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Author: Bill DeMartino

Where Autonomy Meets Reality — Enterprise Context, External Conditions, and Decision-Making (Part 3)

Posted on by Bill DeMartino

In the previous article, we introduced Enterprise Context as the organizational understanding that allows information to be interpreted consistently within a specific enterprise. We suggested that as AI moves beyond supporting users toward participating in decision-making and execution, this understanding becomes increasingly important.

Enterprise Context, however, is only part of the picture.

Procurement decisions are rarely made based solely on what is happening inside the enterprise. They are also shaped by changing supplier markets, geopolitical events, commodity movements, regulatory developments, and many other external factors.

The challenge isn’t simply understanding the enterprise.

It’s determining how the enterprise should respond to the world around it.

The Enterprise Doesn’t Operate in Isolation

Procurement technology has made significant progress in bringing external intelligence into enterprise decision-making.

Supplier intelligence platforms provide visibility into financial health, ESG performance, and operational risk. Category management and market intelligence solutions help organizations understand pricing dynamics, supply markets, and sourcing opportunities. Risk platforms monitor geopolitical events, logistics disruptions, and supplier exposure. Increasingly, these capabilities are being combined with AI to surface recommendations and identify emerging issues.

The breadth and maturity of these capabilities vary across the market, but collectively they represent an important evolution in procurement technology.

We refer to these changing conditions collectively as External Context—the supplier markets, geopolitical events, regulatory developments, commodity movements, logistics disruptions, and other external factors that influence procurement decisions.

Enterprise Context explains how the enterprise operates.

External Context explains the environment in which it operates.

Procurement decisions emerge by reconciling Enterprise Context with External Context to determine the appropriate course of action.

Decision-Making Is an Exercise in Integration

Consider the supply chain risk example introduced earlier in this series.

A risk monitoring platform identifies that a critical supplier has been exposed to a geopolitical disruption.

The signal is accurate.

External Context explains the disruption.

Enterprise Context explains the organization’s relationship to that supplier—its dependency, contractual commitments, inventory position, operating priorities, and risk policies.

Neither determines the appropriate course of action.

  • Should procurement engage the supplier immediately?
  • Can existing inventory absorb the disruption?
  • Are qualified alternatives already available?
  • Should production schedules change?
  • Does the business accept the risk, or begin mitigation immediately?

Historically, these have been the kinds of decisions experienced procurement professionals have made every day.

Not because the systems failed.

But because procurement decisions have always required integrating multiple perspectives before determining the appropriate course of action.

Decision-Making Under Uncertainty

Procurement decisions are rarely made with complete information.

Supplier situations evolve. Market conditions change. Customer priorities shift. organizational priorities continue to evolve. Yet the enterprise still has to determine an appropriate course of action.

Historically, experienced procurement professionals have performed that role by bringing together the best available Enterprise Context, External Context, organizational priorities, and business experience before acting. In doing so, they reconciled incomplete information, competing signals, and business trade-offs that were rarely represented explicitly within software. As AI increasingly participates in decision-making and execution, software must increasingly perform more of that integration itself—not because uncertainty disappears, but because decisions still need to be made despite it.

The objective is not to eliminate uncertainty.

It is to enable decisions—whether made by people, AI, or increasingly some combination of both—to be grounded in a consistent and shared understanding of the enterprise.

A More Practical View of Autonomy

Thinking about autonomy in this way leads to an important observation.

Progress toward autonomy isn’t simply a function of more capable AI models. It also depends on how effectively software can operate with increasingly rich Enterprise Context and External Context.

As Enterprise Context becomes more explicit and procurement technologies continue to incorporate richer External Context, software will increasingly participate in a broader range of procurement decisions and execution.

That doesn’t mean every procurement activity should become autonomous.

Some operate within relatively stable boundaries and may ultimately require very little human involvement.

Others involve greater uncertainty, more significant business trade-offs, or higher consequences if the wrong course of action is taken.

The interesting question isn’t whether people remain involved.

