The fourth technological revolution is associated with the era of AI applications. Providing corporations with multiple opportunities to distinguish themselves within a competitive business environment by allowing companies to utilize data they accumulate to improve business processes, products, and services. However, media coverage of the AI revolution has been characterized by a wealth of contradictory information and inflated expectations regarding how, and where, AI can be applied.
Managers and business stakeholders largely believe that AI will soon replace most professionals, instead of supporting them with specific tasks in which the machine has an advantage over the human. Moreover, AI applications are probabilistic in contrast to traditional software development where scope and deliverables are predefined. It is therefore essential for business managers to embrace uncertainty and support AI development with a hands-on approach in an experimental setting.
As of today, the AI technology stack has yet to evolve into a solid framework that incorporates standard design and development patterns, like with traditional software. Therefore, randomly selecting from the numerous options in this rapidly growing AI field without proper ownership, ill equipped decisions can lead to a heavy financial burden and a technical debt. A lack of leadership and human capital, specifically data scientists, makes it difficult to develop core competencies in this area. Many solutions may appear to make sense, but when tightly linked to a clear strategy, the better solutions quickly begin to rise to the top. In a comprehensive market review, leading American venture capital firm Andreessen Horowitz stated, that AI presents a new economic model, combining software and services in a way that is different from the familiar market economy of software and SaaS applications.
The regulation of AI technologies presents a further challenge, particularly in highly regulated industries such as digital healthcare and finance. This has been accelerated by the publication of a new EU AI regulation, and other countries and regulators are expected to follow suite. It appears year 2022 will be a pivotal year for AI regulation and governance.
What does it take for AI solutions to provide ROI that has actual business value for an organization?
Nonetheless, the benefits of AI are indisputable, and companies are generated massive annual savings and revenues from successfully implemented AI solutions. The most effective way to create an efficient environment for AI success, is to establish an internal AI center of excellence (AI CoE) to manage challenges and leverage AI capabilities. Companies adopting AI CoE’s facilitate the development of solutions, examine relevant technologies, and recruit and train data scientists to oversee the organization’s investments in AI. Pilot projects run within a CoE help to avoid costly mistakes when implementing AI related technologies.
DSG offers an innovative and distinct model for building dedicated AI CoE’s for organizations based on four fundamental components; An AI enterprise platform; proven methodologies that support the entire development life-cycle of AI applications; a dedicated human capital cluster comprising data scientists, data engineers, software engineers and business representatives; and capabilities for monitoring AI models in a production setting to ensure they meet regulation standards. Combining these four pillars will help companies in the rapid, efficient, and creative development of advanced AI solutions while forming an internal organizational capability base.
In summary, the field of AI is still in the early stages of development, even with all the challenges and misguided speculations, companies that are quickly adapting and forming experienced educated teams capable of developing and executing a strategic AI plan, are gaining an exponential competitive advantage that will continue to evolve, while maintaining a sustainable structure for the future.
The writer is CEO Data Science Group, Lecturer at Tel Aviv University’s graduate courses in AI.