As this discipline emerges — like every time new trends do — it’s normal to hear contrasting opinions and misconceptions. We’ve seen it 10 years ago with the growth hacker, and even with RevOps. Companies confuse GTM engineers with RevOps, growth PMs, and even CRM admins.

So, what is GTM Engineering? First of all, we feel more needs to be said about the origins of the term, how it came to be, and why now. With that in mind, we’ll have more context to define it. But while everyone is trying to give a positive definition of the term, we’ll do the opposite and unpack our anti-definitions instead — what GTM Engineering is not.

Then, in the next post, we’ll lay out a first principle thinking framework and primitives to help people get their heads around this emerging, multi-disciplinary position.

The origins of GTM Engineering

Let’s first trace back the DNA of GTM Engineering. While the first job titles were made popular in early 2024 by Clay, the role isn't as new as it seems.

Companies like Ramp, Gorgias, and Rippling — considered among the golden standard of growth teams — have been the pioneers of embedding engineering talent and skill sets within the go-to-market function.

Whether they were called growth engineers or growth ops, these roles shared the usage of automation, data, and APIs to scale pipeline and revenue without increasing the size of the go-to-market team.

Growth engineers were the technical counterpart to growth marketers — building technical solutions to solve GTM use cases in an efficient and scalable manner.

Need GTM efficiency? Meet our latest AI

But, where does this need for GTM efficiency suddenly come from? There are two key factors contributing to the rapid rise of GTM engineers last year.

First, When VCs pulled back their money in 2023, startups had to keep scaling revenue without expanding headcount — or actually firing half of their teams — making GTM efficiency the name of the game. In other words, they needed to:

• Maintain or speed up time to market, pipeline and revenue velocity

• While improving efficiency and costs metrics (eg. cost per opportunity, ROI, etc.)

• And keeping headcount constant (eg. revenue per employee)

However, “automation” still meant getting an engineer to work on building custom scrapers, ETLs and data warehouses connections — strategies that were not that accessible to most teams, especially given growth engineers were not just rare, but also expensive!

That’s where the promise of the last generation of LLMs comes in. New SaaS categories quickly emerged between 2023 and 2024 wrapping few critical ingredients of growth engineers into low-code SaaS and making them A LOT more accessible to non-technical users. Think like:

1. Custom waterfall enrichment to make prospecting and research dead easy

2. Workflows UI to line up multi-step, tailored GTM operations across the lifecycle

3. LLM capabilities and prompt engineering to scale research and personalization

4. Robust integration layer with built-in APIs

The result has been a fervent and colorful GTM tech industry popping up in the last 24 months — with plenty of leading players like Clay, Cargo, Lemlist, LaGrowthMachine, Common Room, and AirOps, blending or specializing in the above capabilities.

Suddenly, you could do the advanced work of a growth engineer with just a few lines of code — and you could actually code the rest by just prompting ChatGPT.

And in late 2024, the concept of “founder mode” popularized by Brian Cesky compounded this efficiency mantra of “doing more with less” into the industry. This — coupled with the ever-present promise of AI — made the idea of small, lean, and technically-enabled orchestrators too compelling to ignore.

With the entire industry on founder mode — and the right AI tools for the job — there were no more excuses. A new storm was coming.

Service-as-Software and the wave of Clayagencies

In this new hyper-competitive arena, everyone needed to keep scaling GTM operations efficiently but no one could afford in-house growth engineering that would do custom “integration or automation work”.

That’s why GTM Engineering started to become popular; because it’s a role that blends capabilities and knowledge across sales, marketing, customer success, and engineering in one person.

A cross-functional GTM skillset alone yields massive efficiency gains — because you have one person doing the job of three — and when enabled by the right AI tools and workflows, its promise scales 10X.

With that in mind, it became clear that a growth engineer wasn’t a must-have to build complicated workflows in your GTM motion. But a hands-on, semi-technical GTM operator with Clay and an email sequencer could do it. And as more non-technical teams got exposed, the promise of GTM Engineering grew, and the overall movement accelerated.

However, it’s still very rare to find this type of talent; and even when you do, it can be expensive and risky, especially as a full-time hire.

That’s why we believe one of the first cohorts to really help grow awareness of the role were the so-called Claygencies. These were outbound agencies (and consultants) that embraced the GTM Engineering principles on various fronts. These operators consistently:

• Use AI-powered GTM tech to scale beyond $1M ARR with extremely lean teams, really embodying the core premise of GTM Engineering.

• Provide expert GTM services in the form of process workflows, and have strong incentives to productize these workflows with software and AI.

• Use AI and their toolstack as a core differentiator from ‘traditional agencies’, and to showcase their differentiators via the creative workflows they build.

• Offer a more specialized and lower-risk alternative to full-time hires, who are harder to find and more costly to onboard.

And that’s why we’ve seen Clayagencies take over LinkedIn with tool- and workflow-related content on LinkedIn. All the tools, plays, and workflows were all over Linkedin for grab — an insane awareness machine for the entire GTM tech industry and players alike.

Emerging vendors seized this moment to carve their spot in the industry and spin up their agency programs, each for their own ‘niche’ — RB2B for website deanonymization, AirOps for content and SEO, and so on and so forth.

And a new category of agencies and consultants was born — service-as-a-software. A new business model — with AI and GTM Engineering capabilities at its core — that would answer both calls — GTM efficiency and ‘founder mode’.

Stack vs. Expertise — what matters most?

At this point, the underlying question emerged:

“What matters most to drive GTM efficiency? The tool stack or the GTM Engineering execution expertise?”

The Clayagencies buzz initially took off largely due to capabilities that these new tools democratized — waterfall enrichment, AI workflows, and the likes. If you step back, the underlying tech primitives haven’t changed — we had Clearbit for enrichment, workflow editors like Zapier, and ETLs like Segment even before 2020.

The real spark came from making these capabilities more accessible to semi-technical users, and layering the LLM component and prompt engineering on top — which shortened the learning curve and opened the door for more niche, specialized platforms.

Look at any feed and you’ll see a flurry of tactics and workflows touting a slick, new specialized data provider or email sequencer — often packaged as plug-and-play workflows you can easily copy and paste.

“Just comment ‘workflow’ and you’ll scale 1000 meetings in 1 day.” We’ve all been there!

Yes, it’s hard to resist the allure of a “silver bullet,” and yes, these new workflow tools really make it super easy to achieve results. However, now that the cat is out of the bag and these tools are known and accessible, we believe that the elephant in the room comes down to one thing — execution.

So, to answer the question, we believe expertise matters A LOT more than the underlying stack.

Which brings us to the next topic.

What GTM Engineering ISN’T all about

Armed with this additional context, let’s move onto our anti-definition(s) of GTM Engineering.

