Get Ready To Harness Generative AI And Drive Success
There is much hype around generative AI as it continues being applied across the entire enterprise, from marketing and design to sales. How can product management get in on the action, and how might product managers leverage genAI to drive greater business success?
Generative AI is not just a fad. Its ability to create relevant new content from mounds of existing data and targeted prompts has the creative and digital world very excited. For product management, it offers opportunities to streamline operations, enable deeper customer research, and improve collaboration.
What’s In It For Me?
Product managers spend a lot of time understanding customer needs and coming up with offering ideas. Generative AI tools can help with activities such as:
- Quicker identification of customer needs. A company’s issue isn’t having too little data — it’s having too much of it and not knowing how to best synthesize it all. Generative AI’s strength in knowledge management and summarization can help product managers more easily aggregate customer needs data and ensure that they don’t leave key pieces of information out when synthesizing customer needs as inputs to offering strategy.
- Fast and robust idea generation. Product managers are considered key resources for driving growth through innovative solutions to customer problems, especially problems that are not well understood. Given the best information and prompts, genAI can help product managers by creating their initial list of solution ideas and can be used as a “partner” for fleshing out ideas to make them more effective and feasible. This is also a low-risk activity if your team is risk-averse.
How Can I Get Started?
You don’t have to wait for a perfect generative AI solution. Here are some ways to start gaining value:
- Customer research. Lots of your customer data can be analyzed with large language models, especially the data living in your organization’s contact center. Use generative AI to engage with data from support tickets, community discussions, and interviews to hear your customers’ spoken and unspoken needs more completely. In turn, you can identify current and emerging needs faster and more regularly.
- Persona-based documentation. The use of generative AI for the generation of content in customer support channels is the top use case — 48% of survey respondents agree, according to Forrester’s research. Consider using generative AI to support the creation of product documentation for specific personas (e.g., engineering, professional services, marketing, sales) to make the end product more personalized and user-friendly. This can directly help your customer support function, which supports an improved customer experience and better retention rates.
Watch Out For Pitfalls
Make sure that the necessary guardrails are in place to factor in the following:
- Lack of data quality, privacy, and security can lead to mistrust and legal consequences. A common phrase across industries is “garbage in, garbage out.” Quality data must train your generative AI models to generate quality content. Its business use must be ethical and responsible — lack of consideration here can lead to creating harmful content, violating copyrights, and disclosing sensitive information. Plan for security and governance around AI use in your company to reduce risk and build trust.
- Don’t overrotate toward or undervalue generative AI. Many organizations are running full steam ahead with generative AI, while others are averse to even experiment. It’s best to take a measured, thoughtful approach to harnessing this technology for product management. Investigate, play with, and share your learnings from generative AI throughout the organization to encourage teams to identify new use cases.
This post was written by Principal Analyst Sam Somashekar and it originally appeared here.
