a prediction of where you might be in that near future. And looking at that possible future given resources, capabilities and likely market trends, can offer — prescribe — some potential courses of action. Generative A.I. offers a different set of advantages. It can create — generate — content: text, audio, music, still images, even motion pictures. In response to a prompt, Generative A.I. can create new data based on the patterns and information learned from existing datasets — large language models. The accuracy of Generative A.I.’s response can be enhanced through machine learning by feeding it more data, iterating, and refining the prompts. The (relatively) new kid on the block is Agentic A.I. While Generative A.I. focuses on the creation of new content with input and guidance, Agentic A.I. is designed to make autonomous decisions and take action(s) to achieve a goal. It relies on machine learning, natural language processing, and automation technologies as well as the creative abilities of Generative A.I. However, Agentic A.I. focuses on making decisions, does not rely on human prompts, and is tailored to particular goals or objectives — like maximizing sales and customer experience, or refining supply-chain processes. And most importantly, Agentic A.I. can carry out sequences of activities, independently searching databases or triggering workflows to complete activities. It’s goal oriented as opposed to output oriented and is suited to those applications where autonomy and decision making are critical — robotics, autonomous vehicles, customer service operations, education, healthcare … the list is growing. “The opportunity to drive A.I. transformation is upon us and equivalent to the iPhone launch moment in 2007. A few leading enterprise software technology organizations are now able to bring together A.I. Agents + Data + Workflows within one enterprise-grade platform, directly integrated into the flow of work which will win big in this massive technology industry shift.” – David Parsons Lowder Entrepreneur-in-Residence Harbert College of Business
L aying out the strategic plan for a business is not an easy task; and it certainly doesn’t happen overnight. The C-Suite will likely have lengthy conversations among themselves, with the corporate board and senior management; external consultants often play a role; internal stakeholders will be surveyed; and customers will be focus-grouped. Strategy is a gnarly issue. Choices do not readily present themselves; the various stakeholders bring equally various assessments and perspectives; the marketplace is fluid; and often there’s no clear-cut connection between action and outcome. It would not be unusual for a company to take a year to dot the i’s and cross the t’s of a strategic plan. In today’s business environment those who can solve this messy problem quickly will enjoy a competitive advantage. Can A.I. help? It’s doubtful that strategy development can be automated — at least not now. Much of A.I. is still in its infancy, but A.I. and advanced analytic tools can certainly help. And they can certainly speed the process along. Speaking of analytics, they are usually classified in four categories: descriptive, diagnostic, predictive and prescriptive. Descriptive is the simplest form. Think of a dashboard that automatically updates, and can show how your business is doing and/or how you stack up against competitors. A “where we are now” look at the firm. Comprehensive descriptive tools can break down your business into detailed segments and pinpoint where you’re performing well and where you’re not. And it can do so quickly and continuously. With this capability, you can test dozens of scenarios in minutes, as opposed to the days or weeks a team of analysts may need to project those same scenarios. If you look at those descriptions over time, you can get a diagnostic sense of “how” you came to be where you are. With the proviso that past performance is no indication of future performance, drawing a line from the past through the present and extending it can offer
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