It’s where experienced practitioners continue to create the greatest value as software assumes greater responsibility for decision-making and execution.

Looking Ahead

Understanding procurement decisions in this way also helps explain why autonomy progresses unevenly across procurement.

Different procurement activities require different amounts of Enterprise Context, External Context, and integration before an appropriate course of action can be determined.

Those differences—not AI capability alone—have a profound influence on where higher levels of autonomy become practical and where experienced practitioners will continue to create the greatest value.

That’s where we’ll conclude this series.

Enterprise Context — A Foundation for Advancing AI and Autonomy (Part 2 of Series)

Posted on by Bill DeMartino

In the first article of this series, we suggested that procurement autonomy is better understood as a continuum than a destination. We also proposed that as AI moves from supporting users toward participating in decision-making and execution, the amount and richness of understanding required by software increases significantly.

That naturally raises another question.

What kind of understanding does software actually need to participate reliably in decision-making and execution?

Procurement Decisions Don’t Begin with Information

Experienced procurement professionals rarely make decisions by looking at a single report, recommendation, or AI-generated insight. They interpret information within the operating realities of the enterprise—drawing on organizational priorities, historical experience, commercial commitments, and an understanding of how the business actually works.

Much of this understanding has traditionally lived in people rather than in software.

They understand:

  • how the organization operates
  • which supplier relationships are strategic
  • where contractual commitments exist
  • how inventory is positioned
  • what policies govern acceptable risk
  • what has happened before
  • what matters most to the business

Much of this understanding isn’t tied to any single application.

It exists across systems, processes, and accumulated organizational experience.

Yet it fundamentally shapes both procurement decision-making and execution.

Enterprise Context

We use the term Enterprise Context to describe this connected understanding of how an organization operates.

It includes the relationships, historical continuity, organizational structures, operating constraints, business policies, and strategic priorities that allow information to be interpreted consistently within the enterprise.

Enterprise Context isn’t simply additional information.

It provides the organizational understanding that gives information meaning within the enterprise.

Historically, procurement systems were designed to help people interpret information. That worked because procurement professionals naturally supplied the missing organizational understanding themselves. As AI begins participating more directly in decision-making and execution, that assumption changes. Systems increasingly need the enterprise to represent that understanding explicitly rather than relying on people to reconstruct it every time a decision is made.

This distinction matters.

Two organizations may receive the same supplier recommendation, the same disruption alert, or the same sourcing opportunity. And they may arrive at very different decisions.

Not because the information is different.

Because the enterprise context is.

Why Enterprise Context Matters

Today’s AI capabilities are increasingly effective at retrieving information, identifying patterns, and generating recommendations.

As systems begin participating more directly in decision-making and execution, however, they must also operate within the realities of a specific enterprise.

A recommendation is only valuable if it reflects the organization’s operating model, commercial commitments, governance, historical experience, and strategic priorities.

This isn’t simply about building more capable AI models.

It’s about enabling software to understand the enterprise in which it operates.

Without that broader understanding, systems can produce useful insights while still struggling to determine the most appropriate course of action.

This isn’t simply a question of model capability.

It’s a reflection of how procurement decisions are actually constructed.

Building Toward Greater Autonomy

We don’t see Enterprise Context as the complete answer to procurement autonomy.

It is, however, a foundational one.

The more explicitly the enterprise can represent how it operates, the more effectively AI can participate in both decision-making and execution.

That doesn’t eliminate the need for people.

It changes the kinds of decisions where human expertise creates the greatest value.

Looking Ahead

Enterprise Context explains how the enterprise understands itself.

Procurement decisions, however, are also shaped by conditions outside the enterprise.

The interesting question isn’t whether one matters more than the other.

The next article explores how Enterprise Context comes together with conditions outside the enterprise to shape decision-making and execution.

A Practical Lens on AI Usage and the Path to Procurement Autonomy

Posted on by Bill DeMartino

AI is changing the ambition of enterprise software. For years, procurement technology has focused on helping professionals gather information, identify patterns, and make better decisions. Increasingly, however, AI is being expected to participate directly in decision-making and execution.

