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.