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Author: Constantine Limberakis

Sales Teams Need Stories They Can Use in Live Conversations

Posted on by Constantine Limberakis

Over the years, I’ve watched B2B technology in Procuremrnt, Finance and Supply Chain evolve through multiple waves of innovation: enterprise software, cloud, data platforms, automation, SaaS, and now AI. Each wave has brought new language, new buying committees, new expectations, and new pressure on sales teams to explain not just what a product does, but why a buyer should believe it will work.

One thing has remained consistent: when a technology is new, complex, or misunderstood, the customer story becomes one of the most valuable tools a sales team can have.

But only if sales believes in it. That is the part many companies miss.

A case study should not be something marketing creates, publishes, and then hopes sales will use. It should be built with sales in mind from the beginning. It should reflect the real questions buyers ask, the objections sales hears in the field, the proof points that move deals forward, and the internal concerns that champions need help addressing.

When salespeople trust a customer story, they use it differently. They do not simply attach it to a follow-up email. They bring it into live conversations. They use it to explain risk, build credibility, challenge hesitation, and help buyers see a path from interest to implementation.

That is when a case study stops being “marketing content.” It becomes sales enablement.

Sales Teams Have to Believe In the Story

A customer story only becomes valuable to sales when salespeople believe it is credible, relevant, and useful.

That belief does not come from a polished PDF alone. It comes from recognizing the real buying conversation inside the story.

Sales teams know when a case study feels too generic. They know when the story has been stripped of the complexity that actually matters to buyers. They also know when the proof is strong enough to help them in a live deal.

The best case studies give salespeople confidence because they answer the questions prospects actually ask:

“Has this worked for a company like ours?”
“What did implementation really involve?”
“How did the customer manage risk?”
“Who needed to approve the decision?”
“What changed after the solution was deployed?”
“What made the customer comfortable moving forward?”

In those moments, sales teams need more than a marketing asset. They need a story they can stand behind.

They need to be able to say, “We saw this same concern with another customer. Here is how they evaluated it. Here is what they needed to see before moving forward. Here is how they handled adoption. Here is what changed after implementation.”

That kind of answer does more than promote the product. It lowers perceived risk.

It shows the buyer that their concerns are not unusual. It demonstrates that the vendor understands the organizational reality of AI adoption. It gives the buyer a path to explain the decision internally.

This is why sales should have a voice in how customer stories are developed. The strongest case studies are shaped not only by marketing priorities, but by the realities of the sales cycle.

What objections keep coming up?
Where do deals stall?
Which stakeholders need more confidence?
What proof points are missing from the conversation?
What stories would help a champion make the case internally?

When those questions shape the customer story, the final asset becomes far more useful. Salespeople are more likely to use it because they see their own conversations reflected in it. And when sales believes in the story, buyers are more likely to believe in it too.

Where Liberis Consulting Fits

At Liberis Consulting, we help B2B technology companies turn customer success into decision-ready narratives.

That perspective comes from years of watching technology markets evolve and seeing how buyers respond when innovation outpaces confidence. Whether the category is SaaS, automation, data, cloud, or AI, the pattern is familiar: companies can often explain what their technology does, but they struggle to prove why buyers should believe it will work in their own environment.

That is where the customer story becomes one of the strongest sales enablement tools a company has.

But it has to be built the right way.

We help companies go beyond surface-level case studies and capture the deeper story behind the win: the business problem, the decision logic, the stakeholder dynamics, the implementation path, the risk considerations, and the outcomes that matter.

For B2B tech today, this work is not just about content creation. It is about building the proof layer around AI that sales teams can trust and buyers can use.

The right customer story should support marketing, but it should not live only in marketing. It should help sales teams handle objections, build credibility, equip champions, and move buyers from interest to action.

Because the best case studies are not just stories of customer success. They are tools of buyer confidence. And when sales believes in them, they become one of the most powerful assets a technology company can put into the market.


 Learn more at Liberis Consulting.



The Most Undervalued Asset in Selling AI: Getting the Customer Story Right

Posted on by Constantine Limberakis

In the race to adopt AI, most organizations are still focused on capability: What can the tool do? How advanced is the model? How does it compare to competitors? But that’s not where decisions are breaking down. They’re breaking down in the gap between what a solution can do and what a buyer believes it will achieve in their own organization. And that gap is increasingly being filled—or left unfilled—by one thing: the quality of the customer story.

The Market Is Telling Us What It Needs

To illustrate this point, Keelvar’s Annual Procurement Report asked what would help organizations adopt AI. The top answer wasn’t better functionality. It was real-world use cases and case studies (54%). This outpaced pricing clarity, pilots, and training. That’s a clear signal: the issue isn’t access to AI—it’s confidence in how it will perform in a real environment.

In previous technology cycles, case studies were important. In AI, they seem even more essential. Because AI introduces uncertainty that traditional software didn’t:

• Execution risk with AI — success depends on adoption, not just deployment
Unlike traditional software, AI often alters how decisions are made, not just how tasks are executed. That introduces confusion: users must recalibrate judgment, relinquish some control, or learn to supervise probabilistic outputs. Execution risk, therefore, sits less in infrastructure and more in change management—training, UX design, feedback loops, and incentives. The real question isn’t “does the model work?” but “can people rely on it when it matters?”

• Governance risk with AI — decisions must be explainable and auditable
AI systems, particularly those based on complex models, introduce opacity into decision-making processes that were previously deterministic or rule-based. In regulated or high-stakes environments, this creates a governance gap: organizations must be able to justify outcomes to auditors, regulators, and internal risk functions. It’s not enough for a model to be accurate; its outputs must be traceable, its behavior must be able to be monitored, and its failure modes understood. This shifts requirements toward model interpretability, logging, version control, and human-in-the-loop oversight. Governance becomes a product feature, not a compliance afterthought.

• Alignment risk with AI — multiple stakeholders must agree and trust outcomes
AI introduces a new layer of stakeholder complexity because it sits at the intersection of technical, operational, and ethical domains. Data scientists, domain experts, executives, legal teams, and end users all evaluate the system through different lenses. Alignment risk emerges when these groups don’t share a common understanding of what the model is optimizing for or how its outputs should be used. For example, a model optimized for efficiency may conflict with organizational risk tolerance or stakeholder considerations. Successful AI adoption requires explicit alignment on objectives, thresholds for trust, and escalation paths when outputs are contested.

• Outcome ambiguity with AI — results are less predictable and harder to define upfront
AI systems are inherently probabilistic and context-sensitive, which makes upfront outcome definition more ambiguous than in traditional software projects. Unlike deterministic systems with clear specifications, AI performance evolves with good data, usage patterns, and model iteration. This complicates ROI modeling and success criteria: improvements may be incremental, non-linear, or uneven across use cases. As a result, organizations need to shift from fixed KPIs to adaptive evaluation frameworks—continuous benchmarking, A/B testing, and iterative deployment strategies. The emphasis moves from “guaranteed outcomes” to “managed upside with controlled downside.”

