Key takeaways: 

• It’s time to take action. Big or small, it’s time to put AI-enabled actions into workflows and empower individuals to learn with these new tools.
• Measure outcomes from AI-driven enhancements. To gain traction, leaders must translate how improvements or efficiencies gained from implementing AI solutions result in either more revenue in the door or less costs out the door. 
• Make it about customer value. It may not be right now, but it’s time to start the discovery process on how AI-powered tools can help you better understand the entire customer relationship so optimizations translate to long-term value and business growth.

Our team recently attended Technology Services Industry Association (TSIA) World ENVISION conference in Las Vegas, and without surprise, AI was the hot topic throughout the event. However, a major theme from the event was somewhat unexpected.

While there’s overwhelming agreement that AI will increase operational efficiency and accelerate time-to-value in the customer lifecycle, to get there, companies are realizing that they have to start from a stable foundation, making sure the basics of the customer journey and go-to-market (GTM) motion are solid. This could mean: simplifying the customer journey; clarifying roles to reduce complexity in cross-functional handoffs; aligning team incentives to business goals, and, most importantly, measuring business value at key moments through the customer lifecycle.

Easier said than done, sure. But, with simplicity and increased precision in the basics, companies are recognizing that it’s easier to take action and learn with AI and ultimately reap the gains and spot the areas for improvement. 

Here are three simple ways we learned GTM leaders and teams are taking a step forward with AI today:

• Taking action where there’s opportunity
• Drawing the line to financial outcomes
• Creating space for experimentation

#1: Take action where you can

“AI needs to have action.” A sentiment that was heard over and over again on the conference floor.

The irony (that many of us have forgotten) is for AI to make an impact, we have to do something with it; we have to experiment, train models, spot inconsistencies, and continually tweak it to fit our business needs.

Unfortunately, we, as an industry, have made AI feel like a mountain to climb versus a path to navigate. There are ample ways teams and organizations can get started on their AI journey today, but it begins with taking action; that uncomfortable first step forward to learning and experimenting with something new. 

Here are a few ways we learned organizations are taking a step forward with AI to start turning action into learnings: 

1. Leveraging content and creative tools to create assets more quickly and effectively. A great example of this is using content and creative tools to help build and version sales materials, from emails to presentations, in order to save valuable time and shift priorities to human relationships and outreach. At Totango, we’ve experimented with content generators like Jasper to take existing content like event abstracts and webinar summaries and turn them into social posts or follow-up emails. We agree that this is an approachable way to start getting comfortable and gain learnings with AI. 
2. Integrate AI tools into current platforms to streamline existing workflows. It can be daunting to reimagine entire workflows with AI in the mainstream of day-to-day operations. On the flip side, it might be quite easy to identify individual activities where significant manual processes could be reduced. A great example we heard involves leveraging AI integration to summarize customer support case notes and automatically document them in a servicing platform. This approach has resulted in significant time savings for customer support agents, reducing the need to re-explain or take additional notes.
3. Take an individual approach to experimenting. If company, function, or even team-wide initiatives are out-of-scope, taking an individualized approach can still put ideation into action. This might be popular language models like ChatGPT or Anthropic’s Claude, or it might be a niche tool for a workflow based on function specifics. Either way, creating space for individuals to experiment (and here’s the real win) to share their learnings with a wider group within the organization will start to build learnings faster than you might think. Leaders can even get creative and work AI-focused experimentation into individual learning plans or goals. 

#2: Draw the line to financial outcomes

Studies show AI can lead to 25% faster output and 40% higher quality. But those are just the inputs to helping team members, like account managers and customer success managers (CSMs), work more quickly and save time. 

The next part of the equation is showing what happens next. 

• Did AMs or CSMs use the time saved to have more strategic conversations with customers?
• Did the time saved transfer to more time sharing feedback and insights with product teams?
• Did the faster communications translate to earlier identification of expansion or growth opportunities? 

Executives are expecting AI to improve bottom-line efficiencies and increase top-line profitability. Even if it’s not perfect or if you’re using estimates, start to show the trade-offs that are paving that path to new outcomes and new growth. 

#3: Create space to experiment

More likely than not, individuals in our organizations are experimenting with AI within their roles, even if it’s not regularly adopted into greater workflows. Rather than fighting against the tide, leaders can make the call to bring it out of the shadows and learn from it. 

One company shared the precedent of being “customer zero,” meaning before they take anything AI-related to market, they focus on driving adoption and results internally. 

Even if it isn’t company-wide, think about internal clubs or learning groups that people could self-select to participate in to share and be curious about AI and new tools. This not only creates a sense of ownability and responsibility, but it can act as a tight feedback loop to make the product or feature better. It also helps surface use cases and workflows across the organization where new tools can add value.

Start setting your team up for AI success

While talking about the potential of AI is important, since we are all just beginning to understand and navigate opportunities we don’t have the luxury of just “talk.” At some point (we argue now), we also have to “walk.”

Even if it’s individual steps with trials and errors, it’s imperative that we, as leaders and innovative businesses, start using AI. We need to start making the connections between the impact of efficiency on new outcomes and overall business goals. One foot in front of the other, and that mountain quickly becomes a hill, which becomes a path.

Learn more about how Totango is building an AI-powered engine to help improve churn and growth intelligence for growing enterprises.