The building blocks to maximize AI-powered outcomes
The shift to becoming an AI-first enterprise isn't easy. But it must be done quickly: 57% of Fortune 1000 companies report that their boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year.3
Based on our experience helping enterprises scale AI, we've identified several building blocks for success. They include embedding analytics in processes and workflows, establishing a cloud-based technical architecture, and creating a scalable operating model. But here are three steps to focus on in particular:
1. Prioritize the right use cases
With so many places to start, deciding where your investments will deliver the greatest value can be difficult. I recommend using generative AI where you don't need high levels of accuracy or deterministic results, focusing on the opportunities with the greatest potential.
For instance, in healthcare, enhancing the patient experience is a core goal. Generative AI empowers healthcare practitioners to make better decisions by creating personalized patient health summaries that they can review based on encounter and claims data. As a result, healthcare professionals can speed up patient response times and improve patient outcomes.
As you weigh up opportunities, be mindful not to fall into the productivity trap. Instead, prioritize creating end-to-end value and reimaging outcomes – not just doing the same things faster.
2. Prepare employees
Becoming an AI-first enterprise requires significant change management and upskilling, especially as many employees are wondering, "What will happen to my job?"
AI will empower employees, open new career opportunities, and allow them to tap into the full value of their unique knowledge, augmenting their impact on the business. But only if you invest in upskilling them. And again, velocity is key.
We're on this journey ourselves, deepening data literacy skills across the business. We've given 70,000 employees new data skills since 2021, and over the past two months alone, we've trained 20% of our colleagues in generative AI – we will reach 40,000 by year-end. Generative AI is at play here too, giving learners instant access to Genpact's collective intelligence through our learning platform, Genome.
3. Make AI-driven decisions responsibly
Demonstrating that your AI practices are responsible and explainable has always been important, but even more so now that employees in any part of your business can now access generative AI tools independently. With generative AI, we've entered a maze of ethics, copyright, and intellectual property complications.
Developing a strategy for these evolving concerns is daunting, and many companies feel ill-equipped to do so. That's why we've created a responsible generative AI framework that you can use as a launching pad or a plug-and-play solution that maintains your reputation by:
- Protecting your IP, data security, and models
- Taking into account how responsible AI differs by region and industry with experts who understand the regulations
- Enabling responsible decision-making