The Gap: Most AI-Based Case Studies Don’t Help Buyers Decide

Despite their importance, most AI-focused case studies we see in our work at Liberis Consulting still fall short. They typically miss in four areas:

• Highlight features instead of decisions: In B2B tech, this is a classic positioning error. Buyers aren’t purchasing AI features in isolation—they’re underwriting a decision with organizational consequences (budget allocation, vendor risk, implementation burden). Feature-centric messaging abstracts away the why now and why this tradeoff that senior stakeholders actually care about. It also commoditizes the offering, because competitors can often claim similar capabilities. Effective B2B narratives instead anchor on the decision logic: what problem was prioritized, what alternatives were rejected, and what criteria justified the final choice. This reframes the product from a toolkit into a strategic lever.

• Present outcomes without explaining how they were achieved: Outcome-led messaging (“reduced costs by 30% with AI”, “increased procurement efficiency with AI”) is necessary but insufficient in B2B contexts. Sophisticated buyers immediately interrogate causality: what changed operationally to produce that result? Without a credible mechanism, outcomes read as marketing inflation. In technical sales cycles—especially with procurement, IT, and finance involved—buyers need to map results to implementation reality: data flows, process changes, integration points, and required capabilities. Omitting the “how” creates skepticism and elongates the sales cycle because the buyer must reverse-engineer feasibility themselves.

• Ignore internal friction and stakeholder dynamics: Just like any technology engagement, AI purchases are rarely linear; they’re negotiated across multiple stakeholders with competing incentives (e.g., IT prioritizing security, finance focusing on cost, department functions like procurement on usability). Ignoring this complexity produces unrealistic case studies and weakens credibility. Buyers want to see that a vendor understands organizational resistance, change management, and cross-functional alignment. Addressing friction explicitly—how objections were handled, how adoption was driven, how internal champions succeeded—signals maturity and reduces perceived implementation risk. In many deals, this is more persuasive than the product itself.

• Focus on early success without addressing what comes next: Initial wins (pilot success, early ROI) are only the first checkpoint in a longer value lifecycle. B2B buyers are increasingly evaluated on durability: scalability, long-term ROI, and adaptability as requirements evolve with AI strategies. Over-emphasizing early success suggests a lack of roadmap or post-implementation strategy. Strong narratives extend beyond “time-to-value” into “time-to-scale”—what happens after deployment, how the solution expands across teams, and how it compounds value over time. This is especially critical in SaaS, where retention and expansion drive actual economic outcomes.

They answer: “What happened?” But not: “How did this actually work—and can I replicate it now and in the future?” That’s the question buyers need answered.

Where Liberis Consulting Fits

AI adoption isn’t limited by technology. It’s limited by alignment, confidence and clarity. And the fastest way to build all three is through well-constructed customer stories—not as marketing, but as the connection between capability and belief, intent and action, and investment and impact. Organizations that get this right will accelerate adoption. Those that don’t will continue to generate interest—without converting it into decisions.

This is where many AI initiatives break down. Not because the solution doesn’t work—but because the story doesn’t translate effectively. Liberis Consulting works with B2B technology providers to turn customer success into decision-ready narratives by framing the real business problems being solved, capturing how decisions were made and risks addressed, structuring stories that resonate across stakeholders and ultimately turning individual wins into repeatable adoption patterns.

This isn’t about improving AI-enabled marketing babble. It’s about enabling buyers to move past the complexity of AI investments based on real outcome based human stories.

Learn more at Liberis Consulting.




AI in B2B Tech: Why Traditional “Build vs. Buy” Is the Wrong Debate – The ProcureTech Example

Posted on by Constantine Limberakis

At recent KonnectHouse Agentic AI events in London and New York, a consistent theme emerged: everyone is using and building AI, but few are clear on what actually differentiates it. The familiar “build vs. buy” debate has also resurfaced—now sometimes reframed as “build, buy, or adopt.” But even that evolution misses the point. The real question is no longer where AI comes from. It’s what solution or set of solutions will procurement use to govern it.

Access to AI Is Becoming Standardized

Access to AI capabilities is becoming standardized across the market. Whether embedded in Coupa, SAP, Ivalua, or deployed through established best of breed, or nascent AI-first Procuretech, most solutions are leveraging the same underlying models.

The artificial intelligence layer itself is no longer a durable source of differentiation—it is rapidly becoming table stakes. What is not standardized is how AI is applied “within the enterprise”—how outputs are made relevant, embedded into workflows, and translated into consistent, coordinated actions.

At the KonnectHouse event in NYC, solution differentiation using AI took on many forms. Intake and orchestration platforms positioned themselves as the new enterprise coordination layers. Domain-specific providers emphasized their use of AI to transform specific functions with functional depth in areas like sourcing and contract management. Large suites leaned in on scale, system-of-record advantage while boasting the acquisition of smaller AI players into their existing suites.

Different narratives—but all had the same dependency on a shared or similar AI infrastructure that is rapidly evolving.

The Build vs. Buy Debate Is Being Replaced by a New Reality

Given the pace of innovation, procurement teams are no longer simply choosing between suite-based solutions and best-of-breed vendors. They are increasingly building their own micro applications within AI platforms—using natural language to generate, refine, and debug code—reducing their reliance on traditional coding expertise and IT-led development.

Standalone LLMs—such as those offered by Anthropic, OpenAI, Meta, and Google—are now widely accessible and increasingly capable. They can already without much procurement context, analyze spend, generate sourcing events, and recommend suppliers with minimal setup.

This has reduced the barrier by potentially empowering procurement teams to create their own functional AI-powered intelligence and workflows in days—not months. In some cases, procurement teams are already building intake flows, supplier evaluation agents, and sourcing support tools internally. This changes the dynamic entirely and this is where the real shift is happening.

The Pressure on Traditional “Procurement” Differentiation

Moreover, this shift is already creating pressure for both vendors and enterprises. For procurement domain-focused providers, the traditional argument—that specialized tools win because they understand procurement—is weakening. Standalone LLMs are rapidly closing the gap on functional knowledge, making capabilities like contract analysis, sourcing optimization, and spend classification widely accessible.

For enterprises, the economics are shifting as well. When internal teams can replicate meaningful portions of functionality quickly, the value of traditional SaaS models comes under pressure. Functionality is no longer enough. The question becomes: how does a solution fit into—and help govern—a broader system of decisions and actions?

So the risk is no longer a single bad decision. It is the proliferation of many independently built agents managing workflows—each locally optimized, but globally misaligned. This is the emergence of “shadow orchestration.” And unlike shadow spend, it operates at machine speed.

