How AI Helped Maximize Our New Offer

How AI Helped Maximize Our New Offer

Getting Our Commercial Offers Right

For established companies, the decision to integrate AI is not made lightly. It’s a strategic imperative, a commitment to transformation that must be built on a foundation of trust, clarity, and measurable ROI. Yet, too often, the journey begins with a confusing array of services, mismatched pricing tiers, and value propositions that don’t resonate with the complex realities of an existing enterprise.

At System in Motion, we believe that mastering AI shouldn’t be harder than the transformation it promises. Our promise is to empower businesses with AI mastery through high-quality training, proven success, and expert guidance.

But how do we ensure our own commercial offer reflects that mastery? How do we architect a service portfolio that is not just a menu of options, but a clear pathway to success?

This is the story of how we used a rigorous, iterative, AI-powered process to analyze, test, and refine our new AI service offering. It’s a case study in strategic alignment, proving that for us, quality and clarity are not just values.

Language and Structure

Our strategic expansion into the dedicated Use Case pillar was a direct response to a critical market gap we identified: our clients, established leaders in their fields, had mastered AI fundamentals through our training and possessed the technology via our integrations, but they consistently struggled with the crucial ‘how’: how to translate this newfound capability into tangible, operational value that addressed their specific industry challenges.

This clear requirement demanded a structured and integrated offer. We moved beyond a complex matrix of options to institute a simple, maturity-based pathway: Level 1 for foundational application, Level 2 for integrated process enhancement, and Level 3 for transformative, ecosystem-wide innovation. To ensure this structure was not just logical but powerfully aligned with our brand promise, we employed AI as a collaborative ideation partner. We fed it our key brand elements and tasked it with generating ten working thematic models.

From this AI-generated landscape, the triad of Core, Processes, and Eco-system emerged as the unequivocal choice. This taxonomy perfectly articulates the value progression we deliver: from establishing a solid Core foundation, to optimizing critical Processes, and ultimately enabling a seamless, intelligent Eco-system, providing our clients with a crystal-clear roadmap to mastery and measurable ROI.

We then brainstormed the key products, price points, benefits and level of services associated with our customer segments. We wanted a second opinion on the structure of the offer and also identify the gaps.

Step 1: First Diagnostic

The Starting Point: A Complex Matrix

Our initial offer was comprehensive, spanning three core tracks: Skills, Use Cases, and Technology. Each had multiple products and tiers, from Free to Corporate. The intent was there, but the execution was a classic “expert’s curse.” We had built something only we could fully understand.

The Analytical Lens: Applying “Value Engineering”

We asked a reasoning model to analyze our offer, with a simple prompt:

You are an expert in B2B Services offering. Analyze our offer. Check that all upsell make sense, between ranges and between offers. [Offer]

Based on this simple prompt, the reasoning model was able to infere the critical questions to answer:

• Value Coherence: Does the price jump between tiers logically match the value jump in features?
• Upsell Logic: Is there a clear, compelling reason for a customer to move from one tier to the next?
• Cross-Track Alignment: Do our specialized tracks (Core, Processes, Eco-system) justify their premium with distinctly superior value?
• Clarity: Is the offer easy to understand and easy to sell?

The “Aha!” Moment: Our initial audit revealed significant noise. The AI found value gaps where price increases were not matched by transformative features. We found logical fallacies where “unlimited users” was the sole justification for a massive enterprise price tag.

The AI’s conclusion, after a long and detailed diagnosis, was:

Strengths:

• Logical progression in Skills and baseline Technology pricing.
• Clear premium for specialized tracks (Processes > Core).

Weaknesses:

• Use Cases Mid tiers lack value justification.
• Corporate tiers rely on “confidentiality” as a catch-all.
• Technology advantages not articulated → upsell friction.
• Eco-system track is incomplete/unpositioned.

Key Takeaway: The first step to empowerment is honest, unbiased diagnosis. A reasoning model is perfect for this analysis.

Step 2: The Iteration

Armed with insights, we began a process of iterative refinement. In each cycle, the AI provided insights into progress and remaining issues, along the same four axes.

Using a reasoning model had the advantage of performing a global analysis at each step, taking into account the complete offer, analyzing horizontal and vertical up-selling.

Refinement isn’t just about fixing flaws; it’s about reinforcing strengths. We aligned every change with our differentiating factors:

• Dense, Valuable Content: We ensured every training tier (“Skills”) clearly articulated the depth of learning and expert guidance provided.
• Proven Success Stories: We baked the outcomes from our case studies directly into the upsell advantages, promising tangible results, not just features.
• AI Accelerator for Established Companies: This entire process ensured our offer isn’t for startups. It’s a tailored roadmap for companies with legacy systems, complex data, and a need for reliable integration.
• Expert Keynotes: The clarity we achieved in our offer is the same clarity we bring to our keynotes, helping businesses understand the future of AI.

