Has GenAI Surpassed the Barrier in GTM Adoption in Business Organizations?
Michelle Tan serves as the Head of Growth at Quilt, a business backed by Sequoia that is developing enterprise AI for Go-to-Market (GTM) teams.
The technology adoption curve shows that innovators and early adopters are quick to adopt new technologies, while it takes more convincing for the early majority to join in. Since the release of ChatGPT in November 2022, Language Models (LLMs) have grown in accuracy, speed, and cost, and there have been numerous experiments utilizing LLMs to assist in workflows within GTM teams. However, where are we in the technology adoption cycle? As a GTM leader, is it time to start experimenting or catch up?
According to a McKinsey report, only one-fifth of commercial leaders have fully implemented GenAI for B2B sales as of earlier this year. This percentage may differ depending on the industry, company size, and go-to-market motions.
If you are a software business selling to Small and Medium-Sized Businesses (SMBs) through cold emails, you may be lagging behind if you haven't at least tested using AI for account research or prospecting. Conversely, the impact could be limited for enterprises dealing with multi-million dollar contracts with pharmaceutical companies for the foreseeable future.
Adoption Stages by Technology
Early Majority: Email/Marketing Copy Generation
AI-generated personalized sales emails and marketing copies were popular use cases for LLMs at first, with companies such as Writer, copy.ai, and Jasper leading the way. These tools can be implemented easily, allow for bottom-up adoption, and can convince sales leaders by promising more leads. As language models' voice capabilities improve, tools like 11x, Artisan, and AI SDR, which utilize AI for sales calls, are gaining traction. However, there is still debate on whether they can provide sustained value to companies.
Nowadays, these use cases are relatively common and commoditized. While product quality and user experience can be inconsistent at times, sales reps have grown fluent in using these tools, and equivalently helpful emails can be generated regardless of whether you use an established or AI-native product. We are also beginning to observe secondary effects, such as crowded outbound channels and even stricter anti-spam policies from platforms like Google.
Early Adopters: More Complex Sales Admin Automation
Over time, more effort has been dedicated to more intricate tasks like sales operations and sales enablement workflows, such as CRM updates, call follow-ups, and AI coaching. These tasks usually require top-level approval, company-wide implementation, and direct integration with proprietary data and systems to be successful. Companies like Rox, Attio, and Day.ai fall into this category.
Innovators: Sales Transformation?
Sales, an ancient job linked with commerce, has evolved with technology, but the fundamental objective of understanding buyer needs and building trust has remained the same. As AI agents become more affordable and reliable, it's conceivable that smaller deals could be handled automatically, matching demand and supply more efficiently without the need for thousands of emails. However, this problem is not easy to solve, and many companies have attempted and failed to accomplish it.
Until then, AI is expected to assist human sellers in becoming better versions of themselves, enhancing their knowledge, empathy, and memory of past customer interactions. It could also make the sales job more enjoyable, much like how it transformed coding by eliminating repetitive tasks. Companies such as Quilt are working towards this future.
Your Strategy
Employing GenAI successfully could provide a substantial competitive advantage in expanding the top-of-the-funnel and improving sales team efficiency. However, there could also be arguments for focusing on existing channels and motions that have proven successful when everyone is chasing new ones.
As you implement GenAI products in your GTM organizations, consider the following checklist:
- Understand the landscape: Researching reputable vendors among the wide array of startups and established providers can seem daunting, but taking short demos and staying informed about new products and technologies can be cost-effective. When creating a shortlist, examine the customer base and team background of each vendor, which can serve as a proxy for an early-stage startup's product maturity and capabilities.
- Build vs. buy: Building internal tools with GenAI, particularly for simple tasks like Slack bots, has become easier with advancements in technology. Evaluate whether building in-house makes sense if the GenAI tools will leverage core company capabilities. However, more complex GenAI products often require established solutions for time and resource efficiency.
- Measure ROI and iterate: Not all AI tools achieve the same ROI. Ensure you collect feedback and measure outcomes as you roll out the products, understanding the tangible benefits these tools provide to your team, such as increased calls and hours saved. Companies should also engage with AI vendors for a good user experience.
It's an exhilarating period, yet filled with uncertainty, to be a GTM specialist. However, thriving in this tech-driven era by being mindful of your organization's distinctive requirements is what could mark you out as a top-tier GTM entity.
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Michelle Tan, the Head of Growth at Quilt, might be interested in the advancements in using AI for go-to-market (GTM) teams, as her company is developing enterprise AI solutions.
In the technology adoption cycle, GTM leaders in various industries and company sizes are at different stages of utilizing AI, from email/marketing copy generation to more complex sales admin automation, with emerging interest in sales transformation.
