Amit Pande is a veteran enterprise leader and the current VP and GM of Commercial AI Applications at C3 AI, bringing over 20 years of experience from giants like Oracle, Yahoo, and HP. A specialist in the evolution of Go-To-Market (GTM) strategy, he has pioneered the shift from siloed sales and marketing functions toward unified "Systems of Action" that leverage AI to drive commercial execution. Amit is a graduate of the Stanford Graduate School of Business and a recognized expert in navigating the digital transformation of deeply regulated industries, where he focuses on using AI to augment human expertise through sophisticated storytelling and "concierge-style" buyer experiences.
1. From Silos to Strategy: The Rise of the GTM Identity
The Birth of a New Identity
Amit points out that 10 years ago, "GTM" wasn't even a common acronym. Companies used to have separate departments that rarely spoke the same language. Now, GTM has become a "foundational construct."
The Shift: At innovative companies like OpenAI, employees across various roles now identify simply as "GTM."
The Adult Language: While tech startups use "GTM," large Fortune 500 companies (like mining or chemical firms) use the term "Commercial Execution" to describe the same integrated approach.
Breaking the "Silo" Culture
The speakers laugh about the "old days" where Marketing Ops and Sales Ops were practically enemies—using different tools, different metrics, and never talking to each other.
Convergence: Modern organizations are moving toward GTM Ops. This treats the entire customer journey—from the first email to the final contract and long-term support—as one continuous thread.
The Result: Customer Success is no longer the "poor cousin" of the revenue department; it is now seen as a vital part of the strategic engine.
AI vs. Legacy Systems
The conversation turns to the "AI wave" currently hitting the business world. Amit and Alex discuss what is changing and what is staying the same:
AI is Eating Legacy Tools: New AI-driven tools are rapidly replacing older software that handled simple tasks.
The "Intestines" of the Company: Despite the AI hype, Amit argues that Systems of Record (the core databases where a company’s most important data lives) are safer from being replaced. Alex compares these systems to "intestines"—they are messy and difficult to rip out, so companies are hesitant to replace them with unvetted AI tools.
What Remains Constant?
Despite all the new technology and fancy acronyms, the core mission hasn't changed: Serving the customer. Whether you call it GTM or Commercial Execution, the goal remains understanding the customer better, acquiring them faster, and helping them grow.
Commercial execution is kind of the adult language of go-to-market in a Fortune 500 context. (Amit Pande)
2. The Evolution of Software: Record, Engagement, and Action
The Four Layers of Software
Amit explains that for decades, businesses have been buying different "systems" to solve different problems. He breaks them down into a clear hierarchy:
| System Type | Purpose | Example | Current Status |
|---|---|---|---|
| System of Record | The "Canonical Truth." Compliant databases of facts. | CRM, Payroll, HR Records | Secure. Hard to replace because they are the foundation of truth. |
| System of Engagement | Where people actually work and communicate. | Slack, Email, Portals | Under Pressure. Being absorbed by AI. |
| System of Intelligence | Analyzing data to provide insights. | Forecasting tools, BI dashboards | Interim. Only useful if it leads to action. |
| System of Action | The Goal. Tools that actually execute a task. | Automated enrollment, AI Agents | The Future. Where orchestration happens. |
The "Learned Helplessness" of Big Industry
One of the most profound "nuggets" of the talk is the difference between Silicon Valley and the rest of the world (Fortune 500 industries like steel, mining, and aviation).
Silicon Valley: Companies here buy "point solutions" (small, specific apps) to sit on top of other apps. They are constantly optimizing tiny parts of their workflow.
The "Real" World: Large, traditional industries often suffer from "learned helplessness." They have dozens of databases (Systems of Record) but never bought the "Engagement" or "Intelligence" layers that Silicon Valley did. They have mountains of data but don't know how to use it to grow their business.
The Power of "Orchestration"
Amit argues that the next big wave isn't just "more AI," but Systems of Orchestration. * The Concept: Instead of having four different apps for records, chat, intelligence, and action, AI "agents" will orchestrate across all of them.