1) GTM engineers don’t define the entire go-to-market

First and foremost, we believe GTM engineers scale an existing strategy instead of inventing one. GTM engineers don’t select markets, craft messaging, or determine positioning — those decisions come from leadership, marketing, and product. Instead, GTM engineers enable these teams to streamline scattered processes into scalable workflows.

And while GTM engineers don't set strategy, their technical expertise can help uncover new, creative, and performant ways to achieve an outcome. For example:

• The Marketing Director doubles down on LinkedIn Conversation Ads, targeting Salesforce users, who drive higher retention, engagement, and ACV.

• Instead of relying solely on the TAM list and CRM contacts, GTM Engineering proposes to expand the audience by pulling recent companies using Salesforce as their CRM.

• The Marketing Manager runs a focused Salesforce campaign, while the broader team targets US-based SaaS companies (50-500 employees).

• The GTM Engineer then helps build an automated system to enrich incoming LinkedIn leads with technographic data, analyze intent signals, and use an LLM-driven scoring model to prioritize leads.

• For Salesforce campaign users, reps receive key insights enriched with real-time news, promotions, new hires, and job postings. An LLM like Claude 3.7 Sonet summarizes the data, making it ready for outreach.

• For high-intent leads, the system automatically triggers personalized outreach emails from the AE’s account, tailored to the prospect’s tech stack and company news. AEs get timely notifications for follow-ups, turning hours of manual work into a streamlined, data-driven process, while still supporting the original marketing strategy.

Marketing still owns the ad strategy and Sales the engagement process, but the GTM engineer's technical capabilities opened up new, scalable ways to achieve the same outcome.

2) GTM Engineering are not Growth PMs or Growth Engineers

We’ve made this distinction before, but we want to make this even clearer.

Growth PMs and engineers emerged to drive activation, retention, engagement, and monetization metrics through experimentation. While there are overlaps with GTM Engineering, there are two crucial differences:

• Technical execution: While growth PMs coordinate with growth engineering teams, GTM engineers directly build and implement technical solutions.

• Scope of impact: Growth PMs and engineers typically own specific product metrics like activation, free-trial to demo conversion, or sign up page conversion improvements, while GTM engineers influence and contribute to pipeline and revenue metrics across the entire customer lifecycle.

And whether there’s a lot more overlap between Growth engineers and GTM engineers, let’s just say that:

• Growth engineer is more involved in the dev work based out of experiment hypotheses — and work with PMs and designers to pull it off — less so in the business understanding.

• GTM engineers are more involved in the business, work primarily with the business teams, and are proactively influencing those decisions.

3) GTM Engineering are not RevOps

Out of all the mixed-up roles, RevOps is probably the most often mistaken for a GTM engineer, probably because RevOps and GTM Engineers work closely together. However, they serve fundamentally different purposes. The key difference is that:

• RevOps focuses on the foundational strategy and pipelines — like setting up the CRM instance, decide which fields to report, the pipeline stages, foundational journeys, and reporting layer across marketing, sales, and success.

• GTM Engineering scales the efficiency of these foundations via tailored, automated and technical solutions.

So while RevOps helps decide where to allocate budgets, GTM engineers focus on maximizing that ROI through technical innovation. And when GTM engineers identify successful experiments, they work with RevOps to roll them out to the entire organization.

For example:

• RevOps might suggest increasing ad spend on Google because they mined as an insight from their attribution model.

• GTM Engineers work with RevOps and Demand Gen to implement server-side tracking enriched with firmographic data and feed that data back to Google to improve Performance Max campaign results, therefore, achieving higher conversion rates and more pipeline.

Let’s see some more aspects where these two compliment each other:

If nothing else, GTM engineers are probably the wingman RevOps has been searching for.

4) GTM Engineering isn’t just about new business and outbound

The common misconception is that GTM Engineering primarily serves top-of-funnel activities — building outbound automation, enriching lead data, or creating target account lists. And while a lot of GTM engineers and agencies made the movement famous with list building and personalized outbound, reducing them to these tasks misses their potential.

Think of a GTM Engineer building an intelligent early warning system for customer success.

• Rather than just tracking basic usage metrics, this system combines multiple signals: product engagement patterns, job changes across multiple stakeholders — like when champions leave or new executives join — sentiment indicators like NPS scores, support ticket tone; and renewal context.

• When the system spots potential churn patterns — such as a champion departing followed by declining feature usage — it automatically triggers the right messages to the right stakeholders on the right channels.

• This proactive approach helps teams prevent churn before the obvious signs appear, turning potential risks into retention opportunities.

GTM engineers don't just support existing processes — they reimagine what's possible through the lenses of the new AI tooling available today.

Good GTM engineers ask: "How do we automate this manual process?" But the best GTM engineers ask: "How can we use AI and tech to give our GTM teams superhuman capabilities?"

Again, consider these other use cases way beyond just list building and outbound:

• Automating win/loss case analysis from customer calls to refine ICP, messaging, and trickle those updates into multi-channel campaigns.

• Programmatically score and route prospects into tailored nurturing journeys with AI without SDRs doing manually allocation and CRM hygiene.

• Create an automated workflow to turn customer calls insights into long-form content that can be repurposed across blog, LinkedIn organic, and retargeting ads.

These use cases show the potential for scaling GTM efficiency across teams, and stresses how GTM engineers build the technical bridge between strategy and efficient execution — and not just for generating new business, but also expansion!

Most importantly, this approach creates a compounding, ripple effect across the organization:

• Sales teams ask "Could we build something to predict which deals need attention?"

• Marketers think "How can we automatically personalize campaigns based on intent data?"

• CSMs wonder "What if we could predict churn before it happens?"

It's this cultural transformation that empowers every GTM team to think creatively and consider technically-enabled capabilities to achieve specific outcomes that makes GTM Engineering truly powerful.

5) GTM Engineering isn’t just about technical execution

While most of today's LinkedIn discourse suggests GTM Engineering success comes from simply connecting tools and writing code, once again, this view dangerously oversimplifies the role.

GTM Engineers are, yes, technical strategists who build integrated systems across the entire revenue organization, but they’re not like “developers taking requirements” from business teams. They translate business context into technical architecture while orchestrating solutions across teams — making the role both challenging and critical for scale.

Technical skills are crucial, but they're just one piece of the puzzle. The reality? GTM Engineering requires three-dimensional thinking.

1. Business understanding. Instead of just automating a process because "we can," top GTM engineers go deeper, and probe things like:

   • Why does this matter to revenue?

   • How does this scale with our existing GTM motion? (eg. given our PLG or SLG)

   • What are the short- and long-term impact of this on adjacent teams?

2. Systems thinking. Rather than building isolated solutions, they consider:

   • How do marketing, sales, and product workflows intersect?

   • How does this affect the customer journey, experience, and buying process?