The Real Divide Is Just Starting to Emerge

The conversations at KonnectHouse made one thing clear: the market is converging faster than it appears. The distinction between AI-first startups and established suites is narrowing. The difference between building, buying, or adopting is becoming less meaningful as capabilities standardize.

The winning solutions will not simply embed AI. They will define a role within a broader system—owning critical workflows, acting as orchestration layers, or embedding directly into enterprise decision-making in a way that ensures consistency and control. In a more autonomous environment, alignment—not capability alone —will define differentiation.

Where Liberis Is Focused

In a world where discussions around AI are no longer scarce, solution differentiation will not come from AI models and agents alone. This is the shift we are actively helping solution providers navigate at Liberis Consulting. The question we hear most often is simple: “If everyone now has AI, how do we stand out?”

Learn more about how we can help answer this at Liberis Consulting.



From Workflows to Intent: How AI Agents Are Reshaping Procurement Tech

Posted on by Constantine Limberakis

Over the past year, many of the most substantive conversations I’ve had with CPOs, CIOs, and leaders at start-ups and growth-stage technology companies have revolved around a common, often unstated question: What happens to enterprise software when users no longer interact with it the way it was designed to be used?

For procurement leaders, this question surfaces around control, trust, and accountability.
For technology providers, particularly those building and scaling solutions, it surfaces around differentiation, relevance, and long-term value creation.

That convergence is why I view AI agents as one of the most consequential trends in enterprise B2B technology today. Not because of the novelty of large language models, but because of what they represent: a structural shift in how intent, data, and decision-making come together. For procurement and supply chain leaders, this shift is no longer theoretical. For technology providers, it is becoming a defining strategic test.

A Lesson From an Earlier Inflection Point

Early in my career, I remember working with Ariba Operating Resource Management System (ORMS) – (yes this is what it was called 😀 ) at a time when workflow was emerging as a true competitive differentiator. What set it apart was not simply functionality, but visibility. The workflow designer made approval paths tangible — POs, requisitions and any electronic or Eform could be modeled, demonstrated, and understood visually in term of approval state.

At the time, this was a sharp contrast to solutions like SAP SRM, which often struggled to show even basic approval processes in a way business users could clearly grasp. That lack of visibility mattered. Buyers hesitated when they could not see how work actually moved through the system. This workflow visibility combined with reporting became a game changer. It shifted buying decisions because it reduced ambiguity and increased trust. Users did not just assume the system worked—they could see how it worked.

That moment is worth remembering, because the pattern is repeating.

Why This Moment Matters

Enterprise software has historically evolved by layering on more capability — more workflows, more configuration, more dashboards. When adoption lagged, the response was better UI/UX, better design and not a fundamental rethink of interaction of humans and computers.

Generative AI and AI agents change that equation. Instead of learning systems, users increasingly express intent: Where am I exposed to supplier risk? What should I renegotiate next? How do I protect margin without disrupting supply?

What makes this shift unavoidable is convergence. Mature LLMs, accessible agent frameworks, enterprise-grade security tooling, and years of accumulated structured and unstructured data have all matured at the same time. The result is a new interaction model that feels as significant as the move from command lines to graphical interfaces—and arguably more disruptive.

From User Experience To Outcome Experience

Traditional procurement platforms were designed around processes. Users navigated workflows, followed steps, and consumed outputs through reports and dashboards.

Agent-driven interaction reverses that logic. The user starts with the outcome. The agent interprets intent, reasons across structured and unstructured data, invokes workflows across systems, and returns a recommendation—or executes it.

For CPOs, this lowers friction but raises stakes. Decisions happen faster, but they also risk becoming opaque if not governed correctly. Just as workflow visibility once built trust, explainability will now define it.

Four Questions Every CPO Should Be Asking Now

As this shift accelerates, CPOs should anchor their technology strategy around four critical questions:

1. Where does decision authority sit—human or agent?
 As agents recommend suppliers, flag risks, or trigger actions, CPOs must define where automation is acceptable and where human oversight is mandatory. This is not a configuration issue; it is a governance decision.

2. Can the system explain its recommendations in business terms?
 Trust will determine adoption. If an agent cannot clearly articulate why a recommendation was made—what data it used, what assumptions it applied, and what trade-offs it considered—CPOs will hesitate to rely on it for material decisions.

3. How effectively does the platform reason across fragmented data?
 Procurement decisions increasingly depend on unstructured inputs—contracts, supplier communications, market intelligence, ESG disclosures. Platforms optimized only for structured ERP or S2P Suite data will struggle as agents become the primary interface.

4. What happens to the procurement operating model?
 As agents automate analysis and execution, procurement roles shift toward exception management, supplier strategy, and value orchestration. Skills, roles, and accountability models must evolve accordingly.

What This Shift Means For Technology Providers

The Ariba ORMS example is instructive for today’s technology providers. At that time, workflow visibility—not just workflow capability—became the differentiator. Today, AI agents face a similar test. The foundational components of agent-based systems are rapidly commoditizing. LLMs, orchestration frameworks, vector databases, and enterprise AI tooling are broadly accessible.

Differentiation will not come from having an agent, but from what sits beneath it:

  • Embedded domain intelligence, not generic automation
  • Decision governance and explainability, not black-box outputs
  • Outcome reliability, not surface-level AI features

Providers that cannot clearly show how decisions are made will face the same skepticism once directed at opaque workflow engines.

Suites Vs. Best of Breed in an Agent-Driven World

The long-standing suite versus best-of-breed debate does not disappear — it evolves.

Suites benefit from unified data models and end-to-end process visibility, enabling agents to reason across sourcing, contracting, planning, and execution. This supports broader orchestration but may dilute depth.

Best-of-breed solutions retain an advantage in specialization and analytical rigor. However, without a compelling agent narrative, they risk becoming invisible components under a higher-level orchestration layer.

For CPOs, the right question is no longer “Which solution is better?” It’s “Which ecosystem enables agents to deliver trusted, explainable outcomes across the wider source to pay value chain?“

Will The Prompt Be the Differentiator?

Prompts and prompt design will become the dominant interaction surface—but they will not be the competitive advantage.

Two platforms can receive the same prompt and deliver very different outcomes based on data architecture, reasoning logic, governance rules, and embedded expertise. So as interfaces fade, decision quality becomes the new battleground.

  • For CPOs, the mandate is clear: demand transparency, accountability, and control.
  • For technology providers, the challenge is sharper: prove that your platform still matters when the screen no longer does.

The interface may disappear. Strategic relevance cannot.

How Liberis Consulting Helps Navigate This Shift

For start-ups and growth-stage technology providers, the move toward agent-based interaction creates both opportunity and confusion. The market is crowded with vendors leveraging similar AI tooling, making differentiation increasingly difficult. Liberis Consulting works with technology companies that recognize this moment as a strategic inflection point. We help providers:

  • Define where intelligence truly lives within their platform
  • Design agent strategies that prioritize explainability, trust, and control
  • Align product direction with how CPOs evaluate value and risk
  • Position effectively against suites and adjacent competitors
  • Translate technical capability into clear, credible market narratives

Just as workflow once separated leaders from laggards, agent intelligence will now determine who shapes the next generation of procurement technology. Liberis Consulting helps ensure your platform is on the right side of that divide.