Step 4: Final Check

When the offer looked complete, consistent, and a clear path to delivering value, we did a final analysis with AI. Here is the conclusion summarized:

Key Upsell Pathways

• Skills Track (Vertical Upsell - Tier-to-Tier):

  • Free -> Entry: Offers massive value jump from free videos to guided training (“Expert guidance”).
  • Entry -> Mid-Size: Significant price increase justified by “Improved learning” (implied deeper/more personalized training).
  • Mid-Size -> Corporate: Premium justified by “On-site”, “Confidentiality”, and “Use internal AI”. Target decision-makers needing high security and customization.

• Use Cases Track (Vertical Upsell - Tier-to-Tier): […Similar Analysis…]

• Technology Track (Vertical Upsell - Tier-to-Tier): […Similar Analysis…]

• Horizontal Upsell (Offer-to-Offer - Skills -> Use Cases -> Technology):

  • Skills -> Use Cases: Building capability naturally drives demand for application. “Now that your team is trained on AI, leverage our Use Cases track to implement solutions specific to your needs.” Price points across tiers suggest joint purchase potential (e.g., Corporate Skills + UC).
  • Use Cases -> Technology: Implementing sophisticated Use Cases (especially Mid/Corporate) often requires full Tech stack deployment. “To run those custom AI Agents (UC Contributor/Corporate), you’ll need the dedicated MCP integration from our Ecosystem Technology tier.” Process UC Mid/Corporate logically leads to Process Tech.

• Core -> Processes -> Ecosystem (Technology Specific): Logical progression of complexity and scope:

  • Core Tech (AIM V2) enables simple AI tasks.
  • Process Tech (n8n) automates workflows; likely relies on Core Tech running models.
  • Ecosystem Tech (MCP) integrates enterprise systems; essential for automating complex Processes or running AI Agents against core data. This is the strongest horizontal tech upsell.

• Technology Tier-Specific Upsell Triggers

  • Core AI -> Process AI: Triggered when customer needs automation beyond single tasks (“Increase productivity of process execution”).
  • Core/Process AI -> Ecosystem AI: Triggered when customer needs integration with core backend systems (ERP, CRM, WMS, etc.) or is developing complex AI Agents. MCP becomes essential.
  • Single Deployment -> High Availability: Triggered as usage becomes critical to operations. “Uninterrupted usage” becomes a priority. This is a key upsell within each tech tier as the client’s reliance grows.

• Smart Upsell Leverage Points

  • Co-Creation in Use Cases (Mid Tier): Encouraging clients to submit Use Cases (2/month) builds investment and creates a natural justification for needing more powerful Technology tiers to implement them fully.
  • Progressive Capability: Lower tiers (especially Skills Entry/Mid & Use Cases Entry) function as lower-cost proofs of concept and enable skills transfer, making the high-cost Technology tiers feel less risky and more necessary.
  • Premium Drivers: Consistently leverage high-value triggers at higher tiers: Confidentiality, Control (On-site/System Isolation), Scalability (Unlimited Users/Participants), Robustness (HA/Redundancy), Customization (Internal AI, MCP), and Integration.

• Potential Gaps & Considerations

  • Ecosystem Skills/UC Adoption: Are there enough clear, lower-cost paths building towards the complex Ecosystem capabilities? Are Core/Process customers easily identifiable as ready for Ecosystem up-sell?
  • Horizontal Path Clarity: While Core -> Process -> Ecosystem is logical, ensure marketing/sales messaging makes this progression clear. Document customer journey maps.
  • Upsell Trigger Identification: Need systems to identify when a customer on a lower tier/track (e.g., heavy Process UC Contributor) is demonstrating behaviour (data volume, complexity, integration need) that flags readiness for an Ecosystem Tech upsell.

• Conclusion

  • Your offer has well-defined, logical upsell paths both vertically (tiers) and horizontally (tracks), leveraging increasing levels of complexity, value, and customer commitment. The progression from Core to Process to Ecosystem technology, driven by needs for automation and integration, is particularly strong. Skills and Use Cases tiers create valuable entry points and build capability/intent to justify moving to higher tiers and into the Technology stack. Focusing sales and marketing efforts on understanding customer needs along these pathways and leveraging key triggers (Scalability, Integration, HA, Confidentiality) will be crucial for maximizing customer lifetime value. Address the Ecosystem SaaS gap if possible and ensure clear communication of the value progression across the tracks.

Conclusion: Offer Masterfully Engineered

The journey from our initial draft to our refined offer was a transformation in itself. It was a practical application of our own philosophy: that true AI mastery requires a methodical, insightful, and empowering approach.

For System in Motion, this process is never truly finished. It’s a cycle of continuous improvement, much like the AI systems we help implement. It’s how we ensure that when you partner with us, you’re not just buying a service—you’re gaining a clear, trustworthy, and proven path to AI transformation.

Ready to see our offer built for your success?

We are Here to Empower

At System in Motion, we are on a mission to empower as many knowledge workers as possible. To start or continue your GenAI journey.