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Whether you’re just starting out or well into your AI journey, the tidal wave that is generative AI (GenAI) has undoubtedly impacted your course. You may be asking yourself what to do next:

  • “Do I jump into GenAI POCs without building a comprehensive strategy first?”
  • “Should I pivot away from the AI path I’m on and go all-in with GenAI?”
  • “Do I redirect planned IT investments into new GenAI tools and technologies?”

AI market hype and speed of innovation can make this kind of decision-making particularly challenging. But the stakes are high, and the pressure is on to deliver business value, so you need to act swiftly and confidently—but how?

Featured resources

Explore the resources on this page to learn how you can cut through the complexity and focus on a course of action that not only boosts your bottom-line, but ultimately gives you a leg up in the marketplace.

What’s the big deal with GenAI agents?

Understand this rapidly evolving technology and how you can use it to deliver a higher order of business value.

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AI strategy shortcut: The payoff matrix

No time to build out a comprehensive generative AI strategy? Learn how our Payoff Matrix can help you identify high-value initiatives that get you in the GenAI game now and create a blueprint for future investment.

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Take the shortcut

Chief AI officer Q&A videos

It’s a fast-moving and complex landscape – particularly in the generative AI space. Chief AI Officer Wendy Collins shares her expertise and advice on how to rise above the noise and zero in on the AI investments that can deliver real business value to your organization.

Watch the videos

GenAI strategy and roadmap

The heat is on to pilot and deliver GenAI use cases that put your company on the innovation map and unleash business value. Learn how you can fast-track a GenAI roadmap that helps you achieve both these goals.

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Explore the roadmap

GenAI implementation patterns

Identifying the right implementation pattern is critical to the success of your GenAI strategy. Explore the pros, cons, and bottom lines of these four different approaches.

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Lessons learned from a GenAI proof of concept

See how a successful POC validated the feasibility and potential of GenAI for our property management client. We share key takeaways to help you drive greater value from your own GenAI initiatives.

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GenAI Readiness in manufacturing

Get meaningful insights on how manufacturers can use GenAI for maximum business and competitive impact. Watch these short-take Q&A videos featuring NTT DATA’s North American Chief AI Officer Wendy Collins.

Visit the playlist

How manufacturing succeeds with GenAI

Explore the many ways GenAI is shaking things up in the manufacturing industry and how companies can prepare to lead in an AI-driven future.

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Enterprise AI Continuum

  • Automation & Analytics

    This is the beginning of the continuum of AI. These include several capabilities like dashboards and data visualizations, advanced or interactive reporting, and robotic process automation. There's a lot that can be learned and unlocked from automation and analytics — especially when you align it to your organizational decision-making needs.

  • Data Science

    At its heart, data science is math. These math models help decompose and solve business problems using common math tools, formulas and algorithms, to predict behaviors. These can be market behaviors in the form of demand forecasting to reduce risk and maximize opportunities or analyzing a company’s website and in its retail stores to have the right product in stock and offer customized promotions. It can also be the behavior of assets in a manufacturing plant, predicting equipment failures and maintenance needs to reduce service downtime and operational costs.

  • Machine Learning (ML)


    ML is also based on a set of math problems. A lot of the times they answer the same business questions but the big difference between data science and ML is that the data science approach is more transparent. The downside however is that it requires more handholding — supervised learning as opposed to unsupervised learning.

  • Artifical Intelligence (GenAI)


    GenAI is what’s dominating the AI conversation right now, hitting the market in tools like ChatGPT, Bard, Midjourney and many others. What differentiates GenAI from the AI that has come before it is that it creates new content. Primarily because it’s run by specific models that are trained on incredibly massive data sets. It’s generating new content by reconstructing existing information or images that it has already seen before.

  • Artifical General Intelligence (AGI)*


    It’s exactly what you think of in sci-fi/dystopian future movies, where the AI can think and reason like humans (and in film, usually does take over the world). But you’ll notice there's an asterisk on AGI in the graphics because, luckily, this technology doesn't exist yet. So it’s one less thing you need a rapid deployment strategy for this year!

AI innovation starts with data

The promise and popularity of GenAI has placed mounting pressure on business leaders to launch and accelerate AI initiatives that generate value. But before you can leverage the enterprise AI continuum to power your organization’s value chain, you need a solid data foundation.

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