A Generative Approach to AI

The pervasiveness of Generative AI applications and tools is leading to some of the most accelerated transformations we’ve seen since the advent of the Web. More companies across more industries are experimenting and engaging with the tech in the hopes of driving improved efficiencies, kick-starting creative endeavors, and improving general business performance.

We couldn’t agree more with the approach. Despite the very real concerns about the technology’s early misgivings, the ability for its misuse, and the potential for over-reliance on it, the longer view is one of transformational progress.

Between Hitachi Vantara and our parent company, Hitachi Ltd., we’ve been investing heavily in research and development in AI for decades, driving the technology across both internal processes and product development. Just last month, Hitachi Ltd. announced the formation of the Generative AI Center, a new group dedicated to promoting the safe and effective use of the technology.

Taking Steps Toward Exponential, as well as Incremental, Outcomes

Smart enterprises will recognize the potential and make an agenda item to investigate, experiment, and embrace while understanding we’re still in the early innings of this revolution. As Sam Altman, CEO of OpenAI and the creator of ChatGPT, told The Economic Times, recently: “It’s a mistake to get too focused on the current systems…the thing that matters here is that we’re on an exponential curve, truly.”

To fully realize the potential requires a commitment for the long game. Such an approach can open up exponential, as well as incremental, outcomes down the road. To help, consider these guiding principles:

•  Experiment with guardrails. While there are lots of views on where this tech will go and how it will transform businesses, careers, and society, this game is very much in its early innings. It is nearly impossible to have an accurate prognosis of how it will evolve. The only way enterprises large and small can learn the potential for their business is to borrow from Nike, and just do it. Pick areas of strategic focus – operations, customer and employee experiences, business models – identify privacy concerns, regulatory and ethical considerations, and then experiment…a lot.
• Look for a signal in the noise. Ensure that you have a mechanism in place to capture the learnings across these experiments and look for signals in the noise. You want guardrails in place, you want procedures and policies that protect you as an organization from bad actors and unethical behavior. However, you also want significant experimentation. There is no single answer to this. So, encourage experimentation and then utilize a means to capture the learnings to identify the real opportunities.
• Be ambitious. Very Ambitious. Be driven by curiosity, not stalled by concern. The time for exponential thinking, broad and long term, as well incremental, is now.
• Take a strategy-in view. Consider the ways that Generative AI could be applied across your business, from internal processes to customer engagement processes, all the way to new revenue streams. The tech is rapidly becoming an intertwined element of business, not an abstraction of it. So, look for places where AI can be embedded and intertwined and then figure out where it is having the most efficiency and productivity impact; where it is having an acceleration impact; and where it is foundationally changing the business and business model. And then ultimately, you want to discover new opportunities; fundamentally new opportunities that were inconceivable up to now.

Large Strategy Themes

In other words, identify your large strategy themes and then investigate how Generative AI may support them. To be sure, there will be costs involved. But the question should not be can you afford to invest in it, but rather, can you afford not to?

For example, in addition to enhancing developer productivity at Hitachi Vantara, we’re using Generative AI to give data scientists greater power to create and communicate knowledge. In addition, traditional mundane engineering tasks have been reduced, code writing has been sped up, and developer productivity has increased. In addition, things like code consistency, collaboration, and learning have all improved. We’re counting on all of this to drive improved product quality, faster development cycles, and more consistent product delivery.

Actually, these innovations couldn’t have come at a more critical time, as data volumes around the world continue to explode. In its 2022 Global DataSphere Forecast, IDC predicted the “Global Datasphere” – a measure of how much new data is created, captured, replicated, and consumed each year – is expected to more than double from 2022 to 2026.

To help enterprises channel this tsunami of data and prep it for their analytic and AI applications, we’ve also developed new Data Reliability Engineering services that provide high-quality data for more consistent, accurate outcomes.

We Can See the Future from Here

Despite the myriad concerns of data hallucinations, bad actors, deep fakes, and more, the reality is that the Generative AI revolution is well underway. And it’s important to remember that Large Language Models (LLMs) and models based on them are designed specifically to get better, smarter, and more accurate with use and more data.

This is true for public and private LLMs alike. In fact, while public LLMs have been the catalyst to driving development and awareness, in many ways the private LLMs being curated and built within organizations that apply directly to business processes and subprocesses, may very well hold the keys to how Generative AI impacts everything we do in the future.

There will continue to be ethical debates about the potential for its misuse, and there should be. This is only the beginning. Consider that in the not-too-distant future, we’ll be contending with the integration of quantum computing, as well. For now, however, the technology is taking hold. It will be disruptive. It will be transformative. And it will be imperative to learn how to harness it to succeed and grow.

No one, however, owns the truth about how this technology can or will improve a particular business or function. The onus is on the individual and the individual organization. Take the opportunity to experiment, to find answers, and to learn from doing. This is the only way to get better at this. Make it an agenda item and think exponentially as well as incrementally.

By Gajen Kandiah, Frank Antonysamy, and Prem Balasubramanian

Gajen Kandiah is President, Hitachi Digital and Executive Chairmam, Hitachi Digital Services.

Frank Antonysamy is Chief Growth Officer, Hitachi Digital Services.

Prem Balasubramanian is CTO, Hitachi Digital Services.

This story first appeared on Gajen’s LinkedIn page