AI is Raising Customer Expectations: How to Unify Go-to-Market Workflows

Hello go-to-market leaders, strategists, and innovators! 👋 Thank you for dropping by to learn practical AI applications and gain strategic insights to help you grow your business and elevate your team’s strategic value.

Quick Take

AI is changing how customers buy, and it’s faster than we think. Today’s buyers expect seamless, relevant answers at every step. Are your teams ready? To keep up, we need to break down silos and unify every stage of the journey—from research to growth. In this edition, see how AI can transform your approach, with real-world examples and actionable steps to get started.

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Why AI Changes Everything

Ever noticed how AI is pushing us to rethink how we serve customers? It’s not just about better tools. It’s about fundamentally changing how we work together across marketing, sales, and customer success.

The need to break down silos has always been there—delivering the seamless experience customers expect requires unified effort across functions. But with AI driving change, this alignment is no longer optional. AI isn’t just encouraging us to unify; it’s making it possible, especially as buyer behaviors and journeys evolve. As customers increasingly demand fast, relevant answers and direct interactions, AI enables us to remove silos and deliver the experiences they want at every stage.

Here’s why this matters now:

1. AI search is changing buyer behavior – Buyers can now get direct answers to their inquiries instead of scanning multiple websites and resources. They might engage with us differently or later in their journey. Here are five must-do strategies for marketers to stay ahead in AI search.

2. AI serves up insights from across the entire customer lifecycle – Why have separate teams looking at pieces of the puzzle when AI can identify patterns across the whole journey?

The chart below shows how GTM workflows and teams can evolve with AI. We’re moving from siloed teams to AI-connected workflows and, ultimately, to unified customer experiences. It’s about combining “Human Layer – Strategy & Relationships” with the “AI Layer – Shared Data & Insights.” Together, they create seamless, connected customer journeys.

This fundamental shift is changing how companies approach customer success. As Sanjay Kini, Chief Customer Officer at 6Sense, observes:

“Driving success for customers has shifted from being a Customer Success goal to a Company goal. Customers constantly give us crumbs of valuable information through their interactions with Sales, CS, Marketing, Services, Finance, Product and Support.

Great companies will consume this data and convert them into meaningful signals that activate plays to give customers a fantastic Customer Experience. This would be impossible (or really difficult at a minimum) to do without the use of AI.”

Take a simple example: A company researching enterprise analytics software. Today’s AI tools can do the following all before the first conversation:

• Understand their specific forecasting needs from search behavior

• Share relevant insights with sales teams

• Guide customer success on potential implementation needs

And this is just the beginning. AI agents will take this further by actually doing tasks for us autonomously across the customer journey.

Think Amazon’s Rufus (AI shopping assistant currently in beta), which already connects shopping questions to related products and content. I shared my experience in using Rufus in this post.

What This Means for Teams Today

Let’s look at how this transforms each stage of the customer journey, from research through growth.

Wendy White, CMO of Daxko, explains:

“The signals are clear. B2B buyers solve problems in ways that ignore our departmental boundaries. They research deeply, make decisions faster, and expect seamless responses at every step. AI isn’t just making unified experiences possible, it’s making them necessary.

While AI tools are making unified experiences more possible than ever, the real shift is in mindset. Start by mapping your buyers’ natural problem-solving journey. You’ll likely find it doesn’t match most internal processes and workflows. The gaps are your biggest opportunity.”

Let’s follow how this works with our enterprise analytics platform example:

Research Stage

Problem: Enterprise buyers want relevant answers, not marketing pitches

• AI notices they’re researching “predictive analytics for sales forecasting”

• Shows case studies of similar enterprises using your platform

• Shares these early signals with sales to prepare relevant examples

• Instead of marketing just passing a form fill

Buy Stage

Problem: Multiple stakeholders, complex needs

• AI helps connect tech specs to their IT requirements

• Guides sales on which features matter to their use case

• Helps tailor ROI based on their industry benchmarks

• Instead of sales rediscovering what marketing already knew

Use Stage

Problem: Enterprise rollouts are complex

• CS team starts with insights from sales discussions

• AI suggests training programs based on similar customers

• Proactively flags potential adoption blockers

• Instead of CS starting from scratch post-sale

Grow Stage

Problem: Growth depends on proving enterprise value

• AI spots teams ready for advanced features

• Identifies potential new use cases from usage patterns

• Helps predict renewal risks early

• Instead of waiting for quarterly business reviews

AI Agents: Taking Unity Further – With Trust as Foundation

My experience using Amazon’s Rufus showed something important: AI is evolving to handle more complex tasks, but only because Amazon has earned customer trust over years of delivering value.

Trust starts with transparency. Share how your AI works with your customers. Be open about what data it uses and how it makes recommendations. When customers know they’re in control, they’ll be more comfortable trusting AI to assist them.

Here’s how AI agents could change customer interactions when trust is earned:

Companies that build trust now through consistent value, transparency, and customer control will have the foundation for successful AI agent adoption.

Before we jump into specific steps, let’s consider where your company stands today. In a previous post, I explored how Product-Market Fit (PMF) and trust work together to position companies for AI transformation.

The matrix helps you have honest conversations with your exec team about:

• How well you’re solving customer problems

• Whether you’ve earned the trust needed for AI adoption

• Where to focus improvement efforts

• When to accelerate AI initiatives

Remember: Strong PMF and trust aren’t just nice-to-haves, they’re the foundation for successful AI transformation. Companies that excel in both areas will have the customer confidence needed to push boundaries with AI.

Starting Your Journey: Build Value, Earn Trust

Success with AI isn’t just about technology. It’s about solving customer problems and building trust at every step as shown below:

Applied AI workshops are also a powerful way to help teams explore practical, real-life use cases for AI, tailored to specific functions. These sessions can inspire teams with actionable examples, showing what’s possible with AI in their unique roles. Learn more about these workshops here.

Here’s a sample B2B software company scenario:

• Noticed customers repeatedly asking similar technical questions

• Built AI to quickly answer these questions

• Shared answers across sales, CS, and marketing

• Built trust through accuracy and transparency

• Now expanding to more complex tasks

Frank Nardi, CEO of Cloud Coach, puts this journey in perspective:

“AI has the power to transform how we break down silos and work across teams. It’s no longer just about individual departments efforts, marketing generating leads, sales closing deals, and CS driving adoption—it’s about unifying the entire customer experience into one seamless flow. AI does the heavy lifting connecting our data and automates workflows behind the scenes.

My advice? Don’t try to do everything at once. Start by fixing one customer handoff point with AI, prove its value, and then expand. The leaders who thrive will be those who see the customer journey as one continuous stream of value.”

Remember: Meet customers where they are. Some will want AI to do more, others less. The key is giving choice and earning trust through consistent value.

Your Turn to Guide the Future

The shift toward unified, AI-enabled customer experiences is happening now. The rise of AI agents will accelerate this change, but there’s no need to wait.

Start small:

• Pick one customer problem that spans teams

• Use AI to connect the dots

• Build trust through transparency

• Expand thoughtfully

Your competitive advantage will come from how well you:

• Unify around customer needs

• Balance innovation with trust

• Focus teams on strategic value

• Build genuine relationships

Where are you on this journey? What customer problems could you solve by breaking down silos? How are you building trust with AI? Share your thoughts. We’d love to hear and learn from you.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly for go-to-market strategy, product, brand, demand, content, and digital, and growth marketing. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific marketing functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.