Charlotte Evans Will

Quite a bit! I see AI playing a key role across many points in the launch journey, but you can break it down into 3 simple categories: 1. Develop your story 2. Land it 3. Optimize it Let’s take each piece at a time. 1. Develop your story 1. Know your market — AI can help synthesize key market insights across a wide range of dimensions: 1. Desk research on your target personas and segments 2. Summarize more lengthy secondary research for you (UX research, customer interviews and quant, if you are in the b2b space then analyst reports) 3. If sales is an important channel, you can use AI to summarize customer calls instead of having to ride-along or get second hand accounts 4. Competitive intelligence (see other question that I address on this point) 2. Customer advocacy — Source hero customer stories and testimonials 1. If your company uses a Revenue AI platform, you can mine conversational data to find winning customer stories and soundbytes rather than hoping a seller will serve it up on a silver platter for you. 3. Naming — Brainstorm naming options for features and products you’re launching 4. Messaging — Refine your storytelling with a built-in copy editor 1. Whether it’s a keynote script, blog post, social copy, website or thought leadership content, AI is a great built-in copy editor 5. Creative — I haven’t tried this yet, but you could use AI to help generate your key visual assets 6. Localization — AI can do the heavy lifting for your translations (text and even audio) so you can quickly scale to many markets 2. Land your story 1. You can use AI to predict which customers to target 2. You can use AI to tailor your message to your target audience across your channels. There are many AI solutions for account based marketing and paid media more broadly. 3. If you roll new messaging or product activations out to the sales channel, you can use AI to track whether sales is adopting your messaging (down to the talk track and slides) and the impact on important business results like win rates. 3. Optimize 1. If your media team is using AI campaigns, it’s important to understand who you end up converting because your targeted audience may not convert as well as another one. I recall an example from my Google Ads days where a shelf stable food company had been targeting doomsday preppers without realizing that sailing and boating enthusiasts were an equally good (yet radically different!) audience for them. Imagine the shift in positioning and marketing based on that campaign insight. AI is able to uncover these unexpected pockets of demand for you. 2. Contextualize product adoption and NPS trends through customer insights gleaned by AI from various customer interactions. By knowing what your customers are saying and feeling, you can better guide product roadmap, positioning, messaging and go-to-market strategies.

[note this is the same answer to several overlapping questions] AI has a key role to play across many points in the launch journey, but you can break it down into 3 simple categories: 1. Develop your story 2. Land it 3. Optimize it Let’s take each piece at a time. 1. Develop your story 1. Know your market — AI can help synthesize key market insights across a wide range of dimensions: 1. Desk research on your target personas and segments 2. Summarize more lengthy secondary research for you (UX research, customer interviews and quant, if you are in the b2b space then analyst reports) 3. If sales is an important channel, you can use AI to summarize customer calls instead of having to ride-along or get second hand accounts 4. Competitive intelligence (see other question that I address on this point) 2. Customer advocacy — Source hero customer stories and testimonials 1. If your company uses a Revenue AI platform, you can mine conversational data to find winning customer stories and soundbytes rather than hoping a seller will serve it up on a silver platter for you. 3. Naming — Brainstorm naming options for features and products you’re launching 4. Messaging — Refine your storytelling with a built-in copy editor 1. Whether it’s a keynote script, blog post, social copy, website or thought leadership content, AI is a great built-in copy editor 5. Creative — I haven’t tried this yet, but you could use AI to help generate your key visual assets 6. Localization — AI can do the heavy lifting for your translations (text and even audio) so you can quickly scale to many markets 2. Land your story 1. You can use AI to predict which customers to target 2. You can use AI to tailor your message to your target audience across your channels. There are many AI solutions for account based marketing and paid media more broadly. 3. If you roll new messaging or product activations out to the sales channel, you can use AI to track whether sales is adopting your messaging (down to the talk track and slides) and the impact on important business results like win rates. 3. Optimize 1. If your media team is using AI campaigns, it’s important to understand who you end up converting because your targeted audience may not convert as well as another one. I recall an example from my Google Ads days where a shelf stable food company had been targeting doomsday preppers without realizing that sailing and boating enthusiasts were an equally good (yet radically different!) audience for them. Imagine the shift in positioning and marketing based on that campaign insight. AI is able to uncover these unexpected pockets of demand for you. 2. Contextualize product adoption and NPS trends through customer insights gleaned by AI from various customer interactions. By knowing what your customers are saying and feeling, you can better guide product roadmap, positioning, messaging and go-to-market strategies.

We are using AI for our competitive intelligence program in two ways right now: (1) product intelligence for competitive offerings Rather than manually build and maintain feature lists for our competitors which then immediately get out of date, we have an internal answer bot that will scrap the web to answer whether a competitor has xyz feature. Any seller can submit a prompt to our answer bot which greatly reduces the burden on PMM fielding repetitive questions. (2) the tl;dr for real time competitive intelligence AI can not only keep you abreast of what’s happening, but also distill the noise into the key messages that matter for your world. AI tools today help aggregate and synthesize announcements for you to pass on to product and the field. While I find that you still need a human expert to analyze the potential impact of a given announcement to your business, AI can help do a lot of the upfront aggregation and distillation for you. 