The Result: It connects point A to point B without the user needing to hop between 10 different windows.
Bridging the "Consumer Gap"
Alex shares a story about insurance brokers who use interactive "benefits guides" to help employees choose health plans. This is a perfect example of moving from a System of Record (a boring PDF list of plans) to a System of Engagement/Action (an interactive calculator that signs you up immediately).
Amit concludes that enterprises are finally catching up to the "Technicolor" world we experience as consumers. We expect our work tools to be as immersive and helpful as our cars or our favorite streaming apps.
The systems of orchestration are really evaporating a lot of the layers that had emerged on top of the systems of record. (Amit Pande)
3. The "Technicolor" Turn: How Big Business is Finding Its AI Edge
The "Silicon Valley Jealousy" Phase is Over
For years, huge industrial companies watched Silicon Valley with a mix of envy and skepticism. Tech startups were buying dozens of "point solutions"—specialized apps for sales, marketing, and forecasting—while traditional giants felt they couldn't afford to experiment.
The High-Tech Bubble: Tech companies grew at massive margins, allowing them to stack tools like Salesforce, Gong, and SalesLoft on top of each other.
The Industrial Reality: Companies like Honeywell couldn't just play with new software; they had to stay focused on steady growth and legacy systems.
The Great Shift: From "Buying" to "Building"
Amit argues that the biggest change in the last few years isn't just the AI itself, but a change in mindset. Large companies are no longer just looking for an app to buy; they are looking for platforms they can build upon.
The No-Code Revolution: AI is finally fulfilling the promise of "no-code." It allows companies to create their own custom tools (AI agents and apps) as easily as they used to create a PowerPoint deck.
Content vs. Apps: Amit notes a fascinating psychological barrier. Companies are terrified of "deploying internal apps," but they are perfectly comfortable "deploying content." By making AI agents feel more like interactive content, the technology is becoming much easier to adopt.
The Original "Low-Code" Hero: Excel
Alex points out that we’ve actually had "apps" in the enterprise for 20 years: Microsoft Excel.
The Hidden App Builder: For decades, pricing analysts and consultants have built incredibly complex models in Excel, wrapped them in a nice interface, and used them as functional applications.
Why Off-the-Shelf Fails: In specialized industries like pharmaceuticals (Novartis) or semiconductors (AMD), standard software often doesn't work. Their pricing and supply chains are too unique.
They’re looking at these technologies and saying, 'What can I buy that I can build upon in a way that I couldn’t do even two years ago?' (Amit Pande)
4. From Messy Data to Meaningful Guidance: The "Concierge" AI Revolution
Why the Mobile App Era Failed
Amit argues that companies made a major mistake during the last tech wave: they tried to build internal "app stores" filled with dozens of specialized apps.
The 1% Rule: Most business apps have 25 screens, but users only ever need one. The friction of downloading, logging in, and navigating a complex app for a simple task (like uploading an expense) killed adoption.
The "Slow Reduction" Technique: Amit suggests that if you "boiled down" all of a company's software, you’d find that only about 1% of the screens actually provide core value. The rest is just noise.
The "Talk to Your PDF" False Start
The speakers agree that the first wave of Generative AI was a bit of a gimmick. Simply "chatting with a PDF" isn't enough.
What People Actually Want is Guidance: Users don't just want a search bar; they want a concierge.
The "Concierge" Experience: Instead of a cold interface, the next generation of software acts like a guide, saying, "Let's talk about your dental plan" or "Here is exactly how this fertility benefit works for your specific situation."
Humanizing High-Stakes Decisions
Alex shares a powerful example of how this works in the real world: Employee Benefits.
The Life-Changing App: For an employee trying to start a family, finding fertility coverage in a 150-page PDF is a nightmare. When AI can answer that question instantly and accurately, it moves from "HR software" to a tool that helps fulfill a life dream.