 Learn more at  Liberis Consulting.



After the Lights Fade: Reflections from DPW Amsterdam 2025

Posted on by Constantine Limberakis

Before the buzz of DPW Amsterdam began, I had the privilege of spending a quiet day ahead of the conference at the Rijksmuseum—standing before masterpieces that have endured for centuries. Works by Rembrandt, Vermeer, and Hals—each brushstroke preserved through wars, revolutions, and generations—remind us that true craftsmanship withstands the tests of time.

It was a striking contrast to what awaited later that week: the fast-moving, high-decibel world of procurement and supply chain technology, where innovation cycles turn in months and yesterday’s breakthrough can quickly become tomorrow’s memory. At the Rijksmuseum, permanence is celebrated; at DPW, impermanence is reality. The question that lingered as I left the museum was the same one that echoed through the conference halls: what in this rapidly shifting landscape will truly endure—and what will fade just as quickly as it arrived?

Another DPW Amsterdam has come and gone. The music, lights, and unmistakable energy of the world’s most forward-leaning procurement and supply chain conference still echo a week later—but so do the questions. Beneath the spectacle lies a deeper conversation about what’s next: which technologies, companies, and ideas will truly shape the year ahead—and which might quietly fade before we meet again next fall.

This year’s event was bigger, louder, and more crowded with vendors than ever before. The exhibition floor reflected the growing gravitational pull of procurement and supply chain tech as a magnet for innovation. Yet amid the booth activations and slick demos, what stood out most was the rise of start-ups—scrappy, ambitious teams determined to define the next wave of digital transformation. They represent the foundation of future innovation but also its inherent volatility.

Practical Founders (2025) estimated that for every 100 software or SaaS startups, roughly 10% advance past each meaningful growth stage, suggesting that only about 1 in 10 ever reach sustainable revenue or multi-year viability. That sobering reality hung in the air as founders pitched, networked, and hustled to make an impression in a crowded market.

Adding to the complexity, the majority of startups at DPW were European—many of whom operate in a funding environment that is fundamentally different from the U.S. landscape. According to Forbes, less than 1% of small businesses in the United States receive funding from venture capital firms, while roughly 26% of European startup founders rely on VC backing. That dependency shapes both the optimism and the urgency felt at events like DPW, where visibility, investor access, and early traction can make or break a company’s runway.


Hype Meets Reality

The questions being asked at DPW were not new—but they carried a sharper edge this year.

When does hype become reality? Which vendors will still be here next year? And how many will quietly disappear—whether through cost-cutting, acquisition, or the simple math of survival in an over-invested market?

The AI narrative, unsurprisingly, dominated discussions. GenAI and the emerging concept of Agentic AI were front and center, but so were the anxieties they’ve unleashed. Everyone—from procurement leaders to startup founders—seemed to be asking the same thing: How do I adopt AI without overreaching? How do I move fast enough to stay competitive, but not so fast that I trip over my own ambition?

This tension—between speed and readiness—was everywhere. Many attendees are still in early pilot stages, experimenting with generative insights, contract summarization, and predictive decisioning. But scaling those capabilities into enterprise-grade reliability remains an unsolved puzzle. The mood, while positive, was not euphoric—it was cautiously optimistic. The crowd seemed more grounded, more focused on execution than excitement.

Standing Out in a Sea of Start-Ups

For the startups exhibiting, another question loomed large: How do you get noticed? With dozens of new vendors competing for the same thirty seconds of attention, differentiation has never been harder. A well-designed booth, clever tagline, or flashy AI integration isn’t enough. Buyers, analysts, and investors are looking for something deeper—proof, not promise.

Standing out now means showing evidence of traction, customer references, and credible results. It means having a clear story about where your product fits, not just in a category, but in the day-to-day of procurement and supply chain professionals. The vendors who manage to communicate that clarity—who demonstrate trustworthiness and commercial readiness—will be the ones still standing when the noise dies down.

Looking Toward 2026

As we head toward the close of Q4 2025, the big question remains: what will DPW 2026 look like?
Who will still be there—and who will not? Some of this attrition will be natural—acquisitions, pivots, market corrections—but some will reflect the deeper truth of our industry: that even great technology is no guarantee of survival.

The next year will likely bring consolidation, sharper messaging, and a renewed focus on ROI. But it will also bring opportunities for those who can bridge the gap between AI potential and practical impact. The ones who balance ambition with readiness—who engage the market early but execute with discipline—will emerge stronger.

Key Takeaways from the Startup Genome Project

If DPW is any indication, 2025 will continue to test how resilient these emerging firms truly are. The Startup Genome Project offers several insights that resonate deeply with what was on display in Amsterdam:

  • Validation takes longer than expected. Early-stage startups need to spend up to 3x longer validating their target markets than founders anticipate—one of the main reasons cash flow and focus are so critical in the first 18 months.
  • Ideas are often overvalued. Founders tend to overestimate the worth of their intellectual property by up to 255%, highlighting the gap between vision and market reality.
  • Pivoting can be a strength. Startups that pivot once or twice during early growth can see 3.6x faster user growth and 2.5x higher returns than those that don’t pivot—or pivot too often.
  • Survival improves with stage. Generally, the failure rate decreases with each round of funding, underscoring why deliberate market engagement and credible traction matter more than speed alone.

These findings echo a common truth seen across DPW: success rarely comes from technology alone—it comes from focus, clarity, and adaptability.

That’s also where Liberis Consulting helps startups chart a sustainable path forward. By guiding early-stage B2B technology providers in defining their market position, sharpening their go-to-market strategy, and engaging analysts and investors with credibility, Liberis helps founders reduce the guesswork between product promise and market proof. In a world where hype cycles move faster than business models, disciplined positioning and execution are the new differentiators.

Just as the art in the Rijksmuseum endures because it was built with intent, depth, and mastery, the technologies that will last in procurement and supply chain will be those grounded in purpose and precision—not just noise and novelty.

For now, the glitz of Amsterdam has given way to the grind of Q4. The questions asked on the DPW stage will be answered not by next year’s buzzwords, but by how companies act in the coming months—how they prioritize, execute, and deliver real value when the spotlights turn off.

About the Author:
Constantine Limberakis is the Founder of Liberis Consulting LLC, a boutique consultancy focused on helping B2B technology providers in procurement and supply chain define market strategy, sharpen positioning, and accelerate growth.

