Revenue leader says AI adoption needs a business case before it touches GTM

Alexey Navolokin leads AMD's business across APAC, where he drives strategy, partnerships, and go-to-market.

We caught up with Alexey to learn how AI is changing revenue-management strategies. Here's what he said.

Revenue leadership in the AI era isn't about defending a territory.

I lead AMD's business across APAC — enterprise, cloud, commercial, and consumer — driving the strategy, partnerships, and go-to-market to bring EPYC, Ryzen, Radeon, and Instinct into data centers, PCs, and AI infrastructure.

But the road here wasn't linear, and that's the part I want to share.

I started in enterprise tech at companies like Apple and Intel, learning how to build and scale global GTM machines. Then, I jumped into something riskier: leading market and software development for a blockchain startup serving telecom operators. I spent those years consulting startups and SMEs across Singapore on digital transformation — helping them use distributed ledger technology to bring transparency to supply chains, food safety, and global trade. No playbook, no roadmap. Just a conviction that emerging tech would eventually reshape how business operates.

That conviction proved right — just earlier than I expected, and for a different technology than I first bet on.

Blockchain taught me how to sell a paradigm shift before the market was ready for one. That proved the exact skill I needed when AI stopped being a slide in someone else's deck and became the product. At AMD, I've watched revenue leadership itself rewire: GTM strategy now accounts for compute economics, GPU roadmaps, and ecosystem partnerships with governments and hyperscalers — not just channel programs and quarterly targets.

So today, my job isn't just selling processors and accelerators. It's building AMD's AI ecosystem across APAC — engaging government bodies, forging partnerships with cloud and developer platforms, and shaping how enterprises in the region adopt AI infrastructure at scale.

This journey taught me one thing: revenue leadership in the AI era isn't about defending a territory. It's about being early to the next infrastructure shift, staying hands-on as the market catches up, and having the change-ready mindset to rebuild the playbook when the old one stops working.

Selling hardware and platforms through a hybrid model

AMD is a mature, publicly traded semiconductor leader that competes at the frontier of high-performance and adaptive computing — CPU, GPU, FPGA, DPU, NPU, and AI accelerator technology (EPYC, Ryzen, Radeon, Instinct).

We sell hardware and platforms through a hybrid model that includes direct enterprise and hyperscaler deals, OEM and channel partnerships, and software- and ecosystem-adjacent offerings such as ROCm and AI infrastructure solutions.

An AI-powered customer-meeting-prep workflow

We segment GTM across enterprise, cloud, commercial, and consumer lines, and build cross-functional GTM around channel partners, hyperscaler co-sell motions, OEM alliances, and government/public-sector engagement (MSIT, EDB, NIPA) for strategic AI infrastructure partnerships.

We serve markets APAC-wide, with particular depth in Korea, Australia, Singapore, and other SEA countries, spanning data center, PC, AI, and edge infrastructure customers.

How AI reshapes revenue management strategies

At AMD, the company adopted AI tools informally across the company, without a shared way to measure their business value — projects moved forward based on enthusiasm for the technology rather than a clear understanding of cost, benefit, and alignment with business goals.

Then, AMD's IT organization, under CIO Hasmukh Ranjan, restructured how it introduced AI into revenue-facing and operational work. AMD now runs every AI proposal through a business-case filter — cost, benefit, and suitability — before proceeding, with defined KPIs tracking value delivery. As part of that shift, AMD mapped a "day in the life" of specific roles, including sales, and built AI tools tailored to each function rather than deploying generic tools company-wide.

With clear KPIs across functions, AMD can now point to measurable, function-specific gains — finance sees a 15% productivity gain from AI-driven automation, and machine-learning insights have driven semiconductor manufacturing yield improvements worth millions of dollars. An internal catalog of more than 100 documented AI use cases now lets teams replicate what works instead of experimenting from scratch.

The change isn't that AMD added an AI tool to sales — it's that AMD now requires a business case and measurable KPI before any AI initiative touches revenue-facing work, which turns AI adoption in GTM functions from a series of one-off experiments into a repeatable, trackable capability.

Alexey's Advice

I believe AI delivers its greatest commercial impact — not by replacing salespeople, but by helping every seller operate with the preparation and insight of their best day, every day.

One AI-powered workflow I use daily is preparing for an executive customer meeting — from research to follow-up.

It starts with AI aggregating public information: the customer's latest earnings, strategic priorities, technology investments, competitive landscape, and industry trends. Instead of spending hours gathering data, I get a concise executive briefing in minutes.

Next, AI connects that external intelligence with our internal priorities. It identifies where AMD's portfolio best aligns with the customer's business objectives, whether that's AI infrastructure, sovereign AI initiatives, cloud expansion, or enterprise modernization.

