How AI will change product management and product marketing

What’s in an AI?

Most people know AI from the movies and those are pretty mixed examples– for every ominous robot overlord like HAL or Skynet there’s a friendly little trash-compacting robot like WALL-E who just wants to help.

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The current reality is far from an actual artificial intelligence that fully replicates general human intelligence. What we have today is software that has been trained on specific data sets to address a particular set of problems.

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The hottest examples right now are large language models like GPT-4 and its derivative ChatGPT, a generative AI that answers questions ranging from “what should I have for dinner tonight?” to more cleverly and specifically worded prompts requesting epic poems in the style of Dr. Suess.

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Here’s how product professionals are putting these advances in AI to work for them (hint: it’s not about epic poetry, at least not yet).

What PMMs and PMs are using AI for today

As with all technology, the most basic aspects get commodified first. These are the things almost anyone can do with generative AI– inputting simple prompts with minimal context in order to build what they need, then reviewing for accuracy. These tools cannot yet handle a complex request like writing hyper-tailored product copy that takes into account your company’s style guide, existing brand copy, competitor’s messaging, and currently trending topics across social media, for example.

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Here’s what the reality looks like in current product workflows, for product marketers and product managers.

Copywriting and proofreading

One of the first things users flocked to AI chatbots for was help with copywriting and proofreading. They’re especially helpful tools for anything that needs multiple iterations, like advertisements or social copy.

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Product professionals can use them to kick start any copy they’re responsible for, including product specification drafts or marketing emails. Drafts can be run back through to tweak and improve them and as teams grow comfortable with the process (like writing good prompts), save time.

Ideation

These AI tools are also helpful whenever users need a lot of ideas fast; they’ll spit out lists of ideas for everything from content, to audiences, category names, and more. Teams can take those lists and narrow them down to workshop what will be best for their content strategy or audience.

Upskilling

Product professionals can also turn to these tools for a crash course in a new industry, instantly pulling up research to dive into or learning about new frameworks from a particular prompt. (The accuracy isn’t always 100% though, and must be checked.)

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The more artistically inclined AI tools can create mood boards for product professionals to share with designers much faster than it might take to create the same thing “manually” via a tool like a collaborative white board or Pinterest.

The bottom line

There’s a lot of promise in these tools, but they’re currently best used as a way to jumpstart workflows. They’re not producing final products that are 100% reliable; they still need to be refined and reviewed for accuracy by humans. And they cannot handle complex tasks that take multiple inputs– and subject matter expertise– into consideration.

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At Ignition, we’re building the next step up from these more generalized tools: our built-in AI is more contextualized and requires data inputs that are more than just a simple question. Our tools require structured data to work from– that we get from what you’ve given us– and specific domain expertise we’ve built up to generate better results for you and your team.

The next step– and what we’ve launched

The next step for AI isn’t a giant leap to becoming Skynet, but for systems to evolve to those that can create contextualized results that users need based on their existing data, combining it with specific relevant external data. Things like review scores for competitors, for example. These tools would also be hyper-trained based on expertise in a way that goes beyond simply relying on the user to write an exceptionally good prompt.

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Here’s what Ignition has launched that’s taking those steps forward.

Insights

You need to plug into places where customer insights are already living– Zendesk tickets, customer calls– and have those insights flow directly into the important work you’re doing. You need product insights: what features are the most requested, which are the most loved, and what the top complaints are so your roadmap reflects the highest priority path forward.

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You also need messaging insights: what is driving value, what problems are being solved, what is confusing your target market and customers, what language those groups are using and anything else pertinent to your team.

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You need win/loss data, for insights into why deals are or aren’t closing and exactly what is driving deal size.

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What if you had something that not only did all of that, but also provided summaries that are both segmented and actionable? That broke everything down into key themes with large-scale, qualitative analysis?

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Sound too good to be true? It’s not! We just launched it and you can try it now!

Enablement 2.0: ChatGTM

The next step in enablement is contextualized search based off of your data. Ignition is taking the standard AI chatbot experience and looking across your roadmaps and go-to-market plans, then having natural language queries ask questions like “How do I talk to a CTO about X for this product launch?” and getting a messaging guide in return.

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This also enables greater visibility into what’s going on without adding more to your (presumably already overburdened) workflow. How? A Slack integration allows you or anyone on the team to ask questions directly in Slack, giving key stakeholders their answers without going to you for them every single time.

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What kinds of questions? Glad you asked! Both status questions like “What’s blocking the launch and who is responsible?” or “What does the launch timeline look like?” and messaging questions like “How do I sell X?”. Even better, the answer for the latter would include a messaging guide, a list of links to the most important assets, and anything else relevant to the particular persona being sold to.

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Meet AI GTM Enablement– the AI chatbot that uses your specific strategic data to keep the whole team on track.

Generative strategy

From generative AI, get generative strategy. Work at a more strategic level knowing what your competitors are actually good and bad at based on real customer feedback. That feedback is used to create battle cards, messaging and positioning (what category perceptions have your competitors created?), and everything you need to set your team up for success at the next level.

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This is more than just taking customer reviews and compiling them; you’re getting battlecard talking points that take into account the competition’s strengths and weaknesses relative to your company (including things like their website messaging) so customer-facing teams are ready to answer all of the most difficult questions strategically.

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The best part? You didn’t have to spend hours creating everything manually.

Looking into the future

While we don’t have a crystal ball, it’s fun to make some educated guesses on what’s beyond the horizon with AI technology.

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It’s not impossible that in the next few years we’ll have complete asset generation– like images that have the right amount of fingers– or “final version” worthy copy for landing pages and more.

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Project management-enabled AI tools might be fully automated and capable of decisioning.

Finally, we might see predictive market intelligence giving the clearest path forward to success.

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We hope you’ll take us along for the ride as we build the future right here at Ignition.

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