The pressure to implement AI-powered tools is rising, and everyone—from the C-suite to product managers (PMs)—has felt it. But despite the push for AI/ML adoption, only 14% feel ready to integrate this into their business.
AI has quickly become the priority for product teams across all types of companies, but a one-size-fits-all approach to implementation simply won’t work. After all, it’s no secret that some companies are more flexible than others. A global company with 40,000 knowledge workers will almost definitely move slower than the scrappy startup down the road.
As PMs like you navigate the ever-evolving landscape of AI/ML tools, they must lay the groundwork carefully. Are your people, systems, and end-product, and tech stack ready to navigate this new territory? Use this checklist to uncover potential pitfalls, identify strategic objectives, and confidently forge ahead.
Stage 1: Company culture and people
Your culture and workforce directly shapes mindset and adaptability to new scenarios and tools. A team that’s open to cross-functional collaboration and that’s growth-oriented will better navigate AI adoption, compared to a risk-averse culture that slows down creative problem-solving.
As you assess your product organization’s AI readiness, ask yourself:
- How open is your product team to change when adopting new AI/ML tools? Do you have a team or individuals that are experienced with AI tools and machine learning?
- Does your company or product team have a set of people and resources dedicated to implementing AI? If not, do you have access to external AI/ML experts?
- Does your executive leadership team support your product team’s AI implementation?
- Are there training or up-skilling plans in place to help your product team adapt to new AI/ML tools?
Stage 2: Data and infrastructure
AI relies on your product team’s underlying data to be effective. But most teams don’t have the centralized, secure, or hygiene to support AI readiness. Your tech stack and data dictate how easily you can integrate AI into native workflows, access data, and support increased power consumption.
Evaluate your readiness by asking:
- Do you have access to high-quality, relevant data you can train AI models on? Is the data organized, labeled, and stored to easily support AI analysis?
- Is your current tech stack (i.e. computing power, network performance, and cybersecurity capabilities) compatible with AI tools and frameworks? Do you have the storage to support computing power?
Stage 3: User experience
The end goal of most AI/ML implementations is to improve the experiences of your employees or customers. Adding AI to your workflows helps PMs focus on high-impact work (think: spending more time with end-users) and scalable personalization.
To avoid any potential pitfalls, consider these questions:
- Have you identified the main business goals your product team wants to achieve by investing in AI/ML tools?
- Have you identified any specific use cases where AI can improve your customer experience, like feedback management or suggested session replays?
- Does your product team have an AI roadmap? If yes, who owns it? How are you prioritizing different AI initiatives?
- How will AI-powered features, products, or internal workflows impact your end-user experience?
- Have you defined potential risks around AI implementation? If yes, what are your contingency plans to mitigate this?
Stage 4: Governance and compliance
While not the most glamorous part of your AI journey, building strong governance and compliance is perhaps the most critical. Without it, you can’t align work with business outcomes, eliminate bottlenecks, or make progress towards strategic goals.
Process-oriented product teams can scale up new tools (and maximize engagement). On the other hand, product teams without strong governance may have the best intentions, but still see their AI tools go ignored in day-to-day work. (Kind of like that long-forgotten bag of popcorn in your pantry).
To get started, ask yourself:
- Has your product team considered and assessed the ethical considerations and potential risks around using AI/ML tools? If you have identified risks what are your plans to mitigate them?
- Does your product team have a data governance framework to implement, monitor, and establish data quality, integration, and workflows of AI/ML tools?
- Do you have a plan to train your team so that your organization can effectively adopt AI? What metrics and KPIs will you track to understand adoption and engagement?
- Do you have a plan to prototype and roll out AI solutions in your organization?
When it comes to your product team’s AI adoption, time is of the essence. Get certified in AI for Product Management—now free!
