How automation is redefining the role of the product manager

Written by Pippa Armes  | 

6 min

 

Machine learning (ML) and artificial intelligence (AI) seem to be popping up all over the place. From the cars we drive, to the shows we watch, to the health care we receive—ML and AI are some of the buzziest buzzwords in tech today.

For some, the idea of AI brings to mind images of robot overlords overtaking the planet. For others, ML evokes the fear that human intuition will one day be replaced by algorithms. But what does a world augmented with ML and AI actually mean for us humans? 

Hannah Chaplin (Director, Product Marketing at Pendo) and Dan Dalton (Senior Product Manager at Pendo) recently sat down with Product-Led Alliance to discuss how automation is redefining the role of the product manager. They highlighted the top ML and AI trends in product management today, the impact these new technologies could have in our products and the field of product management, and how product managers can embrace—not fear—these changes.

Be sure to scroll to the bottom of this page to watch the full webinar!

 

ML and AI is everywhere . . .

Machine learning and artificial intelligence have been around for decades. You may be surprised to know that AI as a field of study was founded way back in the 1950s. And while the technologies themselves aren’t necessarily new or exceptionally innovative, how we’re applying their principles is.

If you’ve used a streaming service like Netflix or Spotify, you’ve benefited from automation. Those recommendation engines that display personalized suggestions based on your previously streamed content? That’s ML and AI. Google’s search bar also relies heavily on automation. Its ML-powered “Did you mean?” feature enables more accurate spelling suggestions, and its AI helps their search functionality serve users more relevant, highly contextual results.

Even annoying interruptions like advertisements in our social media feeds are made better with ML and AI. ML allows advertisers to target us with relevant ads (often eerily in-line with our search intent or even conversations we’ve had with friends)—resulting in a far more delightful, and less abrasive, marketing experience.

Seamless experiences like these are what users expect from all the technology they engage with. Speed, relevancy, and ease are the name of the game—and automation is the way to win.

. . . But there’s untapped potential in B2B

To date, the business-to-consumer (B2C) space has primarily driven innovation in how we leverage machine learning and artificial intelligence in our daily lives. But business-to-business (B2B) applications present an exciting opportunity for product managers to think big about the possibilities of automation, too. 

To provide the same B2C-level experiences users have come to expect from their tech, product managers need to find opportunities to productize ML and AI, and leverage it to improve their own products. One of the biggest B2B pain points automation can help alleviate is the question of “What do I do with all of this data?!”

Using ML and AI to understand data

B2B companies used to struggle with a lack of data. Then we leaned in and learned how to better capture it—soon making data this century’s most valuable commodity. Today, B2B product managers have an overwhelming amount of data at their fingertips, growing by the second. The challenge is now figuring out how to understand and use it.

Automation is an invaluable way for product managers to organize and act on all that information. It can help teams quickly visualize and analyze massive quantitative data sets or extract themes from pools of qualitative, Voice of the Customer (VoC)-driven feedback. This qualitative analysis, in particular, has been largely untamed to date—and incredibly time and resource-intensive. With ML and AI, features like tagging, theme extraction, and sentiment drill-downs become game-changing product possibilities. Freeing up product managers and analysts to focus on the high-value work they love doing, while turning all those data points into actionable insights.

Specialized ML and AI roles will lead the way

The key to operationalizing automation in product management is having the right mindset and putting skilled machine learning / artificial intelligence teams in place within your organization. 

Product managers should embrace the opportunity to collaborate with data analysts to ensure the data processes they have in place are valid and balanced, with equal weight between quantitative and qualitative data. They should also look to analysts to help them truly understand the data and make it actionable. And they should feel encouraged to use their ML and AI-driven findings as a tool to challenge their hypotheses and extract the information they need—not just the validation they want.

“Machine Learning Product Manager” is likely to emerge as a hot title as automation becomes more prevalent in B2B tech. ML product managers should focus on how to best solve real customer problems—knowing enough to understand the technology so they can productize and apply it to reach business outcomes.

Product Operations teams are also leading the charge for ML and AI. By asking questions like, “How can I automate this time-consuming process?” or “How can I triage this at a high enough level to operationalize this data?”, they can build systems that improve the data processing functionality of the product. This helps empower their product teams to act on the wealth of data at their disposal, not become overwhelmed by it.

Embracing the change

As our understanding of automation tools continues to grow, it’s critical for product teams to stay open-minded and informed. The better we can harness the potential of machine learning and artificial intelligence, the more fruitful our applications of these technologies—and their resulting outcomes—will be.

So how can product marketers embrace this brave new world of automation?

  1. Read up
    Stay curious. Look for opportunities to learn the basics and stay up to date on the latest and greatest in ML / AI.
  2. Think big
    Look beyond typical use cases. Dare to dream about what could be possible with automation (and all the cool projects you could accomplish).
  3. Chill out
    Don’t be afraid of ML and AI! Shift your mindset to focus on the opportunities presented by automation.
  4. Explore technologies to help you
    Leverage automation to help you work smarter and build better. How can you incorporate ML and AI into your product to drive an incredible experience?

 

If you’d like to learn more about how automation is redefining the role of the product manager, check out the full webinar recording here: