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From Vision to Value
Architecting an AI Strategy That Works
Building Your AI North Star: Vision That Actually Drives Value
We are proud to announce that this week’s newsletter is sponsored by LeanData, the Intelligent GTM Orchestration platform that empowers revenue teams to turn buyer signals into actionable insights—fueling growth across your entire tech stack.
Generic AI visions will generate generic and non-ROI results for your organization. "Become an AI-first organization" means nothing. "Leverage AI for competitive advantage" will not inspire anybody to look at or buy your product/service. Your vision needs to be specific, measurable, and resonate emotionally with the buyer.
The 3-Horizon Vision Architecture
Horizon 1 (6 months): Operational Excellence
Define specific processes AI will optimize. Not "improve customer service" but "resolve 70% of customer inquiries in under 60 seconds using AI, improving NPS by 15 points." Aflac achieved this specificity, resulting in a 94% first-call resolution rate and $38 million in annual savings. Your 6-month vision must clearly outline the departments, metrics, and dollar impacts.
Horizon 2 (18 months): Competitive Differentiation
Identify the features and functionality that differentiate you from your competitors. Stitch Fix's vision wasn't "use AI for retail" but "create personal stylists that know customer preferences better than customers know themselves." Being very specific, this enables their feature, the Style Shuffle, to process 2 billion preference data points.
Your differentiation vision must describe customer experiences that are impossible without AI.
Horizon 3 (36 months): Business Model Innovation
Define how AI transforms your operations and what/why you sell, not just how you operate. Progressive Insurance transitioned from selling policies to offering "protection as a service," utilizing AI to prevent accidents rather than just process claims. This vision drove Snapshot, fundamentally changing their business model.
Your transformation vision must describe new revenue streams, not just cost reductions. Cost reductions are often more challenging for customers to implement and recognize.
The Value Commitment Framework
Visions without commitments are dreams, and they will most likely not turn into revenue. Embed four commitments into your vision:
Financial Commitment: Specific ROI targets with timelines. "Achieve 4:1 ROI on AI investments within 18 months, generating $150M in value by 2027." This forces discipline in initiative selection and ensures you're aligning to EBITDA conversations with your board.
Customer Commitment: Measurable experience improvements. "Reduce customer effort score by 50% while increasing lifetime value by 30%." This ensures AI serves a purpose, not technology, and will drive value for your customers. This will also make it easier for your sales and marketing team to execute on a revenue strategy.
Employee Commitment: Ensure you're communicating to the employees in the workforce about the specifics that you're trying to develop when delivering an AI strategy. "Upskill 1,000 employees in AI collaboration, eliminating zero jobs while creating 200 new roles." This addresses fear while building capability. You want to ensure that you are aligning with the employees and not scaring them.
Societal Commitment: In some situations, consider ethical and environmental targets. "Reduce algorithmic bias by 90% while decreasing computing carbon footprint by 40%." This builds trust and attracts talent, which will depend on you, your market, and what you are selling.
The Reality-Vision Bridge: Your Strategic Alignment Map
The gap between the current state and the vision/strategy is what we call the AI GTM Gap until you build bridges. The strategic alignment map creates executable pathways from reality to aspiration.
Bridge 1: The Capability Evolution You cannot go from 0 to 100. You need to take a step approach. Identify a few places that you may want to work on AI and build up to AI execution.
Humana's progression:
Descriptive analytics (understanding member health)
Predictive analytics (anticipating health events)
Prescriptive analytics (recommending interventions)
Autonomous analytics (self-adjusting programs)
Each step built upon the previous one, reducing risk while accelerating learning. Map your evolution: what capabilities must precede others until you are utilizing AI.
Bridge 2: The Value Proof Sequence Link each capability to a specific value—this is essential in AI or GTM. Netflix didn't build recommendation AI, then find applications; they identified a challenge, for example abandonment problems, designed a strategy and then built targeted solutions.
