5 best practices for scaling AI in the enterprise

AI has entered a new phase. The past few months have seen an explosion of generative AI. The ability to use text to automatically write stories and create art is evolving very quickly. The first applications of these new capabilities in co-authoring software, writing news articles and business reports, and creating advertisements are already emerging. We can expect entire industries, from software engineering to creative marketing, to be disrupted.

Basically, AI has become the best possible prediction machine. We've seen AI incorporated not only into big apps like self-driving, but also into hundreds of everyday tools and utilities. AI has reached the right inflection point on the maturity curve to drive common, important, and varied enterprise applications. While AI is disrupting the way we live and work, for most companies, real innovation comes not from experimentation, but from industrializing AI at scale.

Here are five best practices for making the most of emerging AI capabilities in the enterprise.

Start with the question, not the answer

One of the biggest challenges of implementing AI is defining the business problem the company is trying to solve. As the saying goes, don't end up with an answer looking for a question. Simply deploying new forms of technology is not the right approach.

Then look at the issues and determine if AI is the best way to solve the problem. There are other digital technologies well suited to simple problems. To ensure success, clearly define the business problem and determine the path forward from the start. Some may not need AI.

Plan an AI-powered transformation to be different from automation

In automation, the end-to-end process is disaggregated and divided into smaller parts. Each part is then digitized, and the parts are then re-aggregated into the value chain. Automation provides efficiency, time to market, and scalability, but the underlying work and process remains the same.

On the other hand, when companies leverage AI to transform, entire value propositions are redesigned, the customer experience changes, processes are redesigned end-to-end, and the remaining work becomes fundamentally different. from before.

Thus, AI-driven transformation is as much about designing a new operating model, cross-training the workforce, and integrating it into upstream and downstream processes as it is about neural networks and model management. It is important to note that AI in business is 20% about technology and 80% about people, processes and data.

Create a database

We are moving from a data-poor world to a data-rich world. We are increasingly integrating telemetry and digital devices into our operating environments, opening up new sources of data previously unavailable.

Thanks to AI, data that was traditionally in unstructured formats is now easily extracted, converted, and used productively. As a result, the data now available to support business operations and decision-making is unlike anything we've ever had.

Creating a database is key to reaping the benefits. It is essential to manage data not only in terms of basic data infrastructure, but also considering quality, security, authorized purpose and granular access.

Focus on digital ethics

With the growing footprint of ambient intelligence comes an associated risk of security breaches, model drift, unintended bias, and unethical use. As AI use cases grow and proliferate and large amounts of data are collected and managed centrally, this opens the door to security vulnerabilities.

Model drift occurs when AI models, while adapting to new data, end up drifting to less accurate results. If not designed on purpose, bias can often be unintentionally introduced into AI systems. The use of AI should be overseen to ensure it is used ethically.

Digital ethics must be included from the start in the design and architecture of the system...