   • Where are the data inputs, outputs, and handoff points?

   • What breaks if we scale 10x?

3. Technical excellence. Beyond just making things work, GTM Engineers build for resilience:

   • How can we intentionally vet, test, and integrate new GTM tools?

   • How do we plan for gracefully retiring them if they no longer fit?

   • What happens when data quality from different vendors degrades?

   • What metrics or logs can detect performance issues, data synchronization failures, or API outages in real time—and who gets alerted

So, what does GTM Engineering mean then?

Taking all into consideration, here’s our best definition of a GTM Engineering.

GTM Engineering blends technical expertise — waterfall enrichment, AI workflows, prompt engineering, and custom APIs connections — with system thinking and business understanding across the entire funnel. Based on this skillset, GTM Engineers build systems that open up new, creative ways of scaling business outcomes and GTM efficiency without the scale headcount proportionately.

This definition highlights how GTM Engineering isn't a one-size-fits-all toolkit you can simply plug in and play — it requires careful customization to your organization's GTM motion, product complexity, and team setup.

If you look at Unify and Common Room, you’ll see playbooks are already becoming productized and will soon be commoditized. The real value comes from using these unique skills to unlock creative ways that give your company unique advantages in how you engage and grow your market.

In the next post of this series, we’ll dig deeper into the lifecycle of GTM Engineering, and reveal our framework to execute on these problems.

In the race for scalable organic growth, many B2B SaaS companies find themselves caught in a paradox. Everyone craves the long-term benefits of organic growth, yet few are willing to invest the necessary time and resources upfront. This shortsightedness has led to a troubling trend — the misuse of AI to automate outbound marketing and content creation, resulting in a deluge of subpar messaging flooding the internet.

Consider the all-too-common scenario of a founder lamenting, "We're burning $100k monthly on ads with moderate success, but can't justify $10k for content because it's 'too slow'."

This mindset often stems from past disappointments with overpriced content agencies delivering mediocre results. However, this approach misses a crucial point: to win the organic game, companies must shift from renting audiences to earning them.

The situation is further complicated by the first wave of AI tools, particularly chat-based systems. While they promise to automate and scale organic growth, the reality is often disappointing. Despite a significant productivity boost — estimated at 2-3x per individual contributor — these tools are frequently misused to amplify noise rather than signal. Even more advanced vertical AI content writing platforms, while gaining popularity, often fall short of delivering truly effective, tailored content that resonates with specific audiences and aligns with unique business goals.

The bottom line? Growth and marketing teams are already leveraging AI, but often inefficiently. Many find themselves investing the same, if not more, time in programming and connecting these tools, without achieving the top-tier results they seek. To truly harness the power of AI for organic growth, a more strategic, quality-focused approach is essential.

Why AI workflows actually deliver on the AI promise

AI workflows are ushering in a new era for B2B SaaS go-to-market strategies, representing the third generation of AI and a revolutionary approach to scaling growth. 

These workflows deliver on the AI promise by empowering small teams to amplify their productivity 100-fold, enabling the rapid deployment of complex organic strategies and providing a significant edge in time-to-market and market share expansion. 

The impact is astounding: our experience shows that AI workflows can slash content creation costs from $1-2 per word to as low as $0.10 - an impressive 20X reduction! 

This post delves into how integrating AI into GTM strategies can supercharge growth and efficiency for modern startup teams. We're not just offering a step-by-step guide on implementing AI workflows for B2B SaaS GTM across content and outbound channels; we're providing a strategic framework to identify and scale growth opportunities throughout your business. Our approach leverages AI for research, content generation, and workflow automation, dramatically boosting efficiency and output. 

At HyperGrowth Partners, we're not just giving you the fish, we're giving away our fishing rod, equipping you with the tools to transform your GTM strategy in the AI era.

Benefits of custom AI workflows

AI workflows are revolutionizing B2B SaaS marketing, offering a powerful solution that delivers on the AI promise of scalability and cost savings while maintaining exceptional quality. 

However, it's crucial to note that human oversight remains essential in setup and review processes - this human touch is your competitive advantage, preventing your brand from becoming a "fast-food" content factory in the eyes of both customers and search engines. AI workflows outperform vertical AI apps in several key ways:

• Multi-level customization: Custom AI workflows allow tailoring content to specific needs and use cases, incorporating brand voice, audience preferences, specific constraints, and context.

• Multi-step processing: Breaking down content creation into multiple steps ensures comprehensive and polished output, increasing the quality bar even with complex tasks.

• Multi-model integration: Using different AI models for different tasks like ideation, research, and content generation across text, images, video, and audio — helps leverage their unique strengths while optimizing overall token costs.

• Model training integration: Incorporating industry-specific resources like whitepapers, technical documentation, niche books, and more; ensures content aligns with your brand's expertise, even when there’s no evergreen content to scrape on the Internet.

• Measurement and improvement: Custom AI workflows allow for a much more objective measurement of productivity gains and impact, identify bottlenecks to improve, as well as for refinements for doubling down on scale.

When implementing AI workflows, don’t try to go from zero to 100% AI automated. Instead, adopt a crawl-walk-run approach. Ask yourself:

• What existing tasks could an AI workflow make 10x easier/faster?

• How would complete abstraction from the work look?

• With unlimited time, how would you approach certain tasks?

This strategic implementation ensures you harness the full potential of AI workflows while maintaining your unique brand voice and expertise.

Deepgram’s Story: AI + PLG Flywheel = 24X Traffic Growth in 2 Months!

At Deepgram, Marcel ingeniously combined AI workflows with a Product-Led Growth (PLG) flywheel to create a compounding growth engine, where each component gains momentum to drive efficiency. This innovative approach allowed his team to build entire programs that form the core of this flywheel:

• AI Apps catalog: A comprehensive directory generating significant monthly views, benefiting startups, and attracting newsletter subscribers

• AI Minds newsletter: Engages a large subscriber base, promoting startups and encouraging new signups

• Startup program: Offers free credits and community access, featuring members in newsletters and AI catalogs to fuel growth

• Affiliate program: Provides referral incentives, driving signups from influencers and program members

By leveraging AI workflows, Marcel's team was able to efficiently create and manage these interconnected programs, each feeding into the next to create a powerful, self-sustaining growth ecosystem. This approach not only maximized resource efficiency but also ensured a cohesive, multi-faceted strategy that continually attracts, engages, and converts users in the B2B SaaS space.

Strategic questions to guide AI workflow implementation

When implementing AI workflows for B2B SaaS content marketing, it's crucial to focus on tasks that are time-consuming, repetitive, or require precision. To guide your AI implementation strategy, ask yourself:

• Which tasks consume the most time and could be automated?

• What repetitive tasks are ideal for AI assistance?

• Where are the bottlenecks in current processes that AI could streamline?

• What data-rich sources can AI leverage for insights?

