Break boundaries with generative AI

But before all enterprises can reap similar rewards, there are considerations and lessons to build on.

Prime among them is a warning: the most dangerous thing about AI is assuming that it only delivers productivity.

Take a customer service team in a large enterprise. Instead of simply supporting agents with automated processes to help resolve queries faster and answer more calls, generative AI can make every interaction more meaningful with the right tone and messaging. And just as each communication will be more cost-effective, the customer experience will increase, as will revenue and profit-generating opportunities.

So, to understand the opportunities and change that generative AI can bring to a business, we've identified five key considerations, the steps toward integrating gen AI most effectively, and the use cases that offer unexpected possibilities.

Streamlining holistic change

1. Rethinking workflows for the future

By connecting generative AI to the breadth of your company's data and integrating it across workflows, gen AI not only automates but also augments employees' work and even creates new roles. As AI takes on easier tasks, it enables people to address more complex problems, find connections, and be more innovative. It also has a disproportionate impact on less skilled and experienced workers, enabling them to get onboarded faster while also cutting cycle times to real or near-real time.

With this in mind, as you rethink your business' operating model, consider how generative AI will impact the way they deliver value to customers. Gen AI plays a dual role here. Forward-thinking companies are building AI-native process workflows that are capable of continuous self-optimization using insights from three sources:

• Generative AI's reasoning capabilities that create and test hypotheses
• Live and mined process data from process intelligence tools
• Human ingenuity

A self-optimizing process can deliver between 5%–15% additional speed and value to an enterprise when compared to today's static processes. And even more when integrated with other AI platforms.

2. Enabling human-centric hyper-personalization

With augmented workflows and workers, companies enable human-centric generative AI. By combining the technology's capabilities with the experience, empathy, and ethics that only people can bring to decision-making, companies can now deliver hyper-personalization.

Imagine how generative AI in a financial institution can blend the firm's client portfolio data, research, and customer-relationship-management insights. With this information in hand, a wealth manager can send more targeted and highly personalized communications that match the tone a client responds to best with a single click. The manager can send thousands instead of hundreds of messages that are now more relevant and in tune with a client, deepening their relationship and business opportunities.

Achieving hyper-personalization takes more than generative AI. It requires a business to have data and systems integrated across the organization.

3. Managing change is fundamental

As companies introduce employees to gen AI – a new colleague that will augment their work – there will be questions and concerns about the impact it will have on their roles, how it makes decisions, and how they can best work together. Preparing employees for the change before they can ask "What's in it for me?" will create an environment that people feel safe to learn in and engage with the technology.

A structured change management program assesses a company's organizational readiness, designs strategies for talent, learning, and communication, and improves the employee experience. Once people understand how generative AI will help them, it will build trust, and they'll be ready to embrace their new team member, fueling even greater adoption.

4. Upskill at speed and scale

The need to train new and existing talent on how to best use generative AI is a given. But how you deliver that training will have a direct impact on the return on their investment. Generative AI can help by delivering personalized learning, simplifying user interfaces, and automating tasks.

At Genpact, we have a generative AI training program on our learning platform that itself uses gen AI to give people instant access to the collective intelligence available on the platform. We've enrolled over 70,000 employees, of which 22,000 have completed the program's proficiency level, and 60,000 have gone through the self-paced immersion. And our 12-week immersive program for developers offers different levels of training depending on an individual's role as a user or developer and includes a deep dive into prompt engineering.

Maintaining a strong employee and user experience during reskilling is key to driving adoption at scale.

5. Governance suites are the first line of defense

Alongside the benefits companies can harness from generative AI are the risks they must avoid. There are many examples of harmful or inaccurate content created when gen AI doesn't have guardrails. And without clear guidelines, there can be ethical issues and challenges with data privacy, ownership of AI-generated content, the amplification of biases, and potential misuse of the technology.

A robust, responsible generative AI strategy takes all stakeholders into account, including regulators, customers, employees, and partners. And it prioritizes transparency, accountability, data privacy, and the elimination of bias. At Genpact, we've built a responsible generative AI framework that:

• Protects clients' intellectual property, data security, models, and reputations
• Caters to evolving responsible AI demands and regulations by region and industry
• Manages the end-to-end gen-AI lifecycle
• Assesses the impact of generative AI on privacy through structured audits
• Evaluates the legal and policy implications
• Builds a responsible AI strategy with guardrails

With a framework in place, leaders can also make responsible AI part of their company culture.

How to integrate gen AI

To weave generative AI into a business, we've compiled the steps we believe lead to the greatest value:

• Focus on outcomes to make sure you introduce generative AI with an end-to-end approach. Enterprises that have mature digital journeys will have a head start
• Make gen AI part of your technology stack and not a point solution. Integrate it with data orchestration, cloud computing, and robotic automation to unlock true digital transformation
• Build data foundations by establishing high-quality data-acquisition systems and standardizing data-quality practices
• Experiment continuously to tap into the company's collective intelligence and adopt a co-innovation approach for generative AI. Do not fear failure but learn from the results, which will help with ruthless prioritization and accelerate ROI

Four winning generative AI characteristics

As companies evaluate how they can reshape their business models, services, products, and skill sets around generative AI, they need partners with the right experience and approach. Executives should look for the following characteristics:

• Democratizers: Companies that have invested in building the foundational models that have democratized access to AI. Think of OpenAI, Microsoft, Google, and Amazon
• Solution creators: These companies build on foundational models to deliver outcome-focused solutions
• Technology architects: To drive value, these service providers channel their efforts into the areas where they have deep industry and functional knowledge, understand the competitive landscape, and have fully mastered data. They can also use the right data to fine-tune models and create innovative technology solutions
• Data specialists: Companies that can clean and enrich data, create strong governance, and feed foundational models can help their clients use large language models (LLMs) responsibly and ethically. They make end-to-end change happen

For Genpact, our capabilities stretch from being solution creators and technology architects to data specialists. And we have strong partnerships with the major democratizers. Importantly, we own the meta-intelligence and algorithms companies need to access to succeed with generative AI, gathered and created over decades working with clients in their core functions across many industries.

Generative AI in the real world

With an almost endless list of possible ways to use generative AI, it's useful to focus on where it's already delivering value to enterprises. Here are just some of the ways we're bringing gen AI to life:

• Generative AI solutions are helping a fintech giant transform its know-your-customer and anti-money-laundering approaches by enhancing how it identifies potential fraudulent transactions
• A software development team had more than 1,800 custom applications built using many programming languages, so change requests took time to action. With generative AI, the product delivery team can increase throughput and reduce effort by 50% while sprint teams accelerate code delivery by >40% and test design activity by 60%
• The customer experience is at the heart of how a global media and entertainment company is rethinking how it resolves customer disputes. A gen-AI engine now analyzes online chat data and responds to customers in the right tone to address issues and leave them satisfied
• The procurement team at a global medical devices company has adopted generative AI to provide real-time answers to questions on contract clauses and payment terms. It can now quickly address vendor disputes and recommend actions
• At Genpact, gen AI has enabled our accounts payable helpdesk to respond to supplier payment queries 35% more efficiently by querying the ERP for payment info and structuring messages for agents. We expect this improvement to increase

Follow our best practices and real-world applications to speed the adoption of generative AI. By focusing beyond productivity, your company will create more meaningful customer relations, empower employees, and transform revenues, realizing the future of work today.