Do business intelligently: how to transform your organization with GenAI

What did you do with GenAI today?

There’s a good chance that you or your employees asked Copilot for Microsoft 365 or ChatGPT to suggest a few lines of copy, summarize a meeting or create a presentation.

As useful as these GenAI tools are for specific tasks, have you wondered whether GenAI technology will truly change the way you do business and show a healthy return on investment?

What separates the day-to-day activities from landmark, enterprise-level impact? And, when it comes to adoption, how often is failure the outcome?

The pace and prevalence are challenging

The swift evolution of GenAI technology in the past 18 months means there’s already much progress to celebrate across many industries and in a variety of use cases.

According to Gartner^®, “we believe that by 2025, more than 30% of new drugs and materials will be systematically discovered using GenAI techniques, up from zero today … We predict that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022”*.

GenAI is gaining traction in banking and financial services, a process-driven industry conducive to AI-native workflows, and in healthcare. Advances in AI processing at the edge is accelerating the convergence between IT and operational technology in manufacturing, and GenAI-driven disruptions in the retail industry are making customer experiences more seamless and natural.

These are all fitting examples of impactful opportunities. So, if you haven’t yet hit a win or experimented with GenAI in your organization, why not? Your competitors are certainly doing it – and the imperative is fast changing from searching for competitive differentiation to needing to survive.

Gartner forecasts that, “by 2027, GenAI will augment 30% of all knowledge workers’ tasks, from 0% in 2023”**, while Goldman Sachs Research forecasts AI will start having a measurable impact on US GDP in 2027 and affect growth in other economies around the world thereafter.

The projections are there, so why are some organizations still hesitant to tackle the topic? Or, for those that do, why are some feeling disillusioned?

Your enterprise organization is changing

In less than two years, GenAI has evolved from an experimental approach to a core component of business operations – a rate of development far exceeding those of previous technological evolutions such as cloud infrastructure and the wave of corporate digitalization that followed.

GenAI touches the full spectrum of enterprise functionality: people and culture, physical infrastructure, cloud and more. We tend to think of it as a front-end experience, yet it is pervasive at the mid- and back-office levels. To make GenAI work, it must be put to work on an enterprise scale – in every part of the business.

In fact, one could even argue that GenAI serves as a transformational bridge between digitalization and the cloud, as it makes knowledge workers more efficient and gives them more intuitive ways to adapt to the changes introduced by digitalization.

The reality is that GenAI requires end-to-end, full-stack ideation, delivery and management expertise, and building a GenAI strategy and roadmap grounded in business value is essential. Otherwise, you’re just chasing fast-moving technology – and the pitfalls and perils are real and rapid.

Getting in on the trust

GenAI is making great strides in improving customer experience through AI-enabled chatbots, even as organizations grapple with issues of governance, risk, trust, ethics and security in the AI context.

According to Gartner, Inc., “at least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value”***.

Making GenAI more reliable and trustworthy requires specific input and interventions from the same knowledge workers and technical engineers who will largely benefit from its output. So, your employees need new skills to navigate and manage advanced GenAI ecosystems.

Unique GenAI platforms – for innovation, solution discovery, learning and delivery – are also key. Find a way of standardizing tool and components that goes far beyond generalized adoption to make GenAI the fully integrated, enterprise-driving secret to your success.

Now and next in GenAI

Prompt engineering has been one of the first trends shaping the GenAI landscape. Users must learn what types of prompts generate the most relevant and contextually appropriate results.

The technical spotlight is now on two notable approaches that are bringing us closer to creating end-to-end business workflows using GenAI platforms:

• Retrieval-augmented generation: This combines the retrieval of information with generative models to produce answers that are not only accurate but also deeply informed by existing data. In other words, you can opt for an off-the-shelf large language model (LLM) but make its output much more powerful through in-depth training on your company’s contextual data.
• Agentic modeling (smart agents): These AI-driven systems can automate complex workflows alongside the LLM functionality. This leads to systems that can perform tasks and make decisions independently, equivalent to humans. For example, an AI-powered virtual assistant in an insurance contact center can now handle claims without human intervention by making decisions based on given knowledge bases and workflows. Its actions may include looping in human agents for certain tasks or connecting to other systems in the organization.

These developments are already strengthening industry use cases such as managing insurance claims, assessing borrower creditworthiness, proposing treatment plans in hospitals and manufacturing new products.

Overcoming obstacles to a return on investment at scale

Despite these operational benefits, the path to integrating GenAI is not entirely smooth.

One of the main hurdles is scaling your GenAI initiatives from proof of concept (POC) to production. Among other things, this requires ongoing investment in data activation and governance – an often underemphasized challenge partly due to the need to capture the data you collect and store during your business activities but which is not yet contributing to decision-making.

An expert partner will move you through pilot use cases to POCs that truly work and scale and are repeatable. For example, NTT DATA is knee-deep in GenAI solution development within our business and for our clients. Our expertise allows you to experiment and learn on an entirely new level.

However, the strategic integration of GenAI into your business operations involves far more than just a technological update. You have to fundamentally rethink your business processes and focus on data strategy, underpinned by a robust data governance framework that will feed accurate, relevant and harmonized data into your GenAI systems.

And, while the token-based consumption model for LLMs – where users are charged based on the number of tokens (words, parts of words or punctuation) in a query – is fast becoming more affordable, implementing GenAI widely in your organization may need a fully or partly computing-based model that scales based on the volume of computational resources allocated to it. This marked shift to “private AI” involves detailed planning of data center footprints, GPUs, servers, abstraction platforms and more.

“Idea to metal”: expert help is at hand

This is where the value of working with an experienced and comprehensive service provider like NTT DATA becomes clear. Because our global portfolio of products and consulting services stretches from cloud, networks and applications to data centers, business processes, and data and analytics, we can engineer, manage and continually improve the full GenAI lifecycle in your organization.

We invest billions in research and development, and we’re one of the largest data center providers in the world. Through close partnerships with Dell Technologies, NVIDIA, Microsoft and other vendors, we can build infrastructure stacks for private AI solutions. Our digital workplace and edge computing solutions are market-leading. We are rated among the best in business process outsourcing and business process as a service across industries, and HFS Research has named us a Market Leader in the GenAI services industry.

So, whether you're building a GenAI-enabled contact-center solution or hoping to raise employee productivity with Copilot, we have all the touchpoints you need – in the form of specialized, fit-for-purpose solutions for specific industries.

For example, in retail, our AI-driven sales assistant is redefining online shopping for L’Oréal customers. We’re also implementing an AI-enabled payment integrity solution for a major healthcare provider in the U.S., and a major European city will soon launch a powerful, GenAI-based solution aimed at engaging both residents and tourists. In India, our Accelerated AI Platform helps clients make swift progress in adopting AI.

Let your organization thrive

As GenAI evolves, it is bringing about significant shifts in how businesses operate and compete. For CIOs and CEOs, the message is clear: embrace GenAI not just to keep up with the times but to define them, and take a decisive, full-on look at your entire enterprise. Proper business outcomes and a valuable return on investment demand a comprehensive strategy.

By understanding the latest trends and implementing GenAI intelligently, with the help of NTT DATA, you can master your organization’s GenAI destiny.

Read more about how NTT DATA uses GenAI to help you realize your organization’s business goals.

* Gartner Insights, Gartner Experts Answer the Top Generative AI Questions for Your Enterprise, https://www.gartner.com/en/topics/generative-ai.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

** Gartner Research, Emerging Tech: Primary Impact of Generative AI on Business Use Cases, September 2023.

*** Gartner Press Release, Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025, July 2024, https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025.