Before the meeting, AI builds a tailored presentation, suggests executive-level discussion points, highlights competitive positioning, and even anticipates questions based on previous customer interactions and market developments.

After the meeting, AI summarizes the discussion, captures actions, drafts follow-up emails, updates CRM records, and recommends the next best engagement. What used to take several hours now takes minutes, allowing me to spend more time with customers and less time on administration.

The biggest value here is that AI compresses the time between insight, decision, and customer engagement.

For a global organization like AMD, where opportunities span cloud providers, enterprises, governments, and AI innovators across Asia Pacific, speed matters. Faster preparation leads to better conversations. Better conversations lead to stronger customer relationships. And stronger relationships ultimately drive revenue.

That's where I believe AI delivers its greatest commercial impact — not by replacing salespeople, but by helping every seller operate with the preparation and insight of their best day, every day.

The best areas to use AI — and the results

Areas well-suited for AI assistance in revenue organizations are:

• Pipeline prioritization — surfacing signals from account activity, engagement data
• Deal scoring — pattern-matching against historical win/loss data
• Forecasting — aggregating pipeline data into projections faster than manual roll-ups
• Territory/market sizing

The positive results we've seen so far are:

• Faster proposal-to-draft cycle time on strategic deliverables — financial models and decks that used to take weeks now take days
• More iterations possible per deal/proposal in the same time window, meaning sharper final output
• Ability to personally produce cross-functional assets (model + narrative + visual) that used to require pulling in other teams

• Forecast accuracy improvements
• Churn reduction

On the negative side, I've seen over-reliance on AI-generated scoring that missed relationship/political context in government deals. Also, the time required to review and correct AI output occasionally offsets the time saved by generating it. And, while AI has dramatically improved productivity, that does not always translate into proportional revenue growth.

Why revenue leaders must balance AI efficiency with human judgment

One thing that became harder with AI is knowing when not to trust it.

AI effectively summarizes information, identifies patterns, and accelerates everyday tasks. But in enterprise sales, speed can create a false sense of certainty.

An AI-generated account plan, forecast, or customer briefing may look polished and complete, yet it can miss the subtle dynamics that determine a strategic deal's success or failure — changes in executive sponsorship, organizational politics, budget shifts, or the strength of a trusted relationship.

For revenue leaders, this creates a new challenge: balancing AI-driven efficiency with human judgment.

At AMD, we've found that AI delivers the most value when it prepares our teams for better customer conversations — not when it replaces critical commercial decisions. Our account teams still validate AI-generated insights, challenge assumptions, and apply their own experience before engaging customers or making strategic commitments.

Why AI struggles with deal nuances and trust

AI still struggles to understand the human context behind enterprise deals. In complex B2B sales, especially for AI infrastructure, the biggest obstacles are rarely technical. They're organizational.

AI can analyze CRM data, summarize meetings, identify buying signals, and even predict the likelihood of a deal closing. But it often misses the nuances that experienced sales leaders recognize immediately.

For example, a strategic opportunity might appear healthy because executive engagement is high, the technical evaluation is progressing, and procurement has begun. On paper, AI may score it as a high-probability deal. However, an experienced account team may know that the customer's budget has quietly shifted, a board-level decision has been delayed, or a key executive sponsor has moved to another role. Those factors may never appear in structured data, yet they can completely change the outcome of the opportunity.

At AMD, we view AI as a decision-support system—not a decision-maker. It helps our teams prepare more effectively, identify patterns across opportunities, and reduce administrative work. But when it comes to navigating executive relationships, negotiating complex commercial agreements, or understanding a customer's long-term strategic priorities, human judgment remains essential.

Trust is another area where AI has limits.

Customers making multimillion-dollar infrastructure investments don't choose a partner based solely on the quality of an AI-generated proposal. They choose based on confidence that the technology will perform, that the roadmap is credible, and that the people behind it will deliver over the lifetime of the partnership.

That's built through experience, transparency, and relationships — not algorithms.

How revenue leaders must prepare the organization before integrating AI

If I could go back, I'd spend far less time evaluating AI tools and far more time preparing the organization to use them.

Like many companies, our initial instinct was to ask, "Which AI solution should we deploy?" The better question is, "How do we redesign the way our teams work?" AI doesn't create value just by being implemented. It creates value when embedded into daily workflows and trusted by its users.

Looking back, there are three lessons I wish I'd known from day one.

