New artificial intelligence in business innovations are emerging daily that can help companies work smarter and faster. At DigitalOcean, our May 2023 Currents survey of 1600 developers and startup founders uncovered that 61% of respondents expect their usage of AI/ML to increase this year, compared to 41% the previous year. AI isn’t just a buzzword anymore; thoughtful implementation of the right AI solutions enables your team to get to market more quickly and delight users sooner.
Startups and small-to-mid-sized businesses, in particular, can leverage AI to offset limited resources and punch above their weight class when building products. A product org can tap into a range of AI product management tools to develop and launch products more efficiently despite constraints when it comes to capital. Not doing so could mean falling behind competitors.
Dr. Marily Nike, AI Product Lead at Meta, suggests that every product manager will be an AI product manager in the future. “All PMs should (or will!) leverage AI in some capacity when they build smart products,” she says. For product organizations and managers, it’s worth exploring the space and investigating how AI tools can help you and your team build better products.
This article will explore using AI in product management to accelerate each stage of the product development lifecycle—from gathering user insights to optimizing pricing models.
From speeding up your product development cycle to analyzing data at scale, implementing AI solutions provides advantages for your business. Business leaders seem to concur—a 2022 study from Deloitte on AI transformation found that 94% of those surveyed agreed that AI is critical to success over the next five years, while 79% reported full-scale deployment for three or more types of AI applications.
Here are five benefits AI delivers for product teams:
Efficiency. AI can drive greater efficiency in product development, enabling startups to do more with less. For example, AI technologies automate tedious tasks, freeing team members to stay in their zone of genius.
Speed. AI helps teams work faster, accelerating time-to-market. Every second counts in a tech environment where competition is fierce and raising venture capital is more challenging.
Data-driven decisions. AI can provide your company with data insights to inform product decisions. AI and machine learning tools can analyze data and reveal trends and patterns at scale, often more efficiently than humans.
Scalability. AI enables startups to scale crucial processes that might otherwise require large teams.
While AI unlocks benefits, it also carries potential risks. Awareness of these downfalls and mitigating them in product development is critical to using AI responsibility.
Here are five ways product organizations can approach AI cautiously:
Consider user privacy. Evaluate how your AI tools may impact user data privacy and security. Perform due diligence on providers’ policies and protections.
Factor in the risk of hallucination. Remember that AI models can “hallucinate” false correlations not grounded in reality. Validate insights with human oversight and triple-check any research or insights generated by a language learning model.
Evaluate evolving regulations. Stay abreast of emerging regulations governing the use of AI that may impact your product and organization.
Maintain human oversight. Keep humans involved in reviewing and validating AI-generated outputs. Don’t entirely hand off responsibility to algorithms.
Advancements in artificial intelligence are creating new opportunities for product organizations to operate differently. Product teams can drive innovation, efficiency, and growth by integrating AI into key processes.
A 2023 survey from McKinsey on generative AI found that 13% of respondents regularly use AI in product and/or service development. This included identifying trends in customer needs (7%), drafting technical documents (5%), and creating new product designs (4%).
But this is only the beginning. As new tools emerge and the AI space continues to advance, we’ll see many use cases for teams to build better products more quickly.
From wrangling team members to work on product initiatives to bringing a feature from ideation to launch, project management is critical for product managers. However, moving tasks across a Kanban board and writing weekly updates can be tedious and time-consuming. Luckily, this is a place where AI can step in. A 2019 study from Gartner suggests that, by 2030, 80% of project management tasks will be eliminated by AI. We’re already seeing how:
AI platforms can use machine learning to recommend optimal task assignments based on employee skills, workload, and priorities to enhance productivity.
AI-powered risk analysis can identify potential schedule or budget risks, allowing product managers to course-correct quickly.
AI analytics tools can generate project reports using natural language, freeing up product managers from manual reporting.
Here are AI tools for project management and coordination:
HiveMind. This platform lets AI kick off your projects and helps plan them in seconds—from building meeting agendas to writing business plans.
PMOtto.ai. This AI-powered assistant, integrated with GPT-4, promises better project management with less time and resources.
Asana Intelligence. Well-known in the project management world, Asana has added AI capabilities to their platform to help organizations hit their project goals faster.
Tara AI. Built with engineering teams in mind, Tara AI helps improve impact and scale product delivery by collecting and surfacing performance data with AI.
Carefully planning and setting the product vision and strategy is a crucial first step for product managers before diving into execution. Taking the time to define strategic direction aligns the entire organization and ensures all efforts ladder up to a common goal—and AI can help.
AI market analysis tools can quickly process data to reveal insights about market opportunities.
AI vision mapping tools can synthesize research and inputs to draft high-level vision and strategy documents.
AI design tools can rapidly prototype concepts to convey product and feature ideas better.
Here are AI tools for developing product vision and strategy:
Galileo AI. Want to convey a concept quickly? This tool creates UI designs from a simple text description, helping you go from ideation to presentation.
Akkio. Use this AI tool for analysis to understand your data better and build visualizations quickly to drive better decisions.
Userdoc. Explore this AI-assisted tool to build better software requirements, create user personas, and develop user journeys.