Strong Tech, Slow Growth: Closing the Messaging Gap in the Age of Rapid Tech Cycles

Posted on by Constantine Limberakis

Technology cycles have been collapsing for years — and the impact of AI has only accelerated that pace. Companies aren’t just delivering more value; they’re delivering it faster. Lower barriers to entry let new players target niche use cases, scale quickly, and then expand into broader categories to challenge incumbents.

Take Treefera. They started with first-mile supply chain transparency, then rapidly expanded into commodity tracking, geospatial analytics, and compliance automation — quickly threatening established providers reliant on lagging, self-reported data. Their speed highlights a broader truth: innovation now moves faster than most markets can absorb.

And that’s where messaging becomes critical. In this era of compressed technology adoption, a strong product isn’t enough. If your story doesn’t evolve with the value you’re delivering — and at the speed you’re delivering it — buyers won’t keep up. Growth stalls, competitors gain ground, and the messaging that carried you to $20M won’t get you to $50M and beyond.

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Messaging In Motion

In past cycles, companies could ride a stable message for years. Early adopters tolerated rough edges, gave feedback generously, and allowed the story to evolve while the product matured.  Today, that luxury is virtually gone.

  • Markets shift fast
    New regulations, economic shocks, or disruptive competitors can reset priorities almost overnight. What mattered in Q1 may be irrelevant by Q3 — and messaging that doesn’t keep up risks sounding tone-deaf.

  • Products evolve faster than stories
    Feature velocity has outpaced narrative velocity. Engineering keeps shipping, but the story stays frozen. That gap creates confusion: innovation is invisible, misunderstood, or dismissed.

  • Buyers arrive more skeptical
    Committees are bigger, better informed, and less forgiving. Thanks to AI, competitive comparisons and peer reviews are just a click away. By the time your team connects, buyers expect clarity, differentiation, and proof — not promises.

The result is messaging debt. Like technical debt, it builds quietly, slows everything down, and gets harder to fix the longer it lingers — leaving an open lane for competitors to move in.

The Symptoms of Stale Messaging

Here’s the thing: stale messaging doesn’t announce itself. It creeps in, showing up as small cracks that widen over time — until growth erodes and eventually flatlines.

What does that look like?

  • Deals drag. Win rates slow, sales cycles stretch, and “no decision” quietly becomes your biggest competitor. Buyers don’t say your product isn’t valuable — they just can’t connect your story to a problem urgent enough to solve right now. What should feel like momentum instead feels like friction.

  • The story fractures. Sales, marketing, and product each put their own spin on the pitch. The founder’s sharp, memorable narrative gets watered down and reinterpreted until every team is telling a slightly different story. The result? Confusion in the market and wasted energy internally.

  • Competitors steal your edge. Rivals borrow your language, dress it up as their own, and erase the differentiation you once owned. What felt bold when you launched now sounds like table stakes. Without a clear, distinct message, you’re forced into price wars or feature fights instead of winning on value.

  • Innovation goes unseen. Your product keeps evolving, but the market still sees you through yesterday’s lens. New capabilities never get credit because the story hasn’t caught up. The gap between what you’ve built and what buyers perceive only widens, leaving revenue on the table.

These cracks may start small, but left unchecked, they compound. Deals stall, teams misalign, competitors catch up, and even your own innovation becomes invisible. Stale messaging isn’t just a marketing problem — it’s a growth problem.

The Four Messaging Gaps

As the Cranberries once asked, “Do you have to let it linger?” Messaging that’s treated as a one-and-done exercise always breaks down. And in our work, we’ve seen those breakdowns consistently fall into four distinct gaps — the most common ways messaging stops connecting.

  • The Clarity Gap
    Stories accumulate complexity over time. Features pile on, acronyms sneak in, and what was once crisp becomes muddled. Buyers nod politely but can’t repeat your value back in their own words. The moment champions can’t pitch you internally, momentum stalls — not because your product lacks value, but because the message is too tangled to travel.

  • The Relevance Gap
    Markets don’t sit still. New regulations, shifting budgets, or trends like AI adoption change what matters to buyers almost overnight. A message that once hit the mark now feels out of step. When your story doesn’t map to current priorities, you sound dated — and buyers tune out, even if your product is a perfect fit.

  • The Differentiation Gap
    Competitors are listening, too. They borrow your language, dress it up, and erase the edge you once had. What was bold now feels generic. Without a distinct narrative, you’re left competing on price, incremental features, or discounts — a race to the bottom rather than a story that commands value.

  • The Adoption Gap
    Even the sharpest messaging falls apart if it isn’t used consistently. Sales improvises, marketing spins their own angle, product talks features in isolation. The founder’s pitch gets bent out of shape until every function is telling a different story. Misalignment erodes trust with buyers and drains energy internally.

When these gaps start to show, they rarely fix themselves — they widen. Left unchecked, they slow growth, sap alignment, and hand competitors the advantage. Closing them requires a deliberate refresh of your story so it’s clear, relevant, differentiated, and consistently told.

How Liberis Approaches a Messaging Refresh

At Liberis Consulting, we believe messaging isn’t copywriting. It’s strategy. It’s the connective tissue between your product, your market, and your revenue growth.

Our approach is built on four core principles:

  • Outside-In First
    Messaging starts with the buyer’s reality, not internal assumptions. We ground everything in customer language, competitive context, and market priorities.

  • Collaborative Alignment
    Messaging only works if sales, marketing, product, and leadership share it. We bring cross-functional teams together to co-own the story.

  • Iterative Validation
    Messaging isn’t a one-and-done exercise. It has to be tested in the field, tuned to buyer reactions, and reinforced over time.tory.

  • Strategic Asset
    Strong messaging isn’t just a pitch deck. It’s a durable, repeatable narrative that unlocks velocity across the entire GTM engine.

Want To Go Deeper? 

We’ve published a playbook on sharpening your messaging – Sharpen Messaging to Improve Sales Outcomes – showing exactly how to connect your value to buyers. Built for B2B product marketers, it’s a step-by-step resource with ready-to-use templates to help you craft an outside-in messaging strategy that resonates.

Need Expert Guidance?

At Liberis Consulting, we’ve helped B2B teams sharpen their message to accelerate growth, align teams, and compete with confidence.  When your story starts to slip, we help you refresh it.  Contact us at Liberis Consulting and let us help you build a clear and consistent messaging framework that drives results.

Beyond Buzzwords: Why AI-Startups Should Focus on Outcomes, Not Labels

Posted on by Constantine Limberakis

As a participant in KonnectHouse’s Agentic AI in Procurement event at Tobacco Dock, London on September 4, 2025, I found myself immersed in a vibrant ecosystem of forward-thinking procurement professionals and AI innovators. The event delivered a powerful mix of interactive demos, strategic dialogue, and a clear emphasis on real-world impact and a little bit of fear of the unknown..