This shift extends well beyond procurement. Across enterprise software, product teams are investing in copilots, intelligent agents, orchestration platforms, and new approaches to representing enterprise knowledge. While these technologies address different problems, they all point toward the same objective: enabling software to operate with a richer understanding of how the business actually works.

That prompted us to step back and ask a broader question:

What actually determines how far procurement autonomy can progress?

We believe the answer extends well beyond AI capability alone. As AI moves from supporting users toward participating in decision-making and execution, the amount and richness of organizational understanding required increases significantly.

AI Is Changing How Work Gets Done

AI is already creating meaningful value across procurement.

From contract analysis and spend classification to supplier discovery and risk identification, today’s capabilities are helping procurement professionals work faster and make better-informed decisions.

What’s more interesting, however, is how AI is changing the role software is expected to play.

Historically, procurement systems were designed to support human interpretation. People naturally supplied organizational context, historical perspective, and business judgment that rarely existed explicitly within software.

Increasingly, AI is expected to participate in decision-making and execution itself.

That changes the problem technology providers are trying to solve.

From Information to Decision-Making

Consider a familiar supply chain risk scenario.
A risk monitoring platform identifies that a critical supplier has exposure to a geopolitical disruption. The alert is accurate.
But determining the appropriate response requires considerably more than acknowledging the alert.

  • How dependent is the business on this supplier?
  • What inventory is available?
  • Are qualified alternatives already approved?
  • What contractual commitments exist?
  • How has the supplier responded to previous disruptions?
  • What level of risk is acceptable to the business?

These aren’t unusual procurement questions. They arise every day.

The important observation is that while the system successfully identifies the issue, determining the appropriate response still depends on understanding the enterprise in which the event occurs.

We don’t believe this pattern is unique to supply chain risk.

The same dynamic appears in sourcing, contracting, supplier onboarding, and many other procurement activities. As decisions become more consequential, they increasingly depend on a broader understanding of the enterprise.

A Different Way to Think About Autonomy

Rather than thinking only about what AI can do, we’ve found it useful to consider what AI needs in order to participate reliably in decision-making and execution. That shift in perspective changes how we evaluate procurement activities and where higher levels of autonomy are likely to emerge.

We’ve found it more useful to ask three different questions.

  • How much context does this activity require to support reliable decision-making and execution?
  • How much of that context is available to the system?
  • What are the consequences of getting the decision or execution wrong?

Taken together, these questions provide a practical way to think about procurement autonomy.

Some procurement activities operate within relatively well-defined boundaries. Others require a much broader understanding of the enterprise, the external environment, and the business consequences of acting. As a result, they are unlikely to progress toward autonomy at the same pace—not because AI is less capable, but because the understanding required becomes substantially richer.

Viewed through this lens, procurement autonomy is better understood as a continuum than a destination.

Looking Ahead

Throughout this article we’ve deliberately used the word context without defining it too precisely.

That’s because we believe the more important question isn’t whether AI has access to more information, but whether it has enough understanding of the enterprise to participate reliably in decision-making and execution.

What kind of understanding does software actually need?

That naturally raises another question.

What do we actually mean by context, and what would it take for systems to operate within it rather than requiring people to reconstruct it for every important decision?

That’s where we’ll turn next.

Crossing the Pond: Why European B2B Startups Are Engaging the U.S. Earlier

Posted on by Bill DeMartino

For years, European B2B startups followed a familiar expansion path.

Build traction in Europe.
Raise larger funding rounds.
Then expand into North America.

That approach made sense when product development cycles were longer and categories evolved more slowly.

But the enterprise software landscape has changed.

Today, categories form faster, differentiation erodes sooner, and the United States remains the center of gravity for enterprise software spending and ecosystem influence.

As a result, many European founders are reconsidering when—and how—they begin engaging the North American market.

Earlier Engagement, Not Premature Expansion

What we are seeing is not a rush to open large U.S. operations.

