10 Ways B2B Tech Marketers Can Leverage AI to Sharpen Messaging and Positioning

AI is no longer a novelty in B2B tech marketing—it’s a competitive necessity. But while many marketers are exploring how generative AI can save time, fewer are using it to actually improve the quality of their positioning and messaging. At The Artesian Network, we’ve been launching and scaling AI-driven companies since 2017, well before ChatGPT introduced the broader market to what was possible. What we’ve found is that the power of AI in marketing isn’t about replacing expertise—it’s about multiplying it. When used well, AI helps experienced marketers move faster, test more ideas, and spot value gaps that human teams often miss. Here are ten best practices we use to get the most out of AI for B2B messaging and positioning:

1. Start with a Value-Claims Analysis Table

Before you write a single line of copy, use AI to map your product’s value claims against those of key competitors or substitutes. At The Artesian Network, we start this process by instructing the AI to summarize the marketing claims from competing product pages into a condensed list of recurring value statements—phrases like “faster onboarding,” “low-code customization,” or “seamless integrations.” To do this effectively, we: – Feed the AI the exact URLs of competitor websites—especially their product, solutions, and use case pages. – Instruct it to extract and normalize claims into consistent language. – Ask it to count how many times each claim appears on each competitor’s site to gauge emphasis and repetition. This process produces a value-claims matrix showing which claims are ubiquitous (table stakes), overused (hard to differentiate), and which are clear gaps your product can credibly own. But here’s the key: We always consolidate down to just 4–5 focused value claims for your product. These should be the claims your messaging consistently reinforces—across site copy, decks, demos, and outbound. Why? Because spread too thin, your positioning becomes noise. Concentrated, it becomes power. And it goes without saying: you must actually deliver on the claims you make. The most dangerous positioning mistake is to promise what the product doesn’t support—because that’s not positioning, that’s just brand erosion in disguise.

Value-Claims Analysis Output

2. Prime AI with a Fully-Formed Contextual Folder

Think of your AI as a junior strategist—it only performs well when it’s given a complete context pack: – ICP and buyer persona breakdowns – Buyer committee roles (typically 6–7 stakeholders in enterprise sales) – Competitive collateral and positioning – Industry-specific language and macro/microeconomic conditions – Pricing dynamics (commodity, premium, or disruptive) – Funding paths (which budget line the purchase comes from) Without this structure, outputs will always lack depth.

3. Clarify the Type of Sale

AI needs to know if you’re competing on price, features, ecosystem, or disruption. For instance: – A commodity sale competes on cost and utility. – A disruptive sale might draw funds from innovation or transformation budgets. That distinction radically changes how you frame benefits, urgency, and objections.

4. Define the Desired Messaging Tone and Voice Upfront

The best results come when you establish whether the voice should be authoritative, optimistic, provocative, or humble. AI excels at adaptation—but only when you’ve specified your brand’s persona clearly.

5. Use AI to Generate Hypotheses—Then Pressure-Test Them with Sales

Let AI rapidly propose alternative ways of expressing your value prop. Then run those through your sales team or revenue leaders. Often, the best ideas don’t come from a boardroom—they come from a rep’s inbox.

6. Train AI to Recognize Vertical-Specific Language and Pain Points

Generic messaging kills enterprise momentum. Load the AI with case studies, call transcripts, and customer quotes specific to your vertical. This primes it to speak the buyer’s language—without sounding robotic or detached.

7. Generate Variant Messaging for Each Buyer Committee Role

Your economic buyer, technical evaluator, and frontline user all care about different things. Use AI to tailor benefits for each: – CTOs care about scalability and integrations. – Operations leaders want faster workflows. – CFOs focus on ROI, not features. AI can handle that personalization at scale—if you feed it role-specific prompts.

8. Map Messaging to the Buying Journey

AI can accelerate the creation of nurture sequences, but only if you map messaging to awareness, consideration, and decision stages. Use AI to test which content themes move leads downstream fastest.