But one key theme that resonated with me throughout the day: startups shouldn’t get bogged down by the labels—whether “Generative AI” or “Agentic AI.” In truth, these technologies are evolving, often overlapping—and what truly matters is what they accomplish.

1. Labels will Evolve because Technology Evolves

During the demo hub sessions, presenters demonstrated tools that straddled multiple AI paradigms. For instance, one provider offering AI-powered systems was generating RFX content (a hallmark of generative tech), while also autonomously managing supplier negotiations (an agentic capability)—plus everything in between.

What’s clear is that AI solutions are converging. Packaging them under rigid labels can hamper their perception, undervalue hybrid functionalities, and—and most critically—undermine clarity regarding their actual value.

Startups at the event showed why rigid tech labels fail to capture the ingenuity and breadth of modern procurement AI. Each provider demonstrated the challenge of simple categorization—and illustrated the risks of letting outdated labels define a startup’s positioning. For example – 

  • askLio blends generative and autonomous capabilities: it handles free-text requests and guided buying while also deploying intelligent agents for tasks like order confirmation, risk monitoring, and supplier negotiations in real time.

  • Delvo brings adaptive sourcing and compliance bots to the table, scanning market shifts proactively and streamlining RFP cycles. Its platform fuses generative document creation with agent-driven supplier management.

  • Omnea offers a category management suite that adapts to evolving supplier and transaction data, enabling smarter decisions over time. Generative tools power dynamic reporting and analytics, translating complex insights into accessible, actionable outputs.

  • Sligo connects spend analysis, contract reviews, and supplier performance scoring in a single workflow—pairing generative documentation insights with AI-augmented sourcing routines that evolve with usage, without locking into rigid categories.

2. Stakeholders Want Results, Not Definitions

Procurement leaders attending the event also weren’t asking whether a system was necessarily “agentic” or “generative”—they wanted to know: Can it draft an RFP? Flag risk proactively? Save us time and money while ensuring compliance? The key is to demonstrate outcomes—autonomous sourcing, decision orchestration, proactive risk identification—that define Agentic AI’s real value.

3. Hybrid Approaches Are Practical—and Powerful

Some innovative startups are even opting for hybrid solutions, blending generative AI’s language fluency with agentic AI’s autonomy and decision-making. This pragmatic approach allows for more robust feature sets—initial content generation, intelligent evaluation, strategic planning, and downstream execution—all embedded in one workflow.

These hybrids let startups offer “the best of both worlds,” enabling procurement functions to quickly adopt tools that generate, reason, and act—without forcing customers to choose a specific AI label.

4. Messaging Over Monikers: Clear Communication Wins

One challenge discussed at the event was AI buzzwords fatigue. If every solution is tagged “AI,” the term loses meaning. I observed how clarity in messaging—focusing on the problem solved or the benefit delivered—had more impact than catchy labels.

As a startup, say: “Our solution auto-generates and evaluates supplier responses, highlights non-compliance, and manages approvals in real-time.” That’s far more compelling than, “We’re a generative/agentic AI platform.”

5. The Event’s Takeaways: Focus, Practicality, and Impact

Here are the key takeaways I carried from the conference:

  • Outcome > Terminology: Design and present solutions based on what they achieve, not on AI categorizations that get caught in marketing babble.

  • Be transparent and direct: Clarify what’s automated, what’s autonomous, and what’s still overseen by the human team.

  • Start small, deliver value: Choose use cases where impact is immediate—like risk flagging or contract drafting—and scale from there.

  • Hybrid solutioning is your ally: Mix and match AI techniques to build richer, more adaptable solutions for procurement needs.

Conclusion: The Future Is Defined by Utility

Events like KonnectHouse’s Agentic AI in Procurement are invaluable for one key reason—they ground lofty AI concepts in tangible procurement realities. But they also remind us that while labels like “Generative” or “Agentic” may hold technical value, they take a backseat to impact.

To startups navigating this evolving space: don’t get bogged down by definitions. Concentrate on business outcomes that focus on clarity, efficiency, savings, risk mitigation, and strategic insight. Let your messaging reflect what you deliver, not what buzzword you align with. That’s how you turn attention into adoption.

This is where Liberis Consulting can help.

Positioning That Wins: Standing Out in Crowded Markets

Posted on by Constantine Limberakis

Why clarity beats complexity in hyper-competitive markets

Launching a new product in today’s hyper-competitive B2B tech landscape isn’t just about having the best features—it’s about owning a clear, defensible place in the market.

At Liberis Consulting, we’ve seen this challenge repeatedly: Buyers are overwhelmed, competitors are loud, and your product risks getting lost in the noise. A strong product alone doesn’t close deals; clear positioning that highlights your differentiation does. 

[Want to skip all this and go straight to our Playbook? Click here]

The Challenge:  Capturing Mindshare

In B2B tech, any product (whether existing or net-new) isn’t just about winning deals—it’s about breaking through the noise and earning attention. Buyers today are overwhelmed with options, competitors are loud, and incumbents already dominate buyer mindshare. From the vendor’s perspective, several challenges make this even harder:

  • Overcrowded Marketplaces
    Buyers are bombarded with solutions that sound alike. Without a clear, differentiated story, your product risks blending into the noise.

  • Entrenched Competitors
    Established players often hold the advantage with brand recognition, existing customer bases, and proven credibility. Overcoming that bias is an uphill battle.

  • Feature Parity Across Solutions
    In crowded categories, most products offer similar capabilities. When everyone claims the same benefits, it becomes harder to stand out without a compelling narrative.

  • Skeptical, Distracted Buyers
    Decision-makers are juggling multiple priorities and have limited time. They gravitate toward known brands or default to “do nothing” if your value isn’t immediately obvious.

  • Fast-Moving Competitive Dynamics
    New entrants, shifting buyer needs, and evolving alternatives mean your product positioning can become outdated quickly if not carefully managed.

The stakes are high: if buyers can’t quickly answer “Why you?”, your product won’t even make the shortlist.

That’s why the job of positioning isn’t just to describe what your product does—it’s to own a unique and valuable space in your buyer’s mind, making your differentiated value clear against competing alternatives.

Why Positioning Matters More Than Ever

In hyper-competitive markets, where products compete for the same limited attention, positioning is what separates products that win from products that get lost. Buyers move fast, alternatives are everywhere, and competitors are louder than ever. Clear positioning gives you the advantage by:

  • Creating Instant Clarity for Buyers
    When your narrative is sharp and differentiated, buyers immediately understand who you are, what you do, and why it matters—helping you break through the noise.

  • Highlighting the Unique Value Only You Deliver
    Positioning makes it clear why your product is different and why that difference matters, shifting the conversation away from features and toward outcomes that resonate.

  • Aligning Your Teams Around One Story
    Great positioning doesn’t just live in marketing decks. It equips sales, product, and marketing teams to speak with one voice, ensuring buyers hear a consistent, compelling message at every touchpoint.