• How are competitors using AI, and where can we differentiate?

• Where can AI provide quick wins with immediate benefits?

Start your AI workflow journey by:

• Using AI for research on your company and competitors

• Conducting in-depth audience analysis with tools like Gong and Humata

• Leveraging keyword research and competitor analysis tools (e.g., Ahrefs, SEMRush) to form hypotheses

• Beginning small and iteratively building AI capabilities

However, be mindful that not everything should be automated. For instance, integrating with a legacy CMS might be more costly and time-consuming than maintaining a human-in-the-loop process. Always weigh the potential benefits against the implementation costs to ensure your AI strategy truly enhances your content creation process.

Key components of an AI workflow

To implement effective AI workflows in B2B SaaS content marketing, three key components are essential:

1. Command Center (Airtable)

This is your coordination hub, offering:

• Database-like structure with flexibility for various use cases

• Robust ecosystem of extensions, integrations, and automations

• Visual interface for easy collaboration

• Cost-effective scaling based on user seats

2. Workflow automation (AirOps) 

Think of this as Zapier with embedded programmable AI, featuring integrations with CMS, docs, Gmail, and Slack for critical notifications.

3. Integration automation (Make vs. Zapier)

Triggers your AirOps workflow when input lands in Airtable.

4. Modular CMS (Webflow or Strapi)

Additionally, a cutting-edge, modular CMS like Webflow or Strapi is crucial to avoid integration bottlenecks. 

5. Human in the loop

The final piece is the human-in-the-loop strategy, involving a senior IC with content marketing and writing skills who can:

• Run daily AI workflows and SEO/website audits

• Conduct keyword research

• Manage content updates and backlink outreach

• Stage content in the CMS and QA AI-generated content

This comprehensive approach ensures a scalable, efficient content program that leverages AI while maintaining human oversight for quality and strategy.

Use case — AI workflow for content creation, SEO, and organic Traffic

AI tools have revolutionized the way B2B SaaS marketers drive traffic and create engaging content. Programmatic SEO, a powerful strategy that combines AI efficiency with human insights, offers a scalable approach to optimized content creation. This process can be broken down into two key phases:

Phase 1: Insight + Validation

• Question: Can AI write better content programmatically?

• Answer: Yes, when fed the right input, AI can generate superior answers

  

Phase 2: Content + Page building

The focus here is to identify the necessary elements for programmatic page creation at scale.

By leveraging this approach, marketers can efficiently produce high-quality, targeted content that resonates with their audience. The key lies in properly structuring AI inputs and validating outputs, ensuring that the content not only meets SEO requirements but also provides genuine value to readers. This strategy allows for rapid scaling of content production while maintaining quality, giving B2B SaaS companies a significant edge in their content marketing efforts.

Use case — Create an app catalog or database

Creating an app catalog using AI workflows can significantly streamline your B2B SaaS content marketing efforts. Here's a step-by-step process to efficiently build a comprehensive app catalog:

1. Seed list generation: Export a list of websites from Ahrefs or SEMRush.

   

    

2. Import your seed list into Airtable as your command center.

   

3. Categorization: Employ an LLM to categorize the list within your command center.

4. Copy asset creation: Utilize AirOps/Claude prompt to generate:

• Meta titles and descriptions

• Taglines

• Articles’ body

5. Image asset creation

• Cover images: Pull using Brand Fetch

• Logos/icons for thumbnails: Create using thum.io or abyssale

6. Quality control: Implement human-in-the-loop QA tasks to check for:

• Missing cover images, logos, or colors

• Quality flags

• Final QA

7. CMS Publication: Use AirOps to publish the finalized content

This AI-driven approach allows for rapid, scalable creation of high-quality app catalogs, ensuring consistency and efficiency throughout the process while maintaining human oversight for quality assurance.

Use case — Generate SEO articles

Revolutionize your SEO article generation process with this AI-powered workflow:

1. Target keyword identification: Leverage AirOps to gather comprehensive data from:

• Live Google SERP results

• Competitor H1-H4 analysis

• "People Also Ask" and "Related Questions" sections

2. AI-driven copy creation: Utilize AirOps/Claude to generate:

• Meta titles and descriptions

• Article introductions, titles, and outlines

• Full article bodies

3. Enhanced relevance: Use AI to fetch related posts and choose internal links

4. Visual appeal: Employ AirOps/Midjourney to create featured and OG images

5. Quality assurance: Implement automated fact-checking with Perplexity and plagiarism detection with Grammarly

6. Human touch: Incorporate a human-in-the-loop for final QA, including:

• Checking for missing visuals

• Verifying links and cross-links

• Conducting a final quality review

7. Seamless publishing: Use AirOps to push content directly to your CMS

This streamlined process combines AI efficiency with human oversight, ensuring high-quality, SEO-optimized articles at scale.

Use case — Automate content strategy and roadmap

Revolutionize your content strategy with this AI-powered workflow:

1. Identify competitor keyword gaps using Ahrefs data

2. Filter out brand and negative keywords with AirOps/Claude

3. Cluster keywords intelligently using AI prompts

4. Prioritize topics based on search volume, ranking difficulty, purchase intent, and topical authority

5. Implement human-in-the-loop quality control for keyword strategy and final QA

6. Funnel prioritized topics into diverse content types:

• Editorial articles

• Listicles

• Directory pages

• Glossary pages

This approach leads to increased traffic, conversion rates, backlinks, and brand awareness. Expand your AI-driven content strategy to include:

• Directory pages and knowledge-base articles

• Information-rich catalogs and databases

• Content repurposing across podcasts, videos, and social media

• Competitor comparisons and integration landing pages

• On-page technical SEO fixes

By leveraging AI throughout your content lifecycle, from keyword research to distribution, you'll create a scalable, data-driven content machine that consistently delivers value to your audience while boosting your SEO performance.

Use case — Turn Signals into opportunities, and scale pipeline

AI is revolutionizing the way B2B SaaS companies operationalize metrics and user behaviors to generate leads and opportunities. To harness this power, follow these key steps:

1. Define your sales cycle thoroughly

1. Establish your Ideal Customer Profile (ICP) and lead-scoring model

2. Utilize tools like CommonRoom or Koala to capture intent signals

3. Prioritize accounts based on ICP and intent scores

4. Send personalized emails triggered by specific actions (e.g., demo bookings, pricing page views, lead magnet downloads)

5. Automate sales team alerts via Slack and CRM

6. Automate sales team alerts via Slack and CRM

   

This AI-driven approach extends beyond lead generation, offering transformative applications across demand generation:

• Enhancing sales outreach and enablement

• Scaling Account-Based Marketing (ABM) and personalization

• Optimizing paid social and SEM ad-to-landing page experiences

• Enriching e-commerce product pages

• Personalizing lifecycle marketing and churn discount strategies

By leveraging AI in these areas, B2B SaaS marketers can dramatically improve efficiency, personalization, and overall campaign effectiveness, driving growth and customer retention like never before.

