Getting ahead with generative AI

Considering the speed at which generative AI could develop, a coordinated approach is a must.

What it means to coordinate your generative AI efforts

Companies won’t be rewarded for sitting on the sidelines while others figure out how to use generative AI to transform how they do business. Caution? Yes. Dawdling? You’ll regret it.


Ultimately, the advantage will go to those who devise increasingly sophisticated, strategic and intrinsic ways to ride the waves of innovation ahead. 


To gain an advantage, you’ll need to take these actions in parallel:

  1. Choose a few priority business outcomes to drive, and make sure they’re clear for everyone. Company leaders should name the problems they’re trying to solve, whether it’s productivity gains, better insights or even better decisions. This direction can help organizations balance the coordination they’ll need between AI, technology and domain leaders. Together, you’ll see how to equip teams and set expectations for how grassroots innovators can contribute.

  2. Think of value for many when it comes to productivity. Learn to harness bottom-up energy and excitement by looking for generative AI uses that consider risk, ease of implementation and how to offer the greatest lift to the largest number of people. Don’t give a giant productivity boost to a tiny fraction of your workforce when you could give a modest boost to a much larger share.

  3. Build AI for trustworthiness. With AI, trust isn’t just for engineers—it must become a company mindset. Your core development teams can practice and promote the principles of competency, responsibility and transparency as AI governance improves. Across the workforce, teach people how to evaluate risks based on the sensitivity of the data and the nature of each application.

  4. Use generative AI to make your existing AI more powerful. With generative AI, listening engines can combine with the more traditional AI you’re already using to tap into unstructured data that was previously too difficult to mine but could lead to breakthroughs. To understand how this might work, imagine a model that uses patients’ electronic health records (EHRs) to assign risk scores that singles them out for higher-touch care. A large language model (LLM) could scour physician notes on each patient for insights not captured in the EHR that make predictive models much more accurate and reliable.

See how we help you scale the value of AI

Featured insights on generative AI


How the world of AI is changing business

Discover the latest trends on generative AI with Forbes contributor Arun Shastri

Connect with our experts

Frequently asked questions about generative AI


What are the broad types of generative AI use cases?

Common generative AI use cases generally fall into four large categories where models can 1. synthesize information, 2. generate content, 3. provide answers or 4. act as an agent. When it acts as an agent, this means generative AI can break down a business problem into its constituent tasks, complete each task, request feedback, change tack, deliver the requested information and then explain what it did and how.

Should we start assessing job tasks that AI agents can do instead of our people?

While automating tasks can make workers more efficient, we don’t see it creating a sustainable competitive advantage because anyone can do it. More advanced strategies will see companies deploy LLMs to rethink how to carry out work differently, for example by supplying predictive information or simulating outcomes that will likely change tasks and processes.


What’s the hierarchy of risk when considering generative AI use cases?

Generally, as the sensitivity of data increases, risk should be evaluated more thoroughly. For example, teams can evaluate models trained on publicly available data differently than those that may use more sensitive information. Similarly, models that augment decisions and keep humans in the loop can be good places to start with lower risk to your organization than those that automate decisions.

Can we make AI more trustworthy?

You don’t want to create an AI solution only to have its validity and bias exposed by end users. A trusted AI framework can be used to guide development and enhance transparency throughout a model’s life cycle as part of your overall governance model. One potential use case for generative AI is making traditional AI solutions more trustworthy by making them more explainable and intuitive for more users.

How should we organize for using generative AI?
Considering the speed at which generative AI could develop, a coordinated approach is a must. In addition to a responsible leader, representatives from your data science community, engineering, legal, cybersecurity, risk, marketing and other core functions should be charged with aiding the overall approach to balance risk and value.