  • Equipping You to Compete with Confidence
    With a clear, defensible position, you can take control of the narrative rather than reacting to competitors. It becomes the foundation for your GTM strategy and accelerates growth.

In crowded markets, clarity isn’t optional—it’s your competitive weapon. Without it, even the best products risk blending into the background, no matter how powerful their capabilities.

How Liberis Approaches Positioning

At Liberis Consulting, we take a pragmatic, buyer-driven approach to positioning. We start by aligning on buyer needs and priorities, understanding all competitive alternatives, identifying unique product capabilities, and connecting those differentiators back to the customers who derive the greatest value.

But positioning work is never one-size-fits-all. When launching a net-new product into a hyper-competitive market—where buyers are already flooded with solutions—you need a more competitive-first strategy. In these situations, we focus on positioning directly against what buyers already know:  

  • Anchor Positioning to the Competitive Landscape
    In crowded markets, buyers need context fast. We position your product relative to known competitors so buyers immediately understand where you fit, where you stand out, and why you matter.

  • Map Competitor Strengths and Weaknesses
    We go beyond surface-level comparisons to analyze where competitors excel, where they fall short, and where unmet buyer needs exist. This allows us to pinpoint opportunity gaps your product can uniquely fill.

  • Draw Out Meaningful Differentiation
    Features alone rarely win in hyper-competitive markets. We uncover the capabilities and attributes that set your product apart—not just what’s different, but what’s most valuable to buyers in ways competitors can’t claim.

  • Craft a Buyer-Centric Positioning Narrative
    Finally, we turn these insights into a clear, compelling story that connects directly to buyer priorities and pain points. The goal: make it instantly obvious why your product matters and why it’s the better choice versus existing alternatives.

The result? A clear, defensible positioning strategy that resonates with buyers and aligns your product, marketing, and sales teams around a single, consistent narrative that drives growth.

Want To Go Deeper? 

If you’re ready for the how, our Positioning New Products in Hyper-Competitive Markets Playbook shows you exactly how to carve out a winning market position.

Designed for B2B product marketers, founders, and GTM leaders, it’s a step-by-step resource packed with ready-to-use templates that is built from the proven approach we use at Liberis Consulting to help clients stand out in crowded categories. 

Need Expert Guidance?

When you need more than a DIY approach, we’re here to help you uncover your unique value, align your teams, and position your product to win.  At Liberis Consulting, we partner with founders and go-to-market leaders to define clear, defensible positioning that drives differentiation and accelerates growth.

Navigating the Agentic AI Revolution: The Future of Procurement & Supply Chain Management

Posted on by Constantine Limberakis

In a few weeks I will be hosting the KonnectHouse Agentic AI in Procurement conference. Focused on the transformative impact of Artificial Intelligence (AI), I anticipate many diverse perspectives will be shared. But I am sure there is one commonality that will be underscored across the board – AI is no longer a futuristic concept but an immediate strategic imperative for businesses aiming to stay competitive and resilient.

The roles that make up procurement and supply chain functions are at a critical inflection point, with organizations increasingly relying on AI to make faster, more strategic sourcing decisions amidst volatile supply chains, rising costs, and talent shortages. While AI’s revolutionary potential is clear, its effective implementation requires a strategic, yet cautious enterprise-wide lens.

The Rise of Agentic AI: Beyond Automation

A central theme of Agentic AI is that it represents a major step forward from traditional generative AI. According to Deloitte’s 2025 Global CPO Survey, 94% of procurement executives now use generative AI at least weekly—a sharp rise from previous years. Yet, while generative AI adoption is becoming mainstream, Agentic AI is only beginning to emerge and demands a more integrated approach.

Generative AI is largely reactive—producing text, images, or other outputs in response to a prompt. Agentic AI, by contrast, is proactive and autonomous. These systems perceive their environment, make decisions, take actions, and pursue goals without constant human input. This shift moves AI beyond task automation toward autonomous decision-making and scaled action—a transformation with profound implications for procurement and supply chain operations.

Key Applications and Benefits in Procurement & Supply Chain

The potential applications of Agentic AI across procurement and supply chain are vast, promising to address long-standing challenges by analyzing large volumes of historical and real-time data to produce actionable insights. From my research, insights and pure curiosity on the topic, here are some key use cases that I am hearing from procurement and supply chain management leaders:

  • Demand Forecasting: AI processes historical sales data, seasonality, promotions, and economic indicators to produce accurate forecasts, helping businesses anticipate market trends, optimize inventory, and allocate resources.
  • Automated Sourcing & Negotiation: AI can semi-automate sourcing events, analyze RFP responses, and even conduct AI-based supplier negotiations, particularly for long-tail suppliers, freeing up human teams for more strategic tasks.
  • Procurement Orchestration & Intake: AI-powered tools can serve as a “front door” for internal requests, translating needs into categorized choices and guiding users through appropriate approval channels.
  • Supply Chain Optimization: By analyzing data from traffic conditions to fuel prices, AI can model multiple transportation and scheduling scenarios to reduce costs or shorten lead times.
  • Supplier Risk Assessment: AI evaluates supplier performance using historical data, financial reports, and industry news to detect early signs of risk, enabling proactive strategies and supplier diversification.
  • Sales and Operations Planning (S&OP): By integrating diverse data, AI can generate unified, accurate S&OP plans, supporting faster responses to demand changes and optimized resource allocation.
  • Inventory Management: AI can set reorder points and safety stock levels based on demand variability, supplier lead times, and seasonal fluctuations, reducing carrying costs and minimizing stockouts.

These applications promise significant efficiency gains, cost reductions, and enhanced risk mitigation. By automating routine tasks, AI frees procurement and supply chain professionals to focus on higher-value activities requiring judgment, creativity, and business context.

Implementing Agentic AI: Challenges and Strategic Imperatives

While the benefits are compelling, successfully implementing Agentic AI requires careful consideration of several factors. As part of the research I mentioned earlier, one approach that stood out to me came from PwC’s 2025 Digital Trends in Operations survey. In this survey of operations and supply chain leaders, 92% cited at least one reason why their technology investments have fallen short of expectations—and 83% cited two or more reasons. The most common barriers were integration complexity (47%) and data issues (44%).

