What business leaders need to know about AI

Join today’s leading executives at the Data Summit on March 9. Register here.

Virtually every business decision-maker on the economic spectrum now knows that artificial intelligence (AI) is the wave of the future. Yes, AI has its challenges and its ultimate contribution to the business model is still largely unknown, but at this point the question is not whether AI should be deployed, but how.

For most c-suites, even those running the IT side of the house, AI is still a mystery. The basic idea is simple enough — software that can ingest data and make changes in response to that data — but the details around the components, implementation, integration, and ultimate goal are a little more complicated. AI is not just a new generation of technology that can be arranged and deployed to fulfill a specific function; it represents a fundamental change in the way we interact with the digital universe.

Intelligent supervision of AI

So even if the front office says “yes” left and right to AI projects, it wouldn’t hurt to gain a more thorough understanding of the technology to ensure it’s deployed productively.

One of the first things busy executives need to do is gain a clear understanding of AI terms and the various development paths currently underway, said Mateusz Lach, AI and digital business consultant at Nexocode. After all, it’s hard to push AI to the workplace if you don’t understand the difference between AI, ML, DL and traditional software. At the same time, you should have a basic understanding of the different learning models used (reinforcement, supervision, model-based…), as well as the ways AI is used (natural language processing, neural networks, predictive analytics, etc.)

With this foundation in hand, it becomes easier to see how the technology can be applied to specific operational challenges. And perhaps most importantly, understanding the role of data in the AI ​​model and how quality data is paramount will go a long way in making the right decisions about where, when and how to deploy AI.

It should also help to understand where the major challenges lie in deploying AI, and what those challenges are. Tech consultant Neil Raden argues that the hardest road lies in the “last mile” of any given project, where AI must finally prove it can solve problems and increase value. This requires the development of effective measurement and calibration tools, preferably with the ability to place results in multiple contexts, as success can be defined in different ways by different groups. Fortunately, the more experience you gain with AI, the more you’ll be able to automate these steps, and this should reduce many of the last mile problems.

View from above

Making the actual AI models is best left to the corporate workforce and data scientists who know what to do and how to do it, but it’s still important for the higher-ups to understand some of the key design principles and – Understand possibilities that distinguish successful models from failures. Andrew Clark, CTO at AI governance company Monitaur, says models should be designed around three key principles:

Context – the scope, risks, limitations and general business justification for the model should be clearly defined and well documented. regulatory factors should come into play Objectivity – ideally the model should be evaluated and understood by someone not involved in the project, which is made easier if it is designed around adequate context and verifiability.

Models must also exhibit a number of other important qualities, such as re-performance (also known as consistency), interpretability (the ability to be understood by non-experts), and a high degree of implementation maturity, preferably using standard processes and governance rules.

As with any entrepreneurial initiative, the executive’s vision for AI should focus on maximizing rewards and minimizing risk. A recent PwC article in the Harvard Business Review highlights some of the ways this can be done, starting with creating a set of ethical principles to act as a “north star” for AI development and use. Equally important is establishing clear lines of ownership for each project, as well as establishing a detailed review and approval process across multiple stages of the AI ​​lifecycle. But executives must ensure that these safeguards do not falter, as both economic conditions and legal requirements for using AI are likely to be highly dynamic for some time to come.

Above all, business leaders should strive for flexibility in their AI strategies. Like any business asset, AI must prove it’s worthy of trust, meaning it shouldn’t be released into the data environment until its performance can be guaranteed — and even then, never in a way that can’t be undone without painful consequences. for the business model.

Yes, the pressure to push AI into production environments is great and getting stronger, but wiser minds should know that the price of failure can be quite steep, not just for the organization but for individual careers as well.

VentureBeat’s mission is to be a digital city square for tech decision makers to learn about transformative business technology and transactions. Learn more

This post What business leaders need to know about AI

was original published at “https://venturebeat.com/2022/03/07/artificial-intelligence/”