolve the generated friction. This effect is amplified when mixing responsibilities among computer and human actors. Towards that end, sound guidance, ideally originating from the C-suite, is needed to help change perceptions, resolve complications, and in some cases, reorganize entire departments.
The future of AI is exciting, and offers distinct competitive advantages to organizations prepared for this wave of the data and AI revolution. At the extreme end, some organizations are already using AI algorithms to develop, train, and manage other algorithms. This is far from common, with many companies still working on fully implementing robust data analytics and collection tools. Regardless of where on the spectrum of digital transformation your organization falls, there are incremental gains to be obtained with each step towards full AI implementation. It starts with an in-house understanding and interpretation of the available data, followed by a deep understanding of the business processes used to deliver value to your clients. Effective leadership is required to keep the organization focused on impacting the customer experience, and ultimately managing the friction brought about by organizational change. This comprises a full understanding and effective management of the business and concerns faced by your customers. Regardless of implementing AI, these principles are also simply good for business.
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