Giving live demos, talking to real people, strategizing with AEs to figure out the best way to turn that one naysayer stakeholder around – these are the fun parts of being an SE.

Logging activity in Salesforce, updating tasks in Notion or Google Sheets, trying to figure out what someone wants you to do from a 15-email-long chain – these are the not-so-fun parts of being an SE.

And they’re the parts of the job that Muskan Jindal, one of Navattic’s advisors, has set out to automate.

We sat down with her to find out how she’s used AI to lighten her workload, prep for discovery calls (we mean really prep), and focus her human judgment where it matters most.

As a former proposal consultant, Muskan has one of the more unique backgrounds among SEs, and it’s that background that catapulted her headfirst into the world of AI.

She realized that instead of CTRL+F-ing for keywords and trying to find the exact right phrase to drop into an RFP doc, she could just use AI to do a bulk of that work and get her eyes on it for a final check.

After she automated herself out of that job, she sought a new challenge and came on as an SE at a B2B SaaS company, and quickly realized she could carry much of what she’d learned into her new role.

“Before you join a startup, you don’t fully understand that these companies never have enough people, never enough processes, and so most people are DIY-ing a lot of stuff,” she points out.

During her first few months, Muskan paid attention to where people were losing time. She noticed SEs were doing things like:

• Poring over target account websites, trying to pick out nuggets to bring up in discovery
• Creating and updating fake financial models for demos
• Building the same kinds of collateral, just with slight tweaks for every deal
• Monitoring prospect activity to send follow-ups at exactly the right time

“I was trying to look specifically for things that were (1) being done manually and (2) were relatively easy to get into an AI workflow,” Muskan shares. “Instead of ‘low-hanging fruit,’ I call these ‘AI watermelons.’”

Tackling these “AI watermelons” would allow her and her team to work on more deals simultaneously, and make their job more enjoyable (aka stop repeating the same boring tasks over and over).

The first area Muskan focused on was discovery. She approached it in two ways:

1. Extracting critical data from RFPs

Muskan started with what she knew: RFPs can tell you a lot about what a prospect wants and needs.

“9 out of 10 RFPs are rigged. Companies will put keywords in there that they know only your competitor has on their website or use AI to come up with questions that don’t really make sense,” she emphasizes.

“What AI can do for you is pull out the most important themes to have top of mind in a disco call. What will separate your product from the rest in the prospect’s mind.”

For example, maybe the company:

Mentions a specific integration multiple times → Gives you time to think about how to frame building out that integration with your open API.

Signals that they’re trying to expand into a particular market or vertical → Gives you a chance to find your best case study to match.

Implicitly shares that they’ll need to handle more volume very soon → Gives you the heads-up to bring up scalability, reliability, and SLAs early.

AI can also surface features the company did not explicitly ask about in the RFP, but could be extremely helpful to bring up in a discovery call, given what else they’ve revealed.

“It can tell me, ‘Questions 4, 5, 23, and 54 indicate an opportunity to bring up feature xyz.’

Then, it’s on me to read those questions, figure out if the AI’s take is right, and then prepare some good questions for discovery.”

2. Sharing automated demos ahead of live calls

Mining RFPs isn’t just good for call prep, it’s a great way to build automated demos that can get you even more information about a prospect’s goals.

“I use what I’ve gleaned from the AI RFP research to build automated demos and tease the product a bit before the call,” Muskan highlights.

“Because, let’s be honest, you could attach collateral to an invite, but chances are no one will read it. Something they can click through in 30 seconds that matches exactly what they’re looking for is way more convincing.”

With a demo automation tool like Navattic, you can create multiple foundational demos that can be tailored to each prospect’s specific interests, industry, or vertical.

Once you’ve sent them or added them to a meeting invite, you can monitor:

• Whether a stakeholder viewed the demo
• How many steps they got through
• Who else they might’ve shared it with

If you want to get even fancier, you could set up Slack alerts when someone starts or completes that demo.

“By the time the call happens,” Muskan explains, “You have a read on who’s already sold and who’s not.”

Muskan is a bona fide AI native. “I no longer read through all the places I’ve been mentioned in Slack. I don’t read long email chains. I don’t draft prospect emails.”

Instead:

She uses Claude to track what prospects do and follow up

“I love Claude because you can give it Skills. You give it your logins, you explain how systems work and what you want it to do, tell it when to do something, and voila,” she says.

Now, whenever prospect activity happens in a POC portal or interactive demo, Claude looks at the data and fills out a templated email draft with relevant content to share.

Muskan then adds some human flair, “Maybe an emoji or a joke or some non-work context to make them not fall asleep,” and sends it off.

It saves her an hour or two per workday.

She uses Gemini to triage emails

Muskan’s team uses Gmail, which is connected to Gemini.

“Whenever I see a lot of people in an email chain, I don’t get overwhelmed, I just ask Gemini what’s needed from me, and reply from there.”

Rather than trying to sift through the minutiae, she gets the action item checked off her list.

She uses Rox to keep a pulse on the industry and her accounts

Muskan and her AE team use Rox, a revenue agent, to surface news-driven recommendations on who to reach out to when, and about what.

“For instance, it’ll tell us things like, ‘This senior banking person just posted an article relevant to what we sell,’ so we can see if it’s a good lead. Usually, it’s time to take someone out for dinner!”

Rox gives Muskan’s team a timely, specific reason to chat with leads without having to dig through 10-Ks or constantly be on LinkedIn.

For Muskan, adding her own POV or spin to any AI-driven workflow is a given.

“My personal experience is defined by the people I meet, the use cases I come across in the industry, the conferences I go to. That’s valuable content that AI does not have and needs to be injected back into the process,” she notes.

One big way that her judgment comes in handy is in negotiations. Muskan uses a tactic she learned from a senior SE to speed up the process – something she calls a “pre-contract contract.”

“If you come to an understanding with your champion that there are 4 things that need to happen to seal the deal, you say, ‘Can you guarantee that if I prove these 4 things, in an interactive demo, POC, or collateral, you’ll get me a signature?’”

That “pre-contract contract” puts a little pressure on your champion and also gives them the ammunition to go translate the value to the rest of their team.

“Now, this only works if the person is truly your champion,” she warns. “They really have to love the product, and want me and my AE to win. That’s something AI can’t tell me.”

This distinctly human approach recently got one of Muskan’s deals signed in two weeks, almost unheard of for a bank ICP.

When asked where discovery automation and AI for SEs is going, Muskan said, “The future is moving so fast, I am just trying to stick to what’s on my to-do list!”

But she did give us a hint as to what’s on it, ways to free SEs up to apply their cross-functional perspective to problems they have some insight into but don’t have the bandwidth or tools to take on.

One problem she’s eyeing is the gap between product vision and user adoption, and how pricing can bridge the two.

“The way you price your clients is a reflection of how you want them to interact with your platform,” Muskan says.

“Right now, a lot of teams take a reactive approach. Something changes on the platform, and then they scramble to figure out if they can charge more money for it. I think it should go the other way. Set the pricing model first, and let it pull more users in.”

That kind of modeling usually requires an analyst fluent in Excel, the kind of finance person most SEs, AEs, and product folks don’t have easy access to.

With AI, Muskan hopes to run pricing scenarios herself and turn the output into packaging and quoting guidance for AEs to use in the sales cycle.

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