So as with any digital transformation initiative, there are several caveats I keep hearing and are worth repeating:

  • Data Quality and Readiness: AI systems are only as good as the data they are trained on. Organizations must prioritize clean, accurate, and accessible data, addressing inconsistencies, incomplete information, and fragmented systems. The ability to capture and process both structured and unstructured internal data (e.g., emails, meeting transcripts, contract specifications) is crucial for competitive advantage.
  • Change Management and Upskilling: The human element is paramount. Leaders must address employee concerns about job displacement, emphasize new higher-value responsibilities, and invest in training programs that build AI collaboration skills. The shift is from “job replacement” to “job evolution,” focusing on strategic analysis, negotiation, and AI oversight roles.
  • Governance and Ethics: Clear governance is essential for risk management and adoption success. This includes defining AI decision authority boundaries, mitigating biases (e.g., in supplier selection), and ensuring regulatory compliance (e.g., data privacy laws like GDPR). The concept of a “human in the loop” is vital for strategic decisions, allowing human oversight even in highly automated processes.
  • Avoiding “AI Washing”: Organizations must critically assess vendor solutions to distinguish true Agentic AI capabilities from mere “agent washing” where existing products are superficially relabeled.
  • Strategic Roadmap: A successful implementation typically begins with pilot programs to demonstrate value, followed by gradual scaling and full integration into existing systems and processes. Executives are urged to “define business objectives, not just AI projects” and build a culture of responsible innovation.

Cutting versus Bleeding Edge

AI is and will continue to power constant cost benchmarking, real-time monitoring of production sites, and continuous analysis of supplier sentiment. This shifts organizations away from calendar-based reviews toward continuous optimization, enabling near-instant responses to market shifts or disruptions.

But as I’ve heard on a recent recorded conference session, “AI isn’t here for your jobs; people who know how to use AI are here for your jobs“. This highlights the need for procurement and supply chain professionals to embrace new skills and adapt to working alongside AI. So just like Darwin intimated, it’s not so much the strongest that will survive, it’s the ones that are most effective and adaptable to the new rapidly changing environment.

Moreover, many pundits say the next frontier lies in “agent-to-agent” interactions, where AI systems communicate and coordinate tasks directly, reducing interaction costs and accelerating processes even further. Yet, while the potential is significant, much remains unknown. Today we don’t yet fully understand how autonomous agents will compete or collaborate—or what happens when those interactions break down. These unanswered questions will shape how quickly organizations are willing to embrace the move to fully autonomous processes.

The Future Looks Agile and Autonomous

As new technology players unfold and existing technology platforms accommodate to it, the integration of Agentic AI is no longer optional; it’s becoming a competitive edge. Organizations that strategically embrace this transformation will position themselves as an essential contributor to competitive advantage in an increasingly complex global business environment.

And from this perspective, Liberis Consulting is committed to helping businesses navigate this complex landscape by helping the companies developing this technology to better annunciate the benefits they bring to market. In this regard, I am truly looking forward to drawing further insights on Agentic AI at the upcoming KonnectHouse conference in London on Sept 4. Conferences like these accelerate the understanding of the nuances of an Agentic AI implementation, for helping build resilient, efficient, and future-ready procurement and supply chain operations. Check it out!

Are you an AI-native technology provider in Procurement or Supply Chain that wants to navigate this new realm? Contact us to discuss your positioning and strategy in the evolving Agentic AI technology landscape. Let’s talk.

Know When to Hold Them and Know When to Fold Them: Using GenAI to Build Content That Delivers

Posted on by Constantine Limberakis

In the high-stakes game of B2B marketing, messaging can feel a lot like poker. You make bets on campaigns, go all-in on taglines, and hope your audience doesn’t call your bluff. But too often, marketers are playing blind—relying on intuition, anecdotal feedback, or outdated personas instead of a true read on the table. It’s time we got smarter about the hands we play.

Thanks to artificial intelligence (AI), marketers now have access to a sharper barometer for messaging—one that helps you know when to hold onto what’s working and when to fold what’s falling flat.

♠️ The Messaging Gamble: Why Most Teams Don’t Know What Resonates

In procurement, supply chain, and other enterprise tech spaces, the challenge isn’t a lack of content—it’s signal versus noise. Even when the best product marketers pour effort into value propositions, proof points, and solution briefs, without real-time feedback loops, the decision to double down on a message or walk away from it is often made too late—or not at all.

Ask yourself:

  • Are you optimizing based on what customers actually respond to?
  • Or are you recycling messaging that feels comfortable but no longer converts?

🧠 How Tools Like Troupe.ai Solve the Messaging Barometer Challenge

While most AI platforms give you pieces of the puzzle, tools like Troupe.ai help you complete the whole picture. Designed specifically for B2B marketing teams, Troupe uses generative AI and proprietary intelligence models to evaluate your messaging against what actually matters—customer resonance, competitive differentiation, and market momentum.

Here’s how Troupe solves the problem most teams face:

  • Message Alignment Scoring: Troupe evaluates your messaging—value props, web copy, product descriptions—and scores them based on how well it matches (or doesn’t) the messaging guide to create a baseline / benchmark.
  • Live Market Feedback Loops: By pulling in real-time feedback from customer conversations, competitor shifts, and macroeconomic sentiment, Troupe continuously calibrates your messaging barometer.
  • Competitive Contrast Engine: Want to know if you sound like everyone else? Troupe compares your narrative side-by-side with competitors, identifying areas where you can stand out—or where you’re just adding to the noise.
  • Persona-Driven Insights: Troupe’s intelligence layer doesn’t treat all buyers the same. It analyzes how different personas react to different messages—empowering your team to tailor content for relevance, not just reach.

In short: Troupe gives your team a data-driven “gut check” before you launch your next campaign. It helps you hold the messaging that converts—and fold what no longer earns you a seat at the table.

♦️ Knowing When to Hold: Doubling Down on What Works

Let’s say your AI-powered analysis shows that Chief Procurement Officer personas are increasingly responding to messaging about “cost containment and risk visibility”—but not engaging with content that focuses on “supplier innovation.”

That’s your signal to hold.

Lean into the themes that resonate. Use that insight to:

  • Refine web copy and landing pages
  • Create targeted nurture sequences
  • Arm sales with proof points and objection-handling tied to that value

Consistency doesn’t mean repetition—it means strategic reinforcement based on what actually works.

♣️ Knowing When to Fold: Letting Go of the Duds

The harder decision? Letting go of messages you’re emotionally or historically attached to. Maybe your leadership loves a certain tagline. Or your product team insists that “modularity” is the killer differentiator. But if AI reveals the message isn’t landing, it’s time to fold.

And folding isn’t failure—it’s focus.

Letting go of low-performing messaging:

  • Frees up resources for what moves the needle
  • Avoids brand dilution
  • Shows you’re listening—to the market, not your own bias

🎲 Playing the Long Game with AI

AI won’t write your brand story for you. But it will keep your storytelling honest. The best product marketers combine creative instincts with AI-powered insights. They test, iterate, and refine—constantly calibrating their messaging barometer to the shifting winds of the market. Because in the end, winning the game of messaging isn’t about having the flashiest hand. It’s about knowing which cards to hold, which to fold, and when to bet big.

Play your cards right:

Struggling to pinpoint the perfect message for your audience? Discover how Liberis Consulting can empower your business with AI-driven tools like Troupe.ai, transforming your strategic positioning into a powerful performance engine.