Competitive Necessity: In conservative industries like insurance and manufacturing, offering this level of transparency is no longer "science fiction"—it’s a requirement to stay competitive.
Overcoming "Data Shame"
Amit notes a surprising psychological barrier in large, regulated companies: guilt and shame about their data.
The "Messy Closet" Syndrome: Many CIOs feel embarrassed that their data is scattered across "sucking" SharePoints, old Teams folders, and ancient SAP systems.
The AI Solution: The breakthrough for these industries is AI that doesn't require a "data clean-up" first. Modern AI can meet the data wherever it lives—letting companies gain value without having to reorganize twenty years of digital clutter.
If you did a reduction technique and said, 'Let us just put all the web and mobile applications of the previous era on a slow boil... and see how many real core value screens are needed,' you'll find that people are actually using maybe 1% of those screens. (Amit Pande)
5. The "Coexistence" Era: How AI Integrates with Big Industry
Addressing "Data Shame" and the Plumbing Problem
Amit explains that for most large companies, the conversation doesn't start with "cool AI agents"—it starts with security and compliance (like FedRAMP certifications).
The Elephant in the Room: Most companies have "data shame." They are worried their data is too messy, duplicate-heavy, or "stale" for AI to even work.
The "Backpack" Model: Amit advocates for a "Forward Deployed Engineer" approach. Instead of just selling software, vendors come in with a "backpack of AI tools" to fix the customer's plumbing first—cleaning up duplicate data and fixing pipelines before the AI ever starts answering questions.
The "Coexistence" Strategy
One of the most insightful moments is the debate over the "Master Agent." Amit suggests that the dream of one "Super AI" that runs the whole company is unrealistic for big industry.
Master Agent vs. Specialized Agents: Instead of one supervisor, large companies will likely have dozens or hundreds of specialized agents (from SAP, Salesforce, or custom-built) working alongside each other.
Respecting the Workflow: You cannot ask a regulated company to "kill" their existing, approved workflows. AI must coexist with what is already there.
The Innovation "Butterfly"
Amit describes the innovation landscape as a butterfly. The two wings represent the easiest starting points for AI, while the "body" in the middle represents the complex human interaction.
| Part of the Butterfly | Business Function | AI Role |
|---|---|---|
| Left Wing (Top Funnel) | Marketing & Lead Gen | Moving budget from billboards/trade shows to digital, automated outreach. |
| Right Wing (Bottom Funnel) | Customer Support | Productizing the "call center" to give instant, expert answers to complex manuals. |
| The Body (Middle) | Sales & Solutions | Augmentation, not Automation. AI acts as an "expert on your shoulder" helping humans close deals. |
The Rise of the "AI Council"
Amit notes that every Fortune 500 company is currently forming an AI Council.
The Bureaucrat vs. The Buyer: Sales teams need to figure out if these councils are there to "throttle" AI with red tape or if they actually have the budget to buy.
Board-Level Metrics: The most successful AI projects start by solving an ancient problem (like customer wait times) that is already a high priority for the company's board of directors.
Everybody has a lot of guilt and shame about their data... and you have to address that elephant in the room before you can do anything with AI. (Amit Pande)
6. The "Invisible" Room: How AI Wins When Humans Aren't Around
The "Shopping Experience" Strategy
Amit argues that AI’s greatest value is creating "meaningful moments" when a human can't be present. He compares the future of digital sales to a high-end luxury shopping experience in Europe or Asia.
Low Pressure, High Utility: The buyer should feel "unwatched" and "unhassled," yet have access to 500 products (or data points) the moment they want to see them.
Warming Up the Buyer: AI should serve up "utility toys"—calculators, interactive product tours, and simulated worlds—that get the buyer in the mood to solve their problem.