Conclusion

In today's rapidly evolving B2B SaaS landscape, integrating AI into workflows is no longer optional—it's essential for modern go-to-market (GTM) strategies. By creating self-reinforcing cycles around products, companies can ensure sustained growth and market dominance. 

The key lies in rethinking AI not as a standalone tool, but as a series of workflows that operationalize important signals, building competitive muscle in terms of revenue per employee, productivity, time to market, and scale. 

These AI workflows offer a transformative approach to GTM strategies, enabling B2B SaaS founders and marketing teams to effectively scale growth and revenue across every organic and paid channel. While this journey requires careful planning and incremental implementation, the benefits are undeniable:

• Significantly increased speed and efficiency

• Enhanced market impact and penetration

• Improved ability to adapt to market changes

By embracing AI workflows, B2B SaaS companies can position themselves at the forefront of their industries, driving innovation and growth in ways previously unimaginable.

Guillaume Cabane on AI-Assisted Outbound & Targeting

One of the most impactful areas where AI is making its mark is in outbound and targeting. The tooling for these use cases has evolved significantly, and it’s becoming all the rage on our LinkedIn feeds!

1. Automate lead list building and scoring with LLM-assisted intent signals

SaaS such as Waterfall, Clay, and Common Room, are rapidly emerging to streamline the aggregation, cleansing, and enrichment of data before it's used in outbound campaigns. These tools leverage Large Language Models (LLMs) to scrape data from multiple sources and enable the use of AI with custom datasets to inform critical strategic aspects like TAM definition and lead scoring.

For example, with Clay, you can scrape LinkedIn or Google Maps to define your TAM, according to targeting criteria like Industry, company size, type, and location. Clay will give you a potential TAM figure straight off the bat, helping you define and prioritize your go-to-market strategy.

On the other hand, Waterfall helps you enrich your lead lists from multiple data providers at once without you needing to sign up for long-term, individual contracts to each data provider like Apollo, Lusha, or Zoominfo, allowing you to reduce your CPL and overall CAC.

These tools are becoming more and more critical to engage the right customers and improve conversion rates, especially in the current market landscape where capital efficiency is key.

2. Scale personalized outreach and growth ops with AI

Clay — together with Cargo and Unify — play also a crucial role in lead scoring, routing, and outreach personalization your outreach with the help of the data gathered from across multiple sources.

Clay, for instance, is invaluable for — including LinkedIn, Google Maps, and CRM systems, or by adding additional data vendors. By using an “Excel-on-steroids”-like UI, GTM teams can easily enrich these lists with additional data points such as LinkedIn engagements, Technographics, or competitors' actions.

Once you’ve built these highly relevant and segmented customer lists, Clay helps you use ChatGPT to create personalized copy snippets that you can add directly to your email template in Unify, Smartleads, Instantly, Apollo, or AmpleMarket, and drive reply and demo conversion rates.

Cargo helps GTM teams automate pipeline operations like adding custom Salesforce fields, pushing to CRM stages, or scoring leads with signals from across various sources. Whereas this might not be relevant for early-stage growth ops, this automation is particularly beneficial for scaling from 1 to 10 in growth and late-stage orgs (eg. Series B-E), where data collection on Ideal Customer Profiles (ICP), lead scoring, pipeline, and intent signals is readily available.

Players like Unify also show how players are increasingly verticalizing, closing the gap between accessing prospecting contacts, enriching them with signals, orchestrating them, and activating them by sending personalized emails at scale.

3. Increase sales efficiency

Switching gears from outbound and growth ops to sales, that’s when the role of AI in enhancing sales efficiency — namely the SDR team’s ability to book demos — cannot be overstated. 

At Ramp, SDRs are increasingly using AI tools like ChatGPT to craft personalized email drafts by identifying common traits and patterns among prospects, such as shared educational backgrounds or industry experience. This personalized approach has led to a 3x increase in response rates compared to fully automated outbound strategies, which adds credit to the fact that AI-powered teams are still better than standalone AI. 

Another example of AI empowering human performance lies in audio-based solutions like Orum. Orum is revolutionizing salesfloors that reach customers via phone, by rethinking the dialing process from the ground up. Teams using Orum can use AI to boost sales efficiency up to 6x, scaling the number of daily customer conversations. With Orum, your team could:

• Automate the dialing process, by calling your entire CRM without teams having to touch a dialpad.

• Detect voicemails vs. human voices, increasing call coverage and SDR efficiency dramatically.

• Productize objection handling from calls, feeding back into CRM and pipeline generation, and product roadmap, ultimately distributing more insights across the organization.

• Integrate Orum with CRM and sales intelligence tools like Gong also helps make strategic decisions around rep performance visibility, coaching, and hiring.

• Save many hours on logistics that can be allocated to prospecting, customizing emails, and collaborating with Marketing and Product to refine the GTM.

On another note, what doesn’t work yet is fully AI-based SDR voice agents like air.ai — we’ve tried it, and the AI is not there yet.

Rex Gelb on AI-Assisted Paid Marketing

Paid marketing is another domain where AI is driving significant advancements.

1. AI targeting and bid optimization is finally thriving

For example, Google Ads Performance Max utilizes LLMs to optimize bids across channels like Search, YouTube, and Display, enhancing spending efficiency and targeting precision. The evolution of LLM-driven bidding optimization — which now ingests billions of data points every minute — is a testament to the advancements in AI technology, which now allow for more sophisticated and effective campaign management than was possible with earlier iterations of the tool.

Before LLMs, teams used to segment ad campaigns by various customer attributes like gender, interests, geolocation, product, etc. With LLM, targeting is getting so much better at showing the right creative to the right customer segment that you’re better off setting the budget at a campaign level using features like Advantage+ on Meta instead of manual ad-set-level targeting.

Many could ask — what’s the minimum conversion data needed to leverage these AI capabilities effectively? According to Rex:

• For Meta, the best practice is around 50 optimization events within a 7-day period. 

• For Google, you should have a minimum of 15 conversions over a 30 day period. 

If your budget can support generating such conversion volume, then you’re all set. Teams should approach minimum conversion volumes as a curve rather than a step function; the more data the better. 

Furthermore, the incorporation of intent signals into broad match queries by LLMs has increased relevance and conversion rates by up to 40% in comparison to traditional exact or phrase match types. For example, if you hire a celebrity called “Naomi Campbell” to promote your product, your product will never show up for board match queries like “Naomi Campbell”. However, the latest LLM improvements to broad match terms will scout for relevant intent signals and show your product ad with Naomi Campbell when those signals apply.