This question really resonates with me because AI mentions are at an all time high; and therefore, the market is pretty noisy. In order to cut through, I focus on getting to a single headline message that is both pointed and taps into an emotional tension. How I do this is by focusing on the core principles of any good marketing (this by the way is the Google marketing mantra): * Know your user(s) * Know the product magic * Connect the two Let’s say you have a product that will save a bunch of time for your B2B target user. You could just use a generic “supercharge your team with AI product,” which would absolutely get lost in all the other generic messaging out there. Or you could dig into * Why does saving time matter for this audience? How does it change their day-to-day life? What’s the “from” / “to” for the change that this new product drives? * What is hard and/or important about the work your product now automates? * How does your user feel about this topic? * Is there an emotional tension that you can tap into - e.g., maybe they value saving time but fear being replaced by AI? * Again, why does this matter to your target user, really? * How is your offering different from the competition? Ok, but what if what you’ve built is being copied by others and therefore losing its novelty? Then I think it’s important to probe what your brand and point of view is. For example, distinguishing between all the different LLMs today can be hard especially with the tech constantly evolving. However Anthropic takes a very strong and steadfast position in the market around safety. As a result, their message around “Helpful, honest and harmless AI” cuts through. You cannot be memorable if you aren’t willing to put a stake in the ground on something. Finally my last piece of advice is that once you know what you want to say, distill it down to very human, simple terms.

I strongly believe that AI will be a powerful tool to make PMMs better at their job. The PMM role is highly interdisciplinary; you sit at the crossroads of product, sales, PR, and all the marketing channels. There is so much nuance, complexity, and human judgment to the job at hand that I see the scope of AI being very focused on helping with certain specific pieces of the job for many years to come.

Product strategy and product positioning. I find AI useful for honing your message or exploring branding and naming options, but before you can get to that point, you need to settle on the product positioning first. Developing product strategy and positioning requires deep understanding, exploration and finally, alignment with your product partners. AI cannot do that for you. 

Per my answers to the other questions here, there are loads of use cases for AI even if you’re not at an AI forward company. The most helpful AI will be tech that’s integrated into your workstreams and data so that’s probably your biggest barrier. But you can still benefit quite a bit from using “off the shelf” AI like ChatGPT to help you refine your messaging, research new segments, brainstorm naming options and more.

In the enterprise landscape, it’s not just the LLM models that matter. In fact, I’d argue the underpinning LLM models matter less than two other factors: (1) the product design and (2) the data that powers the AI. Business applications are more complex than consumer ones because a number of systems, processes, and people come together to achieve a certain outcome. Most businesses ensure efficiency through standardized workflows and repeatable processes for employees to follow. And so it’s within this context that AI needs to exist. As a result, it takes thoughtful product design to properly embed AI into a business's internal ecosystem. The actual data - both the quality and quantity - is the other factor to consider. As the saying goes: garbage in, garbage out. When I led Google Ads product marketing, I saw how a lack of data (both breadth and depth) could limit an advertiser's ROI when using Google AI campaigns like Performance Max. The best advertisers fueled the AI with lots of high quality data across targeting (first party signals), creative (uploaded tons of assets from text to video), bidding (shared customer lifetime values for value-based bidding). Now that I’m in the Revenue AI space, I see the same thing — sales teams are fundamentally stunted when they fuel AI with CRM data that’s collected from manual seller inputs, instead of automatically capturing all of the customer interaction data using AI directly. 

Categorically Yes! My current company builds AI for revenue teams. We have countless customer stories of AI driving an increase in win rates. And I’ve witnessed the impact first hand with our own sales reps. Power users of our AI generate significantly higher win rates than those who are not. In fact we’ve measured 40%+ more deal capacity and 25%+ higher quota attainment for these AI power users in our sales team.

[note this is the same answer to several overlapping questions] AI has a key role to play across many points in the launch journey, but you can break it down into 3 simple categories: 1. Develop your story 2. Land it 3. Optimize it Let’s take each piece at a time. 1. Develop your story 1. Know your market — AI can help synthesize key market insights across a wide range of dimensions: 1. Desk research on your target personas and segments 2. Summarize more lengthy secondary research for you (UX research, customer interviews and quant, if you are in the b2b space then analyst reports) 3. If sales is an important channel, you can use AI to summarize customer calls instead of having to ride-along or get second hand accounts 4. Competitive intelligence (see other question that I address on this point) 2. Customer advocacy — Source hero customer stories and testimonials 1. If your company uses a Revenue AI platform, you can mine conversational data to find winning customer stories and soundbytes rather than hoping a seller will serve it up on a silver platter for you. 3. Naming — Brainstorm naming options for features and products you’re launching 4. Messaging — Refine your storytelling with a built-in copy editor 1. Whether it’s a keynote script, blog post, social copy, website or thought leadership content, AI is a great built-in copy editor 5. Creative — I haven’t tried this yet, but you could use AI to help generate your key visual assets 6. Localization — AI can do the heavy lifting for your translations (text and even audio) so you can quickly scale to many markets 2. Land your story 1. You can use AI to predict which customers to target 2. You can use AI to tailor your message to your target audience across your channels. There are many AI solutions for account based marketing and paid media more broadly. 3. If you roll new messaging or product activations out to the sales channel, you can use AI to track whether sales is adopting your messaging (down to the talk track and slides) and the impact on important business results like win rates. 3. Optimize 1. If your media team is using AI campaigns, it’s important to understand who you end up converting because your targeted audience may not convert as well as another one. I recall an example from my Google Ads days where a shelf stable food company had been targeting doomsday preppers without realizing that sailing and boating enthusiasts were an equally good (yet radically different!) audience for them. Imagine the shift in positioning and marketing based on that campaign insight. AI is able to uncover these unexpected pockets of demand for you. 2. Contextualize product adoption and NPS trends through customer insights gleaned by AI from various customer interactions. By knowing what your customers are saying and feeling, you can better guide product roadmap, positioning, messaging and go-to-market strategies.