A Typical Enterprise Customer Journey Map

9. Avoid Over-Reliance on Prompt Templates—Invest in Prompt Craft

The best outcomes don’t come from prompt libraries—they come from experienced prompt engineers who understand marketing strategy. AI is only as strong as the marketer guiding it. You wouldn’t give a Ferrari to a student driver. Same rules apply here.

10. Continuously Refresh Competitive Inputs and Market Signals

Your AI models and prompt structures should evolve with your market. Refresh inputs monthly or quarterly: – New product launches – Pricing shifts – Analyst reports – Buyer objections from sales calls This ensures your messaging doesn’t go stale—and your positioning stays ahead.

Final Thought

AI doesn’t replace your positioning strategy. It reveals the gaps, accelerates the iterations, and makes it easier to go to market with confidence. But only if it’s used by people who know what they’re doing. At The Artesian Network, we build that process into every GTM engagement. Because the future of B2B marketing belongs to those who can see the signal in the noise—and move fast on it.

B2BMarketing #GoToMarket #AIForMarketers #PositioningStrategy #MessagingMatters #MarketingStrategy #ValueProposition #TechMarketing #ArtificialIntelligence #CompetitivePositioning

June 1, 2025 B2B marketing No comments yet

Why AI Alone Won’t Save Your Marketing

How We’re Seeing 5x Productivity Gains—But Only When Senior Minds Steer the Machines

The AI hype is deafening. It seems like every week a new tool promises to eliminate your marketing team, write your next viral campaign, or unlock secrets from your data. But here’s the hard truth we’ve discovered at The Artesian Network: AI only works when the person behind the keyboard knows exactly what they’re doing. Only 6% of Gen X professionals are even moderately well-versed in AI, and most of that expertise is limited to basic content generation. For all the power AI tools now hold, they are still just that—tools. Without a skilled strategist guiding the work, they generate output, not outcomes.

The AI Myth: Faster ≠ Smarter

Yes, AI allows junior marketers to “do the things of marketing” faster—generate emails, draft blogs, spin up taglines. But speed alone is meaningless if you’re doing the wrong things, or doing the right things in the wrong way. At The Artesian Network, our process starts long before we prompt an AI engine. We first prime the machine with: – A precisely defined ICP (Ideal Customer Profile) – Market tone and audience nuances – SEO context and competitive language – Technology features mapped to differentiated benefits – A phased, long-term content strategy This is not a junior task. It’s the domain of senior professionals with experience across industries and GTM functions. The art of good prompting is built on the foundation of good marketing. And that can’t be rushed.

Real Numbers: 5x Leverage in Strategic Execution

We’re now seeing a 5 to 1 productivity gain in our engagements thanks to AI. But it’s not just about speed—it’s about scope and precision. We currently use 14 different AI platforms for tasks like: – Value-claims analysis against market competitors and substitutes – Pricing optimization and elasticity modeling – Branding concept development – Website wireframing and layout – Image creation and visual storytelling – SEO analysis and performance optimization – Content generation across formats – Print and collateral layout automation And we don’t just use these tools ourselves—we audit our clients’ AI-readiness, recommend tools tailored to their workflows, and implement AI systems that allow them to scale intelligently after we’re gone.

What’s Next: Personalized Messaging Without the SDR Bottleneck

One of the most exciting AI advances we’re testing this quarter is automated personalization at scale. Imagine: – Dynamic message generation based on prospect-specific firmographics – Consistent brand tone and adherence without manual review – No more dependency on junior SDRs to execute personalization at volume This is the holy grail of outbound marketing—automated precision without losing nuance. And the tech is finally catching up to the dream.

AI Is Ready. Are You?

The promise of AI isn’t theoretical anymore. But the real leverage comes from pairing it with experience. You can’t hand a scalpel to an intern and expect surgery. You can’t hand GPT-4 to a junior marketer and expect a breakthrough campaign. At The Artesian Network, we believe in human-led, AI-powered marketing—where the operator knows what to build, what to ask, and what to ignore. Because in the end, it’s not the tool that wins the race. It’s the one who knows how to wield it.