1. Data matters more than the model. Even the most advanced AI can't overcome fragmented CRM data, inconsistent account information, or disconnected knowledge sources. Clean, trusted data is the foundation of every successful AI initiative.
2. Adoption is the real KPI. It's easy to measure deployed licenses or completed pilots. The harder — and more important — metric is whether sales teams use AI daily to prepare for customer meetings, improve forecasting, accelerate proposals, and make better decisions.
3. Start with business outcomes, not technology. AI should solve a specific commercial challenge: shortening sales cycles, increasing seller productivity, improving forecast accuracy, or identifying new opportunities. Technology should enable the strategy, not become the strategy.

How AI is changing expectations for revenue roles

AI hasn't fundamentally changed who we hire, but it has changed what we expect from every role.

In the past, we often viewed sales, technical specialists, marketing, and operations as distinct functions. Today, AI is blurring those boundaries.

For example, we expect account executives to use AI to research customers, develop account strategies, and personalize executive engagements. Sales managers are becoming data-driven coaches who use AI insights to improve pipeline quality and forecast accuracy. Sales operations teams are shifting from producing reports to designing AI-enabled workflows that help the business make faster, better decisions.

At AMD, we also see increased demand for people who can bridge technology and business. As customers accelerate AI adoption, they want partners who understand not only CPUs, GPUs, and AI infrastructure, but also business transformation, cloud economics, sovereign AI strategies, and return on investment.

That means the most valuable talent increasingly combines commercial leadership with technical fluency.

Equally important is AI literacy. We don't expect everyone to become a data scientist, but we do expect teams to understand how to work effectively with AI—how to ask better questions, validate AI-generated insights, and apply them responsibly in customer engagements.

And perhaps the biggest shift is that AI is elevating the value of uniquely human skills. As AI automates research, meeting preparation, and routine administrative work, relationship building, executive communication, negotiation, strategic thinking, and customer trust become even more important.

In many ways, AI isn't reducing the importance of great salespeople — it's increasing it.

Alexey's Advice

The best salespeople are no longer simply those with the most product knowledge — they connect business objectives with AI transformation…That’s probably the biggest lesson I’ve learned.

How AI is changing GTM, pricing, and sales

For years, product differentiation, relationships, and price largely drove enterprise technology sales. Those factors still matter, but AI has fundamentally changed the conversation.

Today, customers are no longer buying individual products. They're investing in complete AI outcomes.

That means conversations have shifted from "Which processor is faster?" to "How quickly can we deploy AI? How much will it cost to train and run our models? Can this infrastructure scale? And are we locked into one ecosystem?"

As a result, our go-to-market strategy has evolved.

We now focus on how the entire portfolio works together to help customers build scalable AI infrastructure.

AI has also changed pricing discussions.

Customers increasingly evaluate total cost of ownership, performance per watt, infrastructure utilization, software openness, and long-term flexibility rather than simply comparing hardware acquisition costs. The conversation became much more strategic.

Finally, AI has changed how we sell.

The best salespeople are no longer simply those with the most product knowledge — they connect business objectives with AI transformation. They need to understand industry challenges, data strategy, sovereign AI initiatives, cloud economics, and organizational change just as well as technical specifications.

That's probably the biggest lesson I've learned. AI hasn't changed what customers value — it has changed how they define value.

Five priorities for CROs who are adopting AI into their organizations

For CROs, AI isn't just another software purchase — it's becoming a competitive differentiator. But success won't come from deploying the most AI applications. It will come from building the right infrastructure, data foundation, and organizational capability.

Here are five priorities I'd recommend:

• Invest in AI infrastructure that can scale. The greatest source of value isn't the model itself — it's the ability to train, fine-tune, and deploy models efficiently. Open, high-performance infrastructure gives organizations the flexibility to innovate without locking into a single ecosystem.
• Turn AI into customer value, not just internal productivity. Faster proposal generation and automated CRM updates are useful, but the real opportunity is delivering better customer experiences, personalized engagement, and faster business outcomes.
• Make your data AI-ready. AI is only as good as its underlying data. Clean, secure, governed data is now a strategic revenue asset.
• Empower sellers with AI instead of replacing them. The highest-performing sales teams will combine AI-driven insights with human expertise, trust, and relationship building. AI should eliminate administrative work, allowing sellers to spend more time with customers.
• Think long term. AI demand is accelerating rapidly, but today's infrastructure decisions will determine your competitive position over the next decade. Choose platforms that deliver performance, openness, and flexibility as workloads evolve.

The companies that will lead the AI era won't necessarily be those with the biggest budgets. They'll be the ones that build the most adaptable AI infrastructure and empower every employee to use it effectively.

And AI won't replace great revenue leaders. But revenue leaders who embrace AI — and the infrastructure that powers it — will outperform those who don't.

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