I’m thrilled to sit down with Milena Traikovich, a powerhouse in the world of demand generation. With her deep expertise in analytics, performance optimization, and lead nurturing, Milena has helped countless businesses craft campaigns that convert high-quality leads into lasting customers. In today’s conversation, we’re diving into the evolving role of AI in go-to-market strategies, exploring the transition from prompt engineering to context engineering, the importance of relevance over speed, and how businesses can truly harness AI to reflect their unique strengths. Let’s get started!
How did the initial hype around prompt engineering shape the way businesses approached AI, and why do you think it gained so much traction early on?
Early on, prompt engineering was seen as a magic bullet. It was accessible—anyone could tweak a few words and get a chatbot to sound polished or creative. Businesses jumped on it because it felt like a quick win, a way to boost productivity without needing deep technical know-how. It promised a shortcut to harnessing AI, making it seem like strategy was just about crafting the right input. The allure was in the simplicity and the immediate results, even if those results were often superficial.
What were some of the key shortcomings of relying on prompt engineering for delivering meaningful business outcomes?
The biggest issue with prompt engineering is that it’s a surface-level fix. It doesn’t address the fact that AI, at its core, lacked understanding of a company’s specific goals, audience, or value proposition. You’d get outputs that sounded great but missed the mark on strategy or compliance. It couldn’t scale relevance—faster content, sure, but not smarter content. Over time, businesses realized that scaling generic or inaccurate outputs just amplified risks, like misaligned messaging or even legal issues.
Why do you believe context engineering represents a more effective path forward for companies looking to integrate AI?
Context engineering flips the script by focusing on embedding a company’s unique knowledge into AI systems. Instead of hoping a prompt will magically align AI with your business, you’re designing the system to understand your ideal customers, competitive edge, and internal logic. It’s about precision over guesswork. This approach ensures AI isn’t just a tool for speed but a partner that delivers outputs tied directly to your strategic goals, reducing noise and increasing impact.
Can you share an example of how prompt-based AI might churn out content quickly but fail to hit the mark on relevance?
Absolutely. Imagine a marketing team using a generic AI tool to draft a campaign for a niche B2B product. They input a prompt like ‘write a professional email for a tech solution.’ The AI spits out something polished in seconds, but it’s vague—lacking specifics about the product’s unique features, the target industry’s pain points, or the company’s tone. It’s fast, but it’s not actionable. Without context, the content feels like it could belong to any company, missing the chance to connect with the right audience.
What are some of the risks businesses face when they scale AI outputs that aren’t tailored to their specific needs?
Scaling irrelevant or misaligned AI outputs can be a disaster waiting to happen. You’re not just wasting time and resources; you’re risking brand damage if the content or decisions don’t reflect your values or meet compliance standards. There’s also the danger of alienating customers with generic messaging that feels impersonal. Worst of all, if you’re embedding flawed outputs into your processes, you’re building a system that could erode trust with stakeholders—think security breaches or boards questioning ROI due to lackluster results.
How does a focus on context help solve the challenge of making AI outputs more relevant and impactful?
Context is the key to relevance because it grounds AI in the specifics of your business. When AI is trained on your proprietary data—like your sales strategies, customer personas, or brand voice—it stops guessing and starts delivering outputs that resonate. For example, it can craft messaging that speaks directly to your ideal customer’s needs or align with your pricing logic. Context turns AI from a generic tool into a strategic asset, ensuring every output drives toward your unique goals rather than just filling space.
Given that so few enterprises see a real financial impact from AI, what do you think are the main barriers holding them back?
The low impact—only about 10% of enterprises seeing real P&L results—comes down to a lack of alignment. Many companies treat AI as a shiny new toy rather than a strategic tool. They deploy it without tying it to core business outcomes, leading to surface-level activity like more content with less conversion. There’s also a gap in ownership—teams experiment without clear governance or a plan to scale. Without aligning AI to specific revenue drivers or customer needs, it’s just noise, not value.
Why is context particularly crucial for go-to-market teams like marketing and sales when leveraging AI?
Go-to-market teams live and die by precision. Marketing and sales need AI that doesn’t just generate content or leads but understands the nuances of the buyer journey, competitive landscape, and brand positioning. Context ensures AI can tailor campaigns to specific customer segments or provide sales reps with insights that reflect real field dynamics. Without it, you’re stuck with generic outputs that don’t move the needle on engagement or conversion. Context makes AI a true partner for these teams, amplifying their impact.
How does having AI that deeply understands a company’s unique strengths, like its ideal customers or market edge, transform business outcomes?
When AI is steeped in a company’s unique strengths, it stops being a blunt tool and becomes a scalpel. For instance, if AI knows your ideal customer profile inside out, it can personalize outreach at scale, boosting response rates. If it grasps your competitive edge, it can help craft messaging that sets you apart in a crowded market. This kind of AI doesn’t just save time—it drives better decisions, stronger connections with customers, and ultimately, higher revenue by operationalizing what makes your business special.
What’s your forecast for the future of context engineering in shaping how businesses integrate AI into their strategies?
I see context engineering becoming the backbone of AI adoption over the next few years. As businesses move past the experimentation phase, the focus will shift to building AI systems that act as true extensions of their strategy—rooted in proprietary knowledge and designed for precision. We’ll see more investment in structured data pipelines, governance, and human-in-the-loop validation to ensure relevance. The companies that win will be those who treat AI as critical infrastructure, embedding their unique logic into every layer, rather than relying on generic models. It’s a game-changer for how we turn knowledge into revenue.
