Measuring and Operationalizing AI: The Key to Creating Real Impact

Hello and welcome to The GTM Newsletter – read by over 50,000 revenue professionals weekly to scale their companies and careers. GTMnow is the media extension of GTMfund – sharing insight on go-to-market from working with hundreds of portfolio companies backed by over 350+ of the best in the game executive operators who have been there, done that at the world’s fastest growing SaaS companies.

The need to leverage AI is of course known, but how do you actually operationalize it?

Generative AI and large language models (LLMs) have unlocked a new way of working for SaaS companies. By streamlining workflow and scaling efforts with the help of AI, organizations are able to maximize resources and measure impact. AI can also enhance alignment on company objectives, and business leaders are using AI to improve how KPIs are organized and shared.

Incorporating AI into the workflow of an organization is a multi-step process that requires human intervention. Simply employing a tool is not enough — as projects evolve, organizations need to take stock of how AI platforms impact the business overall and adjust accordingly. To qualify and quantify their effectiveness, SaaS companies are setting KPIs to measure AI systems. 

We asked many of the leading AI platforms how they measure the value of AI tools and how they use AI to expedite their work. 

Let’s get into it.

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Measuring and Operationalizing AI 

We asked many innovative AI platforms how they measure the value of AI tools and how they use AI to expedite their work. Three insightful responses 👇

What AI KPIs do you measure? 

Insight from David Zeledon, Paid Marketing Manager at Writer.

Here are some important AI KPIs that I consider to be core benchmarks and should be prioritized for marketers and senior leadership‌:

• Accuracy: This KPI focuses on measuring the precision and correctness of AI-generated outputs, whether it’s generating language or making calculations on data or established business information. Ensuring high accuracy is essential for reliable and trustworthy AI systems, while also ensuring the efficacy of your AI workflows.

• Efficiency: Evaluating the speed and resource utilization of generative AI is vital to optimize performance. Efficient AI can deliver faster results and make the most effective use of computational resources. 

• User Satisfaction: The satisfaction of humans interacting with AI-generated content or services is a critical KPI. Gathering user feedback and assessing satisfaction levels helps in improving the user experience and tailoring AI systems to meet their needs the needs of teams. 

• Cost Savings: AI-driven automation processes can lead to significant cost savings. Quantifying these savings is an important KPI to demonstrate the financial benefits of implementing AI technologies in various business operations. 

• Productivity: AI has the potential to enhance productivity by reducing the time spent on repetitive tasks and streamlining workflows. Measuring the impact of AI on productivity is a valuable KPI to gauge the efficiency gains achieved through automation, like compressing an activity that once took weeks into hours or minutes. 

By monitoring and analyzing these AI KPIs, marketers can gain insights into the performance and impact of their AI systems, enabling them to make informed decisions and drive continuous improvement.

How do you leverage AI to enhance your workflow? 

Insight from Elan Keshen, Product Marketing Lead at Magical.

• Customer Research: I start in ChatGPT with context about the research. I tell it to pretend to be a market researcher focused on our audience, then ask what questions I should ask. Writing these questions from scratch can feel daunting and it’s helpful to have a starting point. 

• Sourcing interview candidates: I use Magical to build a spreadsheet of potential interview candidates with data scraped from LinkedIn and enriched with their email addresses. Then, I use that data to personalize outreach DMs on LinkedIn and emails to get them on a call. I can accomplish two days of research in just two hours. 

What are effective processes for operationalizing AI in an organization?

Insight from Josh Solomon, VP of Sales at Ask-AI.

Here’s what the best companies are doing:

• Dedicating one or two teams as “AI Advocates” responsible for proving the value of AI and creating change internally.

• Selecting use cases that have clear success criteria that are highly measurable provide serious business impact (hint: these often live with customer facing teams).

• Setting the internal foundation for an “AI-first” way of working based on their learnings from these projects. This includes creating AI-Steering Counsels to deploy AI safely, nominating internal champions of AI across multiple teams to drive a cohesive strategy and prevent “tool bloat.”

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In this episode, you’ll learn about ecosystem-led growth, product-market fit, and lessons learned from a $2.6 billion dollar mistake.

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That’s it, that’s all. 

While we are in a phase of AI adoption, this will swiftly shift to AI efficiency. Adopting AI will become table stakes in order to keep up. How well you’re leveraging AI will become the measurement.

The best companies are already doing this.

So while it can be challenging to measure and operationalize something that is evolving at such a rapid pace, putting operational pieces in place is critical for future growth.

Have a great weekend ahead.

I just wrapped a great week in Austin at SXSW with the GTMfund team. Full week of events and last night we capped it off with a GTMfund dinner, bringing together 40 of our executive go-to-market operators and founders. Feeling very grateful.

Barker ✌️

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