Your sequence:
Identify value pools
Quantify impact potential
Design minimal viable AI
Measure actual impact
Then scale
This progression ensures every capability investment returns value to not only the company, but also to your customers. This becomes an iterative process, and you have to make sure that you are continuing to test throughout your sequence.
Bridge 3: The Adoption Acceleration Path Technology implementation isn't adoption—adoption requires behavior change. Microsoft's AI adoption path: productivity tools (familiar context), collaborative AI (shared experience), autonomous AI (trusted delegation). Each phase built trust for the next.
Design your path: where will users most readily accept AI? How does each success enable the next?
Next week: The intensive workshop framework that transforms assessment and vision into executable strategy through five structured sessions.
Dale Zwizinski, Editor of Revenue Creator, and Chief GTM Officer at Revenue Reimagined.
Leandata Presents: The 10th Annual OpsStars
Heading to Dreamforce?
If your 2026 plan centers on pipeline quality, post-sale growth, and AI readiness, join us at OpsStars on October 15 at The San Francisco Mint.
This free, one-day event brings together GTM leaders from OpenAI, NVIDIA, Clay, G2, TechTarget, and more to explore the future of go-to-market strategy.
Space is limited!
Come be part of the future of revenue!
Featured RevGenius Events
GTM Hot Takes Ep 10: Oct 9, 11AM - 12PM ET
In-House SDRs: Asset or Liability?
The in-house SDR team was once a non-negotiable. Now, is it just an expensive liability? Join our debate pitting GTM veterans against outsourcing pros to find out. You'll learn about the hidden costs, how to protect your brand with an external team, and if outsourcing can truly scale for the enterprise.
Where do you stand? This debate will reshape your entire pipeline strategy.Sponsored by Seamless.AI
→ Register Now (free)
Xfactor Roundtable: Oct 21, 11AM - 12PM ET: They are bringing their GrowthAI operating system and ending the "guesswork era" in GTM by helping revenue teams use AI for better planning and execution. At the event, you can hear their keynote on GTM and AI, join a roundtable with top minds, and see how their system replaces static dashboards with a dynamic, unified approach.
→ Register now (free)
LandBase Hackathon, Oct 21, 1PM - 2PM ET: Join the Hackathon to build a complete, AI-powered GTM campaign in just 30 minutes and compete for the grand prize. What normally takes weeks and multiple tools, you'll accomplish in one seamless flow through a simple chat.
→Register now (free)
Attention | AI Agents & The RevOps Revolution
Topic: Dive into how AI agents transform raw customer interactions into the single source of truth for your revenue engine. Discuss practical use cases to improve forecasting, streamline operations, and deliver personalized experiences at scale.
→Register now (free)
Everett Berry | Clay
Topic: In his session, "Foundational Data for GTM Success," Everett Berry from Clay will break down the critical data requirements necessary for long-term growth and successful AI adoption, showing you how to build clean, structured systems that scale instead of just trying to fix a broken one.
→Register now (free)
Ann Bisordi | OpenAI
Topic: Join a panel of top sellers from leading AI companies to explore the future of the sales profession. They will discuss how to land a job in the AI industry, the unique challenges of selling AI products, and how AI is fundamentally transforming the way sales is done.
→Register now (free)
GTM Hot Jobs
Creator DNA Drop
AI can write - but can it design? 🎨
Someone in our GTM Slack channel asked: “Is there any AI that actually makes good images from prompts?”
The community went off.
The top tools mentioned?
Gemini, Adobe Firefly, ChatGPT’s image model, Nano Banana, and Canva. Each had fans but no clear winner. Firefly’s free version has limits, Canva’s results vary, and even the best tools struggle with consistency.
What actually makes the difference isn’t the tool, it’s the prompt. The creators getting standout visuals aren’t typing random descriptions; they’re crafting precise, detailed instructions. Some even use ChatGPT to refine or rewrite their prompts before generating images.
Pro tip: when you land on an image you love, ask ChatGPT to act as a “prompt engineer” and reverse-engineer the command that created it.
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