The Age of AI

⏰ Today in 5 minutes or less:

• Customer success is all about understanding the customer’s needs and designing products or services to help them succeed.

• AI will power customer success teams by alerting the them of any issues before the customer even notices.

• Moving to a predictive and automated customer success process means the psychology of value also shifts.

But before we go there…

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Rethinking Customer Success in the Age of AI with Pablo Bernal

If your client can’t see the service, will they still pay for it? 

Pablo Bernal is one of the few people in this country who can answer that with authority. 

As a top leader in enterprise software customer success – and the former Senior Director of Customer Experience and Success at Microsoft – Pablo spent over a decade at the forefront of monetized, enterprise-scale customer success. 

I think he’s one of the top ten most knowledgeable experts in the world in customer success. And we were lucky to have him share his insights.

In this interview, he explains how customer expectations around technology services have changed, how AI is speeding up the shift in customer success, and what this means for consultants working in tech. 

This is one conversation you’ll want to read before the future arrives. A few highlights:

You’ve had a front-row seat on how customer success has evolved. Can you tell me when that shift started and why it was necessary? 

Pablo: Customer Success had a very interesting evolution in the 10 years that I spent in that function at Microsoft. For the longest time, this was focused on technology support. In fact, the business was called Enterprise Support.

The services we sold were centered on the product, with an emphasis on reactive support which was customers' top priority. It was led by a Technical Account Manager who oversaw product adoption and troubleshooting, with field engineers and support agents delivering in the field.

Over time, and with the shift to the cloud, it became crucial to support the full journey of how and why customers used our products – this big picture needed more attention. 

We saw a shift from “support” to “success.” The emphasis moved from just the product to the customer and what they were trying to achieve in their business.

We started talking about success in terms of outcomes defined by our customers – like achieving specific goals with the help of Microsoft technology. 

The enterprise support business evolved to Customer Experience & Success. We went from Technical Account Managers to Customer Success Account Managers, with a focus on helping customers succeed and attain product consumption goals. Our field engineers became Cloud Solution Architects.

These role changes said a lot. It was no longer just about technical support, but about fully understanding the customer’s business, their priority projects, and the risks to address.

We evolved to provide a full journey of customized services that enabled their success. This holistic approach became even more crucial as customers expanded to unified ecosystems of cloud products, platforms, data, productivity and business applications.

This success model required a close partnership between technology and services sellers.

Our teams became experts in formulating service packages with the right baseline support and a mix of optional services. Such add-ons enabled precise tailoring to ensure that critical applications, platforms, and events received the highest levels of expertise and response, even involving Microsoft product engineering as needed.

Our offerings and pricing models also evolved to provide predictability, choice, and scale.

Let’s imagine we’re in a world where customer success is being delivered by AI agents. How does customer success function in this environment? 

Pablo: I think the essence of customer success remains unchanged – understanding customer goals and helping them achieve key outcomes. But how we deliver is going to change dramatically. 

I believe we’re headed toward teams of AI agents coordinated by AI managers that pull from different sources of expertise, surface the right internal and external data, and deliver relevant insights that inform product adoption and usage.

This way, we get a proactive experience that creates a more successful, less risky journey for the customer.

Reactive support will become highly preventative, with predictive analytics that alert customers and providers before something breaks.

AI could take action before the customer even knows about the issue, resolving it at any time, with full expertise, data and context. If customer interaction is needed, it can happen in their preferred language and mode no matter where they happen to be.

When an AI agent needs to escalate to a human, it can pull in the right person and provide them with precise data and insights for resolution.

We’re talking about much higher service quality, lower risk, less friction, and incredible cost efficiency. 

If success loses the human touch, won’t customers stop paying for something they can’t see? 

Pablo: I believe that the essence of customer success will continue. Companies will continue to define their goals and will rely on technology and related services to enable them. As AI brings predictive and automated support, huge new value and scale is being created in the industry.

There’s going to be exponential value for the customer – less risk, faster resolution, and better outcomes.

Tech providers will also unlock huge cost efficiencies in their global scale, always-on expertise, languages offered, etc. This means the P&L will look different too. 

This brings business strategy questions.

Are we passing those efficiencies on to the customer as lower prices? Will this cause a race to the bottom? Or, can we create differentiated value and command a price premium? How can we capture and defend share?

Business models and pricing models will evolve dynamically in years to come as these questions get answered.

Buckle up for a fascinating 3-5 years ahead, with deep evolution and unprecedented opportunity in the customer success function. Winning in this new era will require the right mix of AI-enabled services, monetization models, and success-oriented relationships.

The latter piece is crucial as customers of all sizes, regions and industries face AI-driven opportunity and disruption. The opportunity for partners and consultants is huge to help customers navigate the AI area we are already in.

What We Can Learn from Pablo Bernal: 

1. Customization wins in customer success. Pablo described a “secret menu” approach that tailors service combinations to fit every customer’s needs. Whether you’re building a strategy or scoping a retainer, listen closely and shape your work to fit real-world needs. 

2. Partnerships always beat silos. Misalignment between engineering, sales, and service was always a challenge in this industry. But the fix is always to partner with everyone right on your first day. Before your brief comes, start building rapport with sales, ops, tech and other departments to make sure your work is tied to outcomes. 

3. Context is the competitive edge. There’s a lot buried in a customer’s history, yet it’s often missed. Before creating a proposal, make sure to dig in first – what's been tried, where they struggled, and what benchmarks apply. Context turns good advice into a great strategy. 

Chart Crimes: All Data, No Insight

🚨 Chart crimes! 

OMG this one is terrible.  

At this point, you’re better off looking at the raw spreadsheet than wtf this is. 

Remember, the path to success is paved with continuous learning and embracing fresh perspectives.

Let's stay connected, share ideas, and elevate your consulting business.

Stay curious, friends.

The 2×2 is brought to you by Keenan Reid Strategies

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