Instead, more companies are beginning structured market engagement earlier in their growth cycle—often well before full commercial expansion.

In practice, this can mean early conversations with analysts and ecosystem partners, design-partner pilots with a small number of U.S. customers, or targeted visibility in North American industry networks. The goal is not immediate scale but building credibility, learning quickly, and validating go-to-market assumptions.

Increasingly, these early signals—customer proximity, market presence, and credible engagement—also influence investor confidence and funding dynamics.

Execution Still Determines the Outcome

Of course, entering the U.S. market earlier does not guarantee success.

Many European startups encounter friction not because of weak technology but because the expectations of U.S. enterprise buyers differ—particularly in areas such as go-to-market discipline, commercial structure, and operational readiness.

In other words, the challenge is not simply entering the U.S. market.

It is executing effectively once you begin engaging it.

Join the Conversation

These themes are explored in more depth in the recent Liberis whitepaper: “Crossing the Pond: Accelerating Hi-Tech B2B Startups for Scalable Growth.”

To continue the discussion, Liberis Consulting is hosting an upcoming session:

Strategies for Crossing the Pond: Accelerating Hi-Tech B2B Startups for Scalable Growth

The discussion will feature perspectives from both advisors and operators who have navigated EU-to-U.S. expansion.

Speakers include:

  1. Jean Arnaud(Visontio) — bringing the perspective of a European founder building in the procurement technology space and navigating international market growth.
  2. Bill DeMartino (Liberis Consulting) — sharing experience supporting B2B solution providers expanding into the North American enterprise market.
  3. Constantine Limberakis (Liberis Consulting) — offering additional insight from working across European and North American SaaS ecosystems.

We’ll discuss:

  • Why the timing of U.S. engagement is shifting earlier
  • How startups can build credibility with North American buyers
  • Common pitfalls European companies encounter when entering the U.S. market
  • Practical approaches to building a scalable go-to-market motion

👉 Register for the webinar:

If you are a European B2B startup considering North American expansion—or trying to determine when that moment should come—this discussion should provide a practical perspective.


 Learn more about Liberis’ perspective on GTM strategy at Liberis Consulting.

Why GTM Gets Harder as You Scale

Posted on by Bill DeMartino

In the early stages of a company, clarity doesn’t need an owner. It lives in judgment.

Founders see the market directly. They hear objections firsthand. They make tradeoffs in real time. Product direction, sales motion, and positioning evolve from the same set of experiences. Shared understanding isn’t formalized — it’s lived.

Then the company scales.

New leaders are hired. Functions specialize. Metrics multiply. Execution accelerates. And slowly, the clarity that once lived implicitly in a few minds begins to diffuse across the organization.

This transition is normal.
And it’s one of the most common inflection points in growth-stage go-to-market.

In our previous piece, we explored why clarity has become a defining factor in effective B2B go-to-market strategy. The natural next question is what happens to that clarity as organizations scale.

From Shared Judgment to Parallel Interpretations

As scale increases, proximity to the market decreases.

Sales sees pipeline patterns but not always the economics behind lost deals. Marketing sees engagement metrics but not always the constraints shaping buying decisions. Product sees roadmap velocity but not always the tradeoffs buyers are unwilling to make. Leadership sees dashboards — not the friction inside conversations.

Each function begins optimizing for its own definition of success.

Sales optimizes for conversion.
Marketing optimizes for response.
Product optimizes for delivery.
Customer teams optimize for retention.

All of it makes sense. And it’s exactly how clarity gets lost.

This is where many leadership teams begin to feel a specific kind of strain: the organization is working hard, but decisions feel harder to make — and harder to stand behind with conviction.

The Product Marketing Expectation

When clarity starts to erode, many organizations look to Product Marketing.

In many growth-stage companies, that function doesn’t yet exist — which often means the responsibility is distributed informally across marketing, sales leadership, and the founder.

Where PMM does exist, the logic is understandable. PMM owns positioning, messaging, and enablement. If the story