To Rex’s surprise, the AI promise made by these tools was not delivered just 6 months ago, while it is very much exceeding expectations now. This shows how fast the AI space moves, and how much incremental improvements compound. That’s why teams experimenting with AI tools must incorporate frequent re-testing to re-validate their potential. Ultimately, teams that won’t are the ones who’ll miss out!

2. AI Chat Ads are Coming

Innovations in ad placement continue to evolve, with Bing testing new AI chat placements that allow advertisers to show ads in Bing Chat results. As AI chat is eating into traditional search behavior at a fast rate, ad platforms are racing to roll out these new experiences.

As advertisers, the competitive advantage is on a first come first served basis, but it gives you the ability to streamline the discovery and comparison process greatly, simplifying complex purchases like B2B SaaS into a more natural and linear process that is done into one, chat UI. These interfaces drive accessibility and potential for PLG motions, where the buyer can self-educate and self-select the best software to buy based on their situation.

3. AI-powered creative creation and optimization

Additionally, both Meta and Google Ads enhance creative outputs with features such as automatic video creation, cropping, and cross-placement optimizations. This can be super useful for small and/or busy teams that don't have or have limited creative resources in-house.

“Major AI innovations in performance are happening at an ad network level, because these platforms own user attention, the data sets that inform LLMs, and the funding to significantly improve the machine learning process. By fall 2024, AI-powered advertising will be wild.”

Closing Thoughts

In conclusion, integrating AI into growth strategies not only enhances operational efficiencies but also fosters innovation and competitive advantage. 

By understanding and applying these advanced AI use cases, teams can effectively harness the power of AI to scale growth and reduce overheads, ensuring they remain competitive in an increasingly AI-powered market.

Once just an abstract concept, electricity soon lit up our cities, extending our time, powering our machines, and revolutionizing how we live and work. This transformation didn't happen overnight; it took visionaries like Franklin, Volta, and Faraday who saw beyond the immediate thought experiment to its vast potential across industries. 

As we stand on the brink of the generative AI era, we're at a similar crossroads. Just as electricity found its way into every corner of our lives, AI will permeate every industry, offering myriads of applications that are already redefining our world before our eyes.

Founders and CEOs are daringly exploring how AI can not just automate but reinvent their operations, with startups emerging at every layer of the stack — from silicon to compute and cloud providers, foundational models, infrastructure, LLM Ops, and horizontal and vertical application platforms.

The AI rally shows in the investment landscape, with $21 billion funneled into 400+ Gen AI deals in 2023. A staggering 75%+ of these ventures are early-stage or bootstrapped; another signal of the vibrant AI ecosystem. This momentum is expected to not only continue but accelerate into 2024, as both established tech giants and startups vie to capitalize on Gen AI's vast potential.

The Hyper-Competitive AI Market Calls for Differentiation

This rapid emergence of companies and products across the Gen AI stack presents a significant challenge to emerge in a hyper-crowded and hyper-fast-moving market. The urgency to differentiate and communicate value becomes more paramount than ever. To put additional pressure on founders, the timeline to achieve product-market fit and demonstrate customer traction has significantly compressed, forcing companies to prove their role and secure a foothold in the landscape, as waiting for full product functionality is a luxury no one can afford

In the last few weeks, several LLMs have launched both open- and closed-source from existing or new players, each outperforming others. Similarly, vector databases emerged as a new category last year and now there are several new entrants and existing database players entering the market making it even more competitive.

As the stack continues to evolve rapidly, the market gets more crowded, and as you move up the stack you start facing both new entrants building those capabilities and big tech incumbents expanding across the stack.

These challenges make it critical to be a top-of-mind brand in a market that changes daily. Unless you are a top-three in your category and are growing your mindshare with your audience, you won’t survive long. With the pressure to demonstrate unique value before reaching PMF, and continue to scale revenue after initial traction — or a new funding round — the question arises. 

How can early-stage AI startups emerge amidst the noise and become category-defining leaders? What strategies should they invest in to conquer their market share, ensuring they drive recognition and adoption amidst a sea of competitors?

It's Time for a Paradigm Shift

Traditional B2B SaaS products follow a sequential path: defining features, building the product, testing with users, and refining based on feedback. Marketing often plays a catch-up role, promoting a finished product once it has found early traction, and there’s already a sales team craving for leads.

This just won't fly with Generative AI, and here's why; the entire landscape — made of the product, the technology, the target market, and user problems — is in constant flux. 

Every week brings breakthrough capabilities and new applications that reshape what’s been on the market until yesterday. Products become obsolete in a matter of weeks, or if you are lucky, a few months. 

In this dynamic landscape, the role of marketing must fundamentally shift. 

• Marketers can no longer wait for the finished product — they must be at the forefront, articulating the company's bold vision for the future, positioning the organization as a category leader, and educating the market on the transformative potential of this technology.

• It's also about crafting a crisp narrative, establishing differentiation, and building the brand of the people behind this vision and the company driving it. In the generative AI space, marketing isn't just about what's on the truck today, but about defining the curve, convincing the market why your company is best positioned to lead the way, and guiding the product strategy to achieve that.

• Marketing needs to build a moat bigger than the product itself. To emerge in a hyper-competitive Gen AI market, founders must embrace a marketing-led approach.

Let’s dive into each aspect in more detail.

1. The Importance of Category Creation and Positioning in the AI Landscape

New AI categories are being formed and existing ones are being disrupted at a dizzying pace. The window of opportunity is narrow, but the potential upside is immense. Successful AI companies will be those that can clearly define the market category they aim to enter and lead, articulating a compelling, differentiated positioning.

This is where marketing takes center stage, spearheading the category creation process in lockstep with the executive team, product, and technology. Drawing on deep market and competitive intelligence, as well as direct customer insights, marketing must lead the charge in identifying the right category to target, crafting a compelling differentiated positioning, and articulating a solid case for long-term leadership.

Crucially, this positioning can't be static — it must evolve constantly, adapting to the market's changing needs and new technological breakthroughs. By shaping the product roadmap and ensuring the positioning is maintained, marketing plays a vital role in propelling the AI company to the forefront as an industry leader.

For example, Cohere has been successful in defining and owning the "Enterprise AI" category for LLMs. When I was leading marketing at Cohere, we did so by carving out a clear and differentiated positioning compared to behemoths like Open AI and Anthropic, establishing ourselves as the go-to provider for sophisticated enterprises. We’ve been consistent in our category messaging and leveraged all our announcements across funding, product launches, events, website, and partners to carve out the category and own it.

In this high-stakes arena, category creation has become marketing's driving mission. 

2. Simultaneous GTM Strategies

Historically, successful startups go to market with one motion — either PLG bottom-up or Enterprise top-down — and then layer the other one on top. However, for Gen AI companies adopting a singular go-to-market strategy won’t be enough. 

