Product Operations and Large Language Models
Enabling better product development with AI-powered tools.
Product Operations is ‘an operational function that optimizes the intersection of product, engineering, and customer success’. As a developing discipline of product management, it’s valuable for scaling tech companies that need to improve their systems and keep teams aligned through high growth.
To stay updated, I recently attended the Product Operations Festival hosted by the Product Led Alliance, an online product management community. They hosted helpful speaker sessions with leaders sharing expertise from their experiences working at prominent tech companies.
Here are some common themes I observed, which resonate with experiences from my own work:
Protect your time: often as a team of one, firmly prioritize your tasks and know when to say no. Delegate responsibilities where possible, and establish a clear system for tracking requests and progress that you can share with others.
Team Cohesion: a shared understanding of product and company goals is necessary for effective alignment across the organization. Build trust through proactive communication and establish feedback channels that ensure inclusivity.
Do more with less: with limited resources, finding and implementing efficiencies for multiple fast-moving teams is essential for role success.
Unlocking Efficiency with LLMs
Product Operations professionals analyze large amounts of varied data, including transaction data, customer feedback, product requirement documents, standard operating procedures, and more.
Large Language Models can process this complex information at scale and offload many time-consuming tasks; this empowers product teams to work on more strategic tasks and get the maximum value from their data, a key competitive advantage for companies now and in the future.
Hence, it looks like we can do more with less.
Let’s take a look at standard LLM use cases outlined on Nvidia’s website:
Generation (e.g., story writing, marketing content creation)
Summarization (e.g., legal paraphrasing, meeting notes summarization)
Translation (e.g., between languages, text-to-code)
Classification (e.g., toxicity classification, sentiment analysis)
Chatbot (e.g., open-domain Q+A, virtual assistants)
For each category, there are plenty of opportunities to further product operations outcomes:
Generation
Draft communications announcements, requirements documents, training materials, etc.
Brainstorm ideas for strategy, create frameworks for problem solving.
Write a Google Apps, Python script to automate simple tasks.
Summarization
Condense user feedback, meeting notes, and research reports into key insights.
Explain complex technical specifications and problems to non-technical stakeholders.
Translation
Translate documentation across languages for global products.
Better research markets around the globe with diverse languages.
Classification
Analyze customer support tickets and triage them more effectively by type, sentiment, etc.
Better flag spam and fraudulent activity in daily operations.
Chatbot
Internal knowledge management - context-specific chatbots can provide employees access to company data quicker.
‘Agentic workflows’ are an exciting new development that allow LLMs to not only generate, but do as well. Starting with automating simple tasks, it has potential for much more.
LLMs and AI-powered tools offer innovative ways to optimize all stages of the product development lifecycle. The wide proliferation of “.ai” domain websites reflects our growing interest in AI innovation, including additional use cases for generative AI like image and audio tools.
As the technology matures, companies will need to adapt and product teams must carefully weigh factors such as data privacy, ethics, potential for bias, and maintenance costs when choosing between pre-built and custom LLM solutions to ensure responsible implementation.
Increased accessibility suggests that building custom tools will become a cost-effective and practical strategy.