Understanding the competitive landscape is essential for product managers to make strategic decisions. But compiling data on competitors—their positioning, pricing, product specs—and analyzing market trends is time-consuming. AI tools can automate gathering and synthesizing market insights to inform product strategy.
AI web scrapers can rapidly pull pricing, features, and other data from competitor websites.
AI sentiment analysis tools can digest customer feedback on competitors at scale to surface strengths and weaknesses.
AI market research assistants can generate detailed market analysis reports using natural language capabilities.
Here are AI tools for conducting market research:
Crayon. Use Crayon to monitor your competitors in real-time and help your sales team stay ahead with millions of collected data points interpreted by AI.
Pecan. Turn your data into more intelligent predictions, taking the mystery out of business decision-making with AI-surfaced insights.
Clearly defining product requirements and specifications is crucial for setting engineering teams up for success. However, compiling all the inputs needed and translating them into detailed docs can become complex and tedious. AI can help streamline and enhance the requirements process in several ways:
AI can analyze customer research data to automatically tag key needs and extract requirements.
AI writing assistants can take product briefs and generate detailed technical specifications.
AI can quickly synthesize cross-functional stakeholder feedback to build consolidated requirement docs.
Here are AI tools for defining product requirements:
ChatGPT. A multi-purpose AI chatbot from OpenAI, ChatGPT can provide product managers with rapid information retrieval, suggestions, and language structuring, streamlining the creation of product requirement documents.
WriteMyPrd. This tool collects vital information on your product—from feature name to feature list—to create a comprehensive PRD for your next product feature.
ProductBoard. An AI advisor for creating successful products, Product Board helps teams clarify their ideas, determine their value, and create a roadmap with requirements.
Product roadmap prioritization, determining which features to prioritize and include in upcoming releases, is one of product management’s most critical and challenging aspects. There are always more potential features than development resources. AI can inject data and introduce objectivity into roadmap planning and feature prioritization.
AI can score and rank potential features based on multiple inputs like customer requests, revenue potential, or cost.
AI sentiment analysis provides quantifiable data on customers’ feelings about potential features, scouring social media and other data sets for insights.
Predictive AI modeling can forecast the business impact of features to inform prioritization.
Here are AI tools for prioritizing features:
Taskade. This AI productivity tool helps product organizations create AI-generated product roadmaps to streamline their planning process.
Aha!. This road mapping software leverages AI to collect and analyze customer data and create powerful reports with insights on what to build next.
Selecting the right key performance indicators (KPIs) and monitoring them is essential for understanding product and business health. However, analyzing volumes of data to derive insights can become complex. AI is making it easier for product managers to leverage metrics and data without reliance on a dedicated data analyst.
AI can automatically surface insights and trends from product data that humans may miss.
Natural language AI enables asking questions of data sets to retrieve relevant KPIs quickly.
AI anomaly detection and alerts can identify changes in KPIs, notifying product managers of potential issues.
Here are AI tools for defining and tracking KPIs:
Analytics Intelligence in Google Analytics 4. This newly introduced set of Google Analytics features uses machine learning to help you better understand and action your company’s data.
ProdPad. Use AI features to generate a series of key results for any product objective and set intelligent goals and meaningful targets.
Successful product launches require extensive cross-functional coordination and managing complex details, from development to marketing activities. With so many moving parts, AI can help automate parts of launch management to increase efficiency.
AI assistants can automatically create comprehensive launch plans with integrated timelines to keep all activities on track.
AI scheduling tools can optimize launch timelines and marketing campaign activities as progress is tracked.
Generative AI writing tools help you and your growth team build marketing collateral, instead of writing it from scratch.
Here are AI tools to promote the product:
Writer. Build AI into your release process with a writing tool that helps you build product descriptions, help center articles, personalized landing pages, and more.
Product Launch AI. This AI assistant helps teams with their product launches, including generating taglines for Product Hunt, writing product descriptions, and suggesting where to promote your launch.
Building products is an ongoing process, even after launch day. Continuously gathering qualitative feedback and user insights is critical for building products that continue to meet the needs of users. However, synthesizing large volumes of open-ended feedback is difficult to do manually, especially when it comes from everywhere—emails, social media updates, app review websites, etc. AI is making it easier to leverage user perspectives.
AI can automatically analyze and categorize open-ended feedback at scale.
Sentiment analysis tools can detect how users feel about product features in feedback.
AI can surface common themes and extract insights from user comments.
Here are AI tools for gathering user feedback:
Remesh. Access real-time qualitative insights at scale using AI, with market research insights on what people say about your company and products.
Clarabridge. Understand the emotion and intent of what your customers say online with this conversational analytics tool powered by machine learning.
The rapid evolution of AI is unlocking new opportunities for product organizations to work more efficiently. By selectively implementing the right AI solutions—both incumbent and emerging solutions—teams can drive more significant growth while focusing their energy on the most impactful work.
With DigitalOcean’s recent acquisition of Paperspace, businesses have a golden opportunity to harness the power of AI and deploy machine learning models in the cloud. Build and scale AI models on low-cost cloud GPUs with Paperspace.
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