Generative AI powering an enterprise reinvention

What if everyone in your company had a super-smart assistant who knew everything about your business and your industry, and could crunch vast amounts of data to produce original insights near-instantly??

Imagine the impact in areas such as upstream oil and gas, where it could forecast reservoir performance and guide exploration.??

Or in mining, where it could provide instant, accurate predictions about future commodity prices and trade flows.?

And what about utilities, where it could transform customer experiences with truly personalized interactions at scale???

In fact, the impact on all parts of all businesses would be profound — across energy, mining, utilities, natural resources, heavy manufacturing and more — rapidly reinventing the way people work and the way operational processes are managed.??

A new era for AI?

We won’t have to imagine this future for long.??

Generative AI and foundation models— the technology behind ChatGPT and similar applications — are quickly making it a reality.??

It’s hard to overstate the significance of this change. Soon, we won’t even remember what it was like before we had GenAI “co-pilots” available 24/7 to help us with our work.?

This will fundamentally reconfigure the way businesses are orchestrated. Generative AI will become the operating system of the enterprise. And its combination of analytical power and near-infinite adaptability will spark a chain reaction of new capabilities.??

What’s more, business leaders recognize it. According to Accenture’s Technology Vision? 2023 research, practically all global leaders (98%) said AI foundation models will play an important role in their strategies in the next three to five years.??

An accelerator of enterprise reinvention?

In industries where an innovation and engineering mindset is transforming not only how things are done, but what is being produced, and how it is offered to customers, the key is not to sit on your hands and wait too long in the face of GenAI. Rather, to be bold and start exploring the use cases today.?

The good news is that across the resources companies I speak with, the conversations are all about how to get started, and how to reap value. It’s why I think the next few years are going to be incredibly exciting for enterprise innovation and reinvention.??

We know that the highest performing companies recognize the days of incremental transformation are over. To stay ahead competitively, leaders across resources are looking to reinvent their businesses on multiple fronts at the same time, all the time.??

For instance, recent Accenture research found almost four in five executives shared the pace of technology evolution has accelerated their reinvention strategy.??

The evolution from traditional machine learning, with its focus on multiple AI models for use cases, to more powerful and endlessly adaptable large language models will provide critical fuel for that reinvention.??

Let’s consider just a few of the places where the impact will be felt.?

Worker safety and knowledge transfer?

The energy sector, metals and mining and heavy manufacturing, face a key challenge as a skilled and knowledgeable generation of employees starts to retire, and is replaced by a more transient workforce. Getting new workers up to speed — in areas like process, safety, and tacit know-how — is a potentially ideal use case for GenAI.??

By capturing that institutional knowledge and collective experience and making it instantly available through a GenAI-powered digital co-pilot, companies will enable new workers to ramp up their skills far faster.??

What’s more, existing employees would also have an invaluable tool for suggesting the next best action in their work. In fact, our research estimates as much as 40% of working hours across industries could be impacted by Large Language Models.1?

Oil and gas prospects?

Identifying new subsurface prospects is a difficult and lengthy process, especially when you consider the need to evaluate land sales, farm-in opportunities, and so on. GenAI could accelerate this end to end, from exploration through to carbon capture, utilisation and storage (CCUS).??

Imagine getting an instant, accurate summary of all the various disparate sources of historic internal and external unstructured data that feed into this process. A GenAI solution could also provide contextually relevant insights into what would work, and what wouldn’t, in each prospect.??

That would transform a process that currently takes weeks or months into something that takes a matter of days, if not hours.??

Leading oil and gas companies are already reinventing with data insights. For instance, we're currently helping one of the largest such companies better manage the ever-growing mountains of data it generates.??

Using multi-modal data handling, cognitive search, semantic modelling, and the latest generative AI innovations from Microsoft Azure OpenAI, we’re creating a foundation for an enhanced organization-wide understanding of data.??

This is automating knowledge gathering and providing refined search functionality that gives faster and more relevant answers to employee questions.??

Utilities customer service?

Large consumer-facing companies like utilities might receive hundreds of thousands of customer emails every single day. That’s a significant effort for customer service teams. As well as operating cost.??

But what if GenAI could draft a personalized response to every single one? Or tailor each response to the needs of different customer segments? Of course, you’d still need a human in the loop to verify the content. At least at first. But that would still represent a massive productivity boost compared to what we have today. And the AI’s accuracy would only improve over time.??

This is already high on the agenda across utilities organizations. Accenture’s Tech Vision 2023 research suggests almost 60% plan to experiment with AI foundation models in customer support use cases over the next three to five years.?

The energy transition?

The shift to net zero will involve new ways of working and will need new logic to be built into core business decision making, systems, processes and functions. By capturing, measuring and suggesting data insights on a super-human scale, GenAI could accelerate this transition.??

On a macro level, consider the fact that the WEF estimates the energy transition could add an estimated 30 million jobs in clean energy efficiency and low emissions technologies by 2030. GenAI could help companies anticipate and plan for this future. ?

Then there’s the potential for optimizing existing industrial facilities. An example would be a chemicals plant using a steam-powered production system. GenAI could be applied in real time across all of its sensor data, steam availability data, and so on, to optimize production efficiency. Our Technology Vision research shows 93% of chemicals executives agree advances in generative AI are ushering in a new era of enterprise intelligence.?

How to get started?

As with any new technology, we suggest three key steps to get started: explore, experiment and then scale.??

The explore phase is really important. It means thinking about the initial use cases with immediate value for the business. This is not something to be buried in the IT shop or limited to data science teams. It’s a whole-of-business activity, right up to the C-Suite.?

Then it’s about selecting the most promising ideas and getting stuck into experimentation. Working with partners to spin up sandboxes and proofs of concept. And allowing business users to get a feel for how the technology could work in practice before scaling up for enterprise adoption.??

Time to spark a democratic reinvention?

While generative AI isn’t exactly new, there’s no doubt that recent months have seen a significant acceleration in capability and public awareness.??

Crucially, the release of applications like ChatGPT have democratized the AI playing field. They’ve taken the technology out of the hands of data scientists and given the whole world a taste of what it can do.??

That’s happened at just the right time for a generation that grew up in the era of digital tech and mobile internet. People were already primed for this technology. The result is an explosion of global interest on a scale rarely seen before.?

It’s why business leaders need to act now. Employee demand is racing ahead of enterprise capability. Companies that dive in and start exploring the use cases will find a wealth of new opportunities to fuel enterprise reinvention. It’s time to get started.??

[1]Accenture Research based on analysis of Occupational Information Network (O*NET), US Dept. of Labor; US Bureau of Labor Statistics