Want to Learn More? Start with These Guides

For those looking to deepen their understanding of how to integrate AI into effective marketing, we highly recommend the practical resources available at www.mindstream.news. One image was created and shared by Will McTighe.

These are quick, practical reads that reflect the philosophy we follow at The Artesian Network: AI is a force multiplier—but only when paired with human judgment and domain expertise.

May 28, 2025 B2B marketing No comments yet

Hard Lessons from Our Two Epic Marketing Failures

What happens when everything is perfect—except the product? Twice in our history at The Artesian Network, we found out the hard way.

Failure #1: When the Product Isn’t What It Seems

Several years ago, we were retained to revamp the messaging, positioning, and demand generation strategy for a five-year-old cyber-security-related company. Backed by over $130 million in funding from top-tier VCs, the company had only 12 customers—but with that level of investment, we assumed the fundamentals were solid. Sales just needed a tune-up.

Within two quarters, the data told a different story. Our campaigns were hitting the right buyers. Engagement was high. Qualified leads poured in. But deals stalled. Proof of Concepts (PoCs) didn’t convert. The sales pipeline became a graveyard.

We stepped outside our formal scope and dug deeper. What we found was alarming: – Of the company’s 12 customers, only two ever installed and used the product. – One didn’t even renew after the initial contract. – Worse, nearly all of these customers were tied to personal relationships with the CEO or investors—not arms-length, market-driven sales.

The core product had a critical flaw: it depended on a notoriously unstable third-party software component that required constant maintenance and specialized expertise. Most companies didn’t have the resources—or patience—to deal with it.

Despite presenting our findings, we faced heavy pushback. The CEO insisted the market was the problem, not the product. We resigned. Within a year, the company was sold off in an asset fire sale.

Lesson #1: Never Assume Product-Market Fit Has Been Validated—Even by Top VCs.

The startup world often points to the high-profile case of Elizabeth Holmes and Theranos, but there are countless smaller-scale versions of this story across Silicon Valley. Big funding doesn’t guarantee a product works—or that the market even wants it.

Failure #2: The AI Gold Rush That Had No Gold

Our second painful lesson came at the height of the early AI boom. We were introduced to a promising AI software startup by a well-known investor we had worked with many times. The assignment was clear: Build the brand, launch demand generation, and drive $3 million in ARR in year one.

We executed flawlessly—branding, messaging, sales and marketing tech stacks, content strategy. Then we hit a wall.

– No customers could be found for interviews. – The CEO provided “translated” customer quotes and refused to let us speak directly with any users. – With three weeks before the planned launch, we demanded hands-on time with the product.

The result? It didn’t work. None of the promised features functioned as advertised. It was a brilliant idea, but nothing more than a prototype wrapped in a sales pitch.

We raised our concerns directly with the CEO and were dismissed. We exercised our right of termination, contacted the introducing VC as a professional courtesy, and walked away. The company limped along for six more months before being quietly sold for parts. To top it off, we had to fight for nine months in arbitration just to recover unpaid invoices.

Lesson #2: If You’re Marketing a Product, Use It Yourself. And Speak Directly to Real Customers.

Everything begins and ends with the product. No marketing brilliance, demand generation strategy, or sales excellence can make up for a product that doesn’t deliver real value.

Final Thought:

Before you set out to “fix” sales and marketing, run due diligence on the product itself. Validate that the early customer base is real and that buyers aren’t simply friends of the CEO or investors doing favors.

In our Build, Test, Prove methodology, we emphasize this critical truth: “Great marketing accelerates momentum. It cannot create it where it doesn’t exist.” Read more about the Build, Test, Prove framework here: https://www.linkedin.com/pulse/build-test-prove-jonathan-w-buckley-3onqe/

In early-stage technology companies, the hardest lesson is also the simplest: There’s no substitute for a product that works.

#startups #marketing #productmanagement #cmo #growth

May 16, 2025 B2B marketing No comments yet