The Marketing Team of the Future
Amit reveals the three specific roles he would hire for an AI-powered marketing team. Notice how they blend deep technology with high-end creativity:
| Role | Responsibility |
|---|---|
| The Creative Producer | Wields AI tools to create bespoke, "Technicolor" content experiences. |
| The GTM Engineer | The "architect" who rigs the entire technology stack together so it works seamlessly. |
| The Party Thrower | Focuses on real-world, high-touch "salon style" events to humanize the digital brand. |
From Boring Emails to Bespoke Experiences
The speakers agree that the standard "follow-up email" is dead. In the future, a decision-maker shouldn't receive a text summary from an AI bot. Instead, they might receive:
Bespoke Narration: A 5-minute audio or video summary narrated by an AI version of the salesperson, explaining specifically what was missed and why it matters to that executive.
3D Product Tours: Software demos that feel like "buying French luxury" rather than clicking through a spreadsheet.
The AI Embodiment: A digital version of a leader's ideas that a buyer can interact with, making them feel like they are "talking to the expert" even at 2:00 AM.
It's like bored people receiving boring materials. That's traditional B2B... [Imagine] being able to make it exciting and then get people out of their, like, 'Oh, another meeting on Zoom. (Amit Pande)
7. Beyond the Sugar Crash: Finding Our New Identity in the AI Era
The Multi-Modal "Nirvana"
Alex highlights that the future of sales isn't just one tool—it’s multi-modal.
The "Choose Your Own Adventure" Buyer: Some buyers want a visual chart, some want an audio summary for their morning walk, and others want a zero-friction link they can instantly forward to their boss.
The Buyer Experience Center: The goal is a personalized portal that adapts on the fly based on the device you're using or where you are in the sales cycle.
The Three Levers of Modern GTM
The speakers identify the three core pillars that will define successful companies in the 2027 era:
Salespeople acting like Marketers: Using high-end, personalized content to tell stories.
Marketers acting like Sellers: Getting deeply involved in the specific needs of an individual buyer.
Getting People into the Product: Creating "time-boxed" or "ungated" versions of the software so buyers can "taste" the value immediately—much like the open pyramid entrance at the Louvre in Paris.
The Rise of the "CMRO" (Chief Marketing & Revenue Officer)
Amit believes the traditional silos are crumbling. He predicts the rise of leaders who combine product expertise, marketing storytelling, and sales grit.
Marketing is the "Connective Thread": Marketing is the only function that can touch every stage of the funnel—from "reanimating" dead leads to walking alongside existing customers to ensure they get value (LTV).
The Consumer Brand Mentality: Even B2B software in boring, regulated industries must start thinking like the world’s best consumer brands (like Apple or Nike) to keep customers engaged.
Parting Wisdom: The Short-Term "Sugar Crash"
Amit leaves the audience with a grounded, yet optimistic, outlook on our future with AI:
The Identity Shift: AI will cause a "short-term sugar crash" and an "identity crisis." We might feel threatened when AI writes a report better than we ever did.
The 80% Rule: Amit suggests that 80% of the work we’ve done in our lives probably wasn't that valuable anyway. AI is here to take that 80% so we can focus on the 20% that actually matters.
The "Smarter, Lighter" You: By embracing these tools at a "grassroots" level and becoming hands-on, we can redefine ourselves. This process will ultimately lead to a more interesting, creative version of our professional selves.
If you were to hire three people in your AI future marketing team... the first person is a creative producer that knows how to wield AI tools to create bespoke content experiences. The second is a GTM engineer who knows how to rig the stack together. And the third is someone who knows how to throw a great party. (Amit Pande)
Check the episode's Transcript (AI-generated) HERE.
To continue the conversation with Amit Pande, connect with him via his LinkedIn.
Other Episodes
Godard Abel | CEO of G2
S 01 | Ep 6 Where You Go for Software: Reach Your Peak
Dean Stocker | CEO of Alteryx
S 01 | Ep 8 Turning Your Customers Into Your Biggest Champions
Peter Fader | Co-Founder of Theta CLV
S 01 | Ep 10 Turning Your Marketing Into Dollars
AUTHOR.png)
The fastest way to build digital experiences. We empower businesses to convert PDFs, presentations and other content into interactive experiences & webpages with instant branding, analytics & more
.png)