Effective market penetration now demands a new level of complexity — the simultaneous execution of not two but three concurrent go-to-market motions — bottom-up product-led growth (PLG), top-down enterprise sales, and partnerships — serving multiple segments at once. Each serves a specific purpose:

• PLG will foster brand recognition and technical credibility, with innovative products and research that will conquer the hearts and minds of the (non-) technical community, driving word of mouth and scaled feedback to influence the product roadmap and build the brand.

• Top-down enterprise sales and partnerships are critical for driving sustainable revenue and cementing credibility at the executive level. By securing large enterprise customers and forging strategic alliances, these high-touch channels ensure speedy market penetration that reinforces the brand at the top of the funnel while earning sustainable and scaled revenue. Specifically:

  • Top-down Enterprise. Every board is asking their CEO about their Gen AI strategy. Your AI company must be top of mind for the C-suite because AI is top of mind for them! They are the executive sponsor and will involve their AI/ML or engineering teams in the conversation because it’s among their top priority.

  • Partnerships. Every enterprise, system integrator, and cloud providers want to offer generative AI solutions to their customers. They are looking to partner with AI players either above or below their (level of the) stack of their core competency. This creates a unique moat opportunity for startups to get co-marketing and distribution muscle via these partners to cross-share customers with a joint GTM motion.

In this ever-complex GTM scenario, the challenge for marketing is clear: they must develop dedicated, coordinated strategies for each motion from the get-go. The stakes are high, but the potential rewards are transformative. By striking the right balance, AI companies can foster rapid adoption, and drive exponential revenue stacked across all the GTM motions.

3. Building Credibility Beyond the Product

In this accelerated generative AI market, the true trailblazers will be those who can shape — and own — the industry conversation way before their full product is ready on the market. That's because it's not only the funding cycle that's at a peak but the news cycle as well. 

That’s why marketing's role must transcend the traditional focus on creating positioning, awareness, and demand for your product among core buyer personas. To truly establish your company as an industry luminary and harvest credibility beyond your product, you need to cultivate relationships with a diverse array of influential stakeholders — AI policymakers, the press, research and developer communities, government leaders, and other key players shaping the generative AI conversation in your category.

It's no longer enough to simply promote "what's on the truck today", a.k.a. your product. Instead, marketing must take a bold, forward-looking stance, articulating your long-term roadmap and showcasing how your company's innovation will transform the future trajectory of AI. This is about shaping the industry narrative, rather than just reacting to it.

At Cohere, we did so by building a brand vision around the executive team. We’ve invested resources so that the CEO and the founding team could share their provocative take on the future of AI targeting specifically decision-makers of the sales- and partnership-led motions — C-suites, executives, and policymakers.

Specifically, we’ve secured speaking slots at the Davos World Economic Forum Panel and published in related publications to ensure that we got on the map of every single world leader and positioned ourselves as a cutting-edge company at the forefront of AI. We’ve completed this presence also by speaking at major events like Collision, National TV broadcasts, and major publications; every channel reinforcing the ever-evolving yet bold vision of the founders.

As mentioned before, Cohere’s corporate and brand communications were integrated by sharing upcoming products and partnership announcements ahead of time as “coming soon”, generating hype and solid brand awareness.

4. Every Marketing Functions Must Have a Growth Mindset

To stay ahead of this market, you need to find new creative levers all the time. You can’t just go through the B2B SaaS playbook. And you can’t just do this in growth marketing, but it becomes an integral part of all marketing functions, especially across Product Marketing, PR & Communications, but also Revenue marketing, and Developer Relations.

Traditional SaaS growth is applied to either bottom-up adoption or top-down demand generation. However, Marketing teams in AI-first companies will need to share a growth mindset across all their functions to meaningfully shape and influence their areas and contribute to category creation and leadership.

It won’t be enough to have the latest tools to create SEO content, demand through paid marketing or automate lead scoring. Growth Marketing and Growth Product teams won’t be the only ones to run AB tests and experiments. In this new game, even PR & Comms, Brand, Product Marketing, Partner Marketing, and Research must embrace the high-velocity, risk-taking attitude of growth teams.

Successful marketing-led motions will focus on extremely lean operations, velocity, tight feedback loops, and focus on concrete outcomes. These cultural defaults require teams with not just functional expertise, but think-on-your-feet, highly resilient, persistent, and adaptable talent. 

While the right talent mix will depend on your category, it could involve hustling your way to the White House AI forums, arranging a roadshow for your leadership and tech team to speak at important events, partnering with your research team to serve the community through open source products or building an entire educational program to create a Generative AI economy for developers. Or educating every reporter of top publications on your narrative to get press mentions every time your competitors are mentioned.

An effective tactic to go against the grain is crafting and owning controversial and memorable opinions around hot topics. Remember the “AI Doomsday” narrative? Leaders from several big AI companies signed a letter warning humanity about the “risks of AI”. However, Cohere’s founders didn’t share that narrative and worked on an opinion piece to counter the mainstream narrative and drive instead their ‘value additive’ vision — educating the market on what the industry should focus on instead. Now, that’s an innovative way to get your company on the map, own the narrative, and rapidly gain mindshare. 

Regardless of the tactics you embrace, everything you do should be focused on penetrating your target market, not following the herd on those topics. The best part is that these initiatives are completely independent of product readiness and new features; when the growth mindset is distributed across all your Marketing functions, you will even move faster, as there won’t be any execution dependencies on product or other teams. This is how you can make things happen.

It's Time for a Paradigm Shift

This requires the marketing team to place a few long-term bets on how the market will evolve and what will drive a sustainable long-term advantage. At Cohere, we’ve made an early investment in building the LLM University — an initiative to upskill developers on the technology, even before the ChatGPT hype stemmed, from the conviction about the skilled resource gap in the AI market. 

This not only boosted Cohere's technical credibility and drove huge traffic, but also helped accelerate the pipeline of potential customers. At some point, this investment alone generated 40% of all Cohere traffic!

Closing thoughts

To successfully stand out in the hyper-competitive Gen AI landscape, startups must embrace a new marketing-led approach, investing early not just in simultaneous GTM strategies, but in shifting marketing from a support and distribution function to a core strategy, where all its teams share a high-velocity, risk-taking growth mindset. This shift involves forecasting future industry directions, redefining product roadmaps through visionary leadership, and executing both bottom-up and top-down marketing strategies concurrently. 

Emphasizing marketing's strategic role ensures products remain relevant and competitive, driving rapid penetration and establishing a solid, beloved brand. This approach not only differentiates companies in a crowded field but also paves the way for sustainable growth and industry leadership.

This heightened complexity brings a burdening question. How do you execute multiple and concurrent GTM motions? And what resources do you need to pull this off? How do you hire a team that builds a category-defining AI startup?

