- Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
- To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
- This is part 14: Digital product engineering.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
This is part 14: Digital product engineering.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
This is part 14: Digital product engineering.
Eliminate the guesswork in product development
What do customers really want? To crack that ever-changing cipher, digital product teams must sift through mountains of data, from market research and user surveys to device metrics, all while navigating complex code bases and enterprise architectures. It’s a perpetual, painstaking process, and there’s no guarantee they’ll get it right. Even when market signals and metrics seem to point to a sure-fire win, products can inexplicably flop. Or a release flying under the radar can lead to an unexpected spike in adoption.
Generative AI helps businesses optimize the product development process—from streamlining ideation to rapidly testing and validating features—saving money and accelerating speed-to-market. At the same time, it frees humans to focus on solving complex engineering challenges and differentiating products through design, UX, and UI—the creative tasks that have the biggest impact on customer loyalty and satisfaction.
Generative AI can help digital product teams hit the mark more consistently by analyzing vast stores of data faster and more effectively than human teams ever could. Using machine learning algorithms to identify patterns and trends in customer behavior, generative AI can quickly uncover unmet needs, suggest dozens of features or new products that could fill a gap—and even validate these options against specific business criteria.
It also makes it possible to develop dynamic products and hyper-personalized experiences that can quickly adapt to shifting customer demands and rapidly validate changes with customers. Given these game-changing capabilities, it’s not surprising that 86% of executives say generative AI is now a critical part of digital product design and development.
The IBM Institute for Business Value has identified three things every leader needs to know:
1. Generative AI helps products hit the high bar of hyper-personalization at scale.
2. Teams using generative AI can conceptualize and evaluate new products in minutes—not days.
3. Rapid code generation frees teams to double down on design.
And three things every leader needs to do right now:
1. Redesign product development to derive high-value product insights from every customer interaction.
2. Build augmented teams to prepare for an influx of generative AI-infused workflows.
3. Upskill product teams on experience and innovation.
Additional content
Meet the authors
Anthony Marshall, Senior Research Director, Thought Leadership, IBM Institute for Business ValueCindy Anderson, Global Executive for Engagement and Eminence, IBM Institute for Business Value
Christian Bieck, Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value
Nisha Kohli, Global Research Leader, Customer Experience Transformation, IBM Institute for Business Value
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Originally published 29 April 2024
This chapter is available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
1. Hyper-personalization
+ Generative AI
What you need to know
Generative AI helps products hit the high bar of hyper-personalization at scale
Imagine a world where every product is tailored to a specific customer—where mobile devices, subscription services, and the Internet of Things work together to curate experiences for an audience of one. This is the world of hyper-personalization, and it’s no longer a distant dream.
As generative AI comes of age, executives expect it to pave the way for personalized experiences at a scale we’ve never seen. By analyzing every click, swipe, and interaction, generative AI can stitch together bespoke product experiences for every customer. But only 30% of organizations have been able to harness this power, tapping generative AI to quickly analyze and summarize customer feedback. Those leading the way have an early edge: They’re 86% more likely to be creating hyper-personalized experiences than their counterparts.
While only a quarter of organizations are using generative AI to create hyper-personalized digital product experiences today, that figure is expected to more than double to 64% by the end of 2024. Using generative AI in tandem with IoT could be a powerful way for companies to deliver true hyper-personalization at scale. IoT devices can feed torrents of data into AI and generative AI models, which may be why executives say IoT will be a top digital product disruptor, after AI and generative AI, over the next five years.
Looking ahead, 70% of executives expect generative AI to improve the personalization of their digital product portfolio. How far they go—and how fast they get there—will likely decide who gains a competitive edge. In the generative AI future, products will need to be functional and personal, adapting to meet every customer’s unique preferences, needs, and expectations, no matter how rapidly they change.
What you need to do
Redesign product development to derive high-value product insights from every customer interaction
Stop letting market trends catch you by surprise. Bypass the competition by cultivating proprietary data inputs and differentiating how you use generative AI. Continuously learn and generate the experiences, products, and content customers want—at exactly the right time.
- Think beyond cross-sell and upsell. Capitalize on the UX/UI potential of hyper-personalization by using generative AI to create dynamic interfaces that adapt based on user behavior, preferences, and context. Customize everything from search results, product designs, and even pricing to increase customer engagement and drive revenue.
- Invite customers to incorporate their data into product experiences on their own terms. Let customers opt in to sharing their data and clearly communicate how it will be used and protected. Tap generative AI to uncover hidden customer preferences and use predictive analytics to forecast what customers will want in the future.
- Tap into customer data to create hyper-personalized experiences. Orchestrate disparate data, including from IoT devices, to enrich the user experience. Use generative AI to map your product priorities to data-driven customer pain points. Keep your product roadmap relevant and targeted by using generative AI to continually refine a backlog that will deliver the most business value.
2. Ideation
+ Generative AI
What you need to know
Teams using generative AI can conceptualize and evaluate new products in minutes—not days
Generative AI has turned the traditional product design process on its head. Gone are the days of endless brainstorming and exhausting pitch sessions. Today, generative AI can use large data sets to incubate dozens of ideas that have high market potential in seconds—freeing teams to validate with customers and focus on the best opportunities.