Stick around to find out in the next posts of this series.

This transformative period is taking shape in two main directions:

• Horizontal AI. Leveraging non-specialized Large Language Models (LLMs) like ChatGPT, Claude, and Perplexity to streamline general information discovery, gathering, and organization workflows that would have taken a lot more time to complete without it. This tech has such a broad set of use cases that it’s quickly shifting across the adoption curve — from early adopters to the early majority — in just over a year.

• Vertical AI.  Utilizing innovative solutions from specialized AI startups to inject automation and intelligence to reinvent very specific use cases, like lead list building, copy personalization, and more. Compared to the horizontal AI market — which is fairly concentrated — there is a plethora of vertical AI apps that are emerging across the whole domain spectrum. This makes it very hard to distinguish the good from the bad and the ugly. However, because these use cases are more niche, they’ll take more time to get popular; hence they represent a greater opportunity for a competitive moat.

Moreover, AI evolves at an exponential pace; whereas our human brain does so linearly. This discrepancy is creating a significant bias around the adoption of these AI tools, especially vertical ones. Experiments, that may fail today, could potentially succeed in a few months. For example, AI voice trying to replace SDRs might not work now, but it could work just fine by Q4 2024.

To sum it all up, sole reliance on generalist AI like ChatGPT is already an industry norm in tech, foregoing any competitive edge. On the other hand, sifting through the plethora of emerging vertical AI apps requires expertise to identify truly valuable tools amidst a sea of mediocrity.

How can growth teams employ AI creatively to enhance growth and realize cost efficiencies in their daily operations? In this three-part series, we’ll introduce nine practical use cases from HyperGrowth Partners across three main growth domains: SEO, outbound, and performance marketing.

on AI-Assisted SEO & Product Marketing

SEO has been incredibly sensitive to the rise of LLM, both for technical and content creation purposes. However, most people use AI in SEO for keyword scraping, analysis, and content creation; but we’ll see that teams can get creative with exciting use cases across content and product marketing. Let’s see some of them.

1. Use Writesonic to Create High-Quality Content with AI

As AI dramatically lowers the barriers to creating content, there’s a reason to believe most AI-generated content is spammy; but the truth is that it doesn’t have to be. AI can also create informative and educational content that doesn’t just rank, but it’s also high quality.

Despite there being a handful of tools for AI content creation, Writesonic seems to be among the best positioned for the creation of high-quality, technical, long-form content. In this context, technical content is defined as topics that are fairly niche and for which there’s no log of content on the Internet. Hence, horizontal LLMs don’t have content to scrape to inform the generation of fresh content.

To fill this gap, Writesonic lets you upload or link reference articles, and rich tone of voice docs to ground their model with defined and technical guidelines that increase the output relevance.

For example, we used Writesonic to create a 10,000+ word technical guide on real-time analytics with one of our clients by feeding the LLM with subject matter expertise from previous work, effectively saving countless hours of work to create the same output.

This content doesn’t just rank, but it also generates product signups at scale; which is outstanding considering the amount of time invested in the creation and editing of this content.

Why? Not because, as most people think, Google will penalize you for AI-generated content per se. Google doesn’t do that, or care about that too much. What Google cares about is maintaining a good user experience, as measured by clicks, CTR, and engaged sessions. That’s why, if your users make the effort to search for your keywords, click through your article, but then see something weird when reading it, they’ll disengage. This is what ultimately hurts not just your rankings, but most importantly, your brand credibility.

In the future, fact-checking APIs like Aspire will be able to help fact-check AI-generated content across large data sets to help minimize these hallucinations at scale; but a closed-loop workflow that solves this problem doesn’t exist yet.

2. Use Humata.ai for Qualitative Market Research at Scale

This use case is especially relevant for Enterprise sales, where the sales organization has tens of customer calls every day; with transcripts stored in sales intelligence tools like Gont, Grain, Fireflies, or tldv.

Humata.ai is an AI chat UI — similar to ChatGPT — that specializes in parsing information across long documents. You can upload a large number of PDFs to let Humata extract valuable insights at scale — like keywords, blog title ideas, and other content plays — which can then be handed over to Writesonic or other tools for creation and articulation.

How does it work in practice?

1. Upload your PDF with transcripts from customer calls, community comments, etc.

2. Ask questions in natural language to extract customer pains, solutions, and patterns.

3. Target each pain point or pattern by creating:

   1. Specific SEO content addressing them.

   2. Inform messaging & positioning, 

   3. Personalize outbound emails

Humata is a great example of a horizontal AI app that can be verticalized depending on who is using it. You can get creative and even use it to streamline the onboarding of new hires and provide them with always up-to-date information about the customers, the company, and the market.

Best of all, Humata has private data rooms so your PII and customer data are safe; which might not be the case with other horizontal AI startups.

According to Kevin Indig: “If you haven’t used this already, ChatGPT Chrome Extension is a must-have in your growth toolkit”.

He’s used the extension with HyperGrowth Partners to create data-driven programmatic SEO campaigns that scrape the publicly available data to create programmatic content at scale.

From our dedicated guide on the topic: “Programmatic SEO means building thousands of web pages that target specific long-tail keywords. Relevant examples include:

• Tripadvisor has thousands of pages ranking for ‘best {thing to do} in {city}’

• TimeOut for ‘best {restaurant} in {area}’

• Glassdoor ranks for ‘{job title} salary’

• Wise has tons of pages ranking for ‘best {exchange rates}’

• Statista ranks for thousands of pages for ‘{category} - statistics & facts’

• Causal has recently ranked for ‘calculate {financial formula}’

• Thomas Cook ranks for thousands of ‘{location} weather in {month of the year}’

These are all examples of programmatic SEO or pSEO for short.”

So, how does the process work in practice?

1. Design a prompt for the extension to retrieve data and fill in your pSEO search query. For example, if your pSEO query is: “Average salary in {us-state}”, {us-state} is your key variable of a data set for each of the 50 States in the US.

2. Run the prompt to scrape data automatically from public APIs, and re-run the prompt regularly to update your output.

3. Map the data from Sheets into your CMS with Zapier or custom scripts to land all the data points and AI-generated summaries into your landing pages.

As you might have guessed, a big part of this process is to ensure you have the right prompt to avoid common patterns LLM falls into and avoid content being bland and non-informative. So, ensure you have a good prompt and see your organic clicks scale.

Get the next 6 AI use cases for Outbound and Paid Marketing

It’s no news AI is the revolutionary tech of our decade.

In this post, we’ve seen the pros and cons between horizontal and vertical AI tools and showed real-life use cases of how you can use AI to create technical content at scale, run programmatic SEO campaigns, and even parse your customer call transcripts to extract insights for multiple use cases.

In parts II and III of this post, we’ll dive into more AI use cases, specifically for Outbound and Paid Marketing.