As this technology matures, two-thirds of executives anticipate that generative AI will inform—or even create—their product roadmap by 2026. Already, nearly one-third of organizations are using generative AI for digital product idea generation. And these organizations have an edge over the competition. Companies that have embraced this early use case delivered a 17% revenue premium for new products and 5% greater revenue from existing product enhancements in 2023, compared to companies that haven’t deployed generative AI for this purpose.
But the revenue boost is just the beginning. Nine in 10 executives already using generative AI for product idea generation say it differentiates their company from the competition by helping them respond to customer needs and new business opportunities faster. Going forward, they also believe generative AI will positively impact product differentiation (88%), product trust (83%), and product quality (80%). They’re consistently more optimistic than peers who plan to implement generative AI for product idea generation by the end of 2024, indicating that experience has increased their confidence.
Organizations already using generative AI for product idea generation are getting ahead of the curve by building the foundation needed to augment human work with AI: 22% more of these organizations are focused on formal prioritization systems, 29% more are focused on building interdisciplinary teams, and 39% more are focused on governance. But executives say the skills shortage is the top constraint that could hold digital product initiatives back.
While generative AI can create product ideas at lightning speed, it is humans who must review, validate, refine, and perfect them. This means people are more important than ever—as are the systems and processes that will support human-machine partnerships as they evolve.
What you need to do
Build augmented teams to prepare for an influx of generative AI-infused workflows
Leverage generative AI to both ideate and rapidly validate a high volume of ideas with customers. Focus the product team’s talent on reviewing, enhancing, and building out the ideas that seem most likely to succeed in the market.
- Treat generative AI as a team member. Embed generative AI to create team workflows that are truly augmented. Define which inputs and outputs team members and generative AI assistants are responsible for, respectively. Ask generative AI to carry out discrete activities. Use it to analyze feedback, generate design options, cut development time, or reduce wasted effort.
- Reinvent the review process to lower costs and improve efficiency. Implement an idea management system to track a high volume of AI-generated ideas, patterns, and trends, including KPIs that help predict success. Streamline the process of generation, evaluation, and implementation of ideas.
- Augment repetitive tasks to drive down testing costs as the pace of innovation increases. Generate and execute test cases based on code and product requirements to reduce the likelihood of bugs and defects in rapidly evolving digital products.
3. Design
+ Generative AI
What you need to know
Rapid code generation frees teams to double down on design
Consumer expectations are evolving at breakneck speeds—and product teams are racing to keep up. Tapping generative AI for rapid code generation can help them roll out prototypes faster without sacrificing the quality and design that customers demand.
How does it work? Generative AI speeds up the coding process, letting teams test and iterate faster to increase their speed-to-market—if development teams know how to use it responsibly. With the right training, governance, and adoption incentives, generative AI can help teams move faster while managing risk, freeing up resources to focus on the creative aspects of UX and UI design that lead to a better overall user experience.
Today, 87% of executives say their organizations sink at least a fair amount of effort into testing code, while 83% say the same for developing new features quickly in short release cycles. And they’re eager to relieve themselves of this burden.
More than 6 in 10 leaders plan to use generative AI for code generation in their digital products by 2025, rising to more than 9 in 10 by 2026. But there’s a real benefit in starting early. Only a quarter of organizations have implemented generative AI for digital product code generation so far, but these pioneers are already seeing real results.
They’re 35% more likely to outperform their peers in revenue growth and 48% more likely to say their teams dedicate significant effort to UX and UI design—focus areas that do more to differentiate them from the competition. What’s more, only 30% of executives at organizations that have already implemented generative AI for code generation say UX and UI design is a challenge, compared to 45% of those that plan to do so by 2026.
What you need to do
Upskill product teams on experience and innovation
Identify obvious time and money drains in the build and test cycle that can be powered by generative AI. Redistribute these resources in a way that supports the development of better UX/UI and more innovative products.
- Liberate developers and designers from traditional skill limitations. Encourage teams to experiment with new training models that will boost their generative AI acumen so they can use it creatively. Allocate dedicated research and development days and sponsor hackathons to give teams opportunities to enhance their skills.
- Offer more training on creativity and customer context. Advocate for all team members to gain domain expertise in experience design. Encourage collaboration within cross-functional teams to enable strategic innovation. Provide opportunities for experimentation without fear of failure.
- Expand the role of testers into user research. Reskill quality assurance testers to support higher-value testing activities, such as concept validation and usability testing with customers.
The statistics informing the insights on this page are sourced from proprietary IBM Institute for Business Value data, including a performance management and benchmarking survey of 450 global digital product leaders on their AI adoption in digital product and its impact on metrics, conducted from December 2023 to February 2024.
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Additional content
Meet the authors
Anthony Marshall, Senior Research Director, Thought Leadership, IBM Institute for Business ValueCindy Anderson, Global Executive for Engagement and Eminence, IBM Institute for Business Value
Christian Bieck, Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value
Nisha Kohli, Global Research Leader, Customer Experience Transformation, IBM Institute for Business Value
Download report translations
Originally published 29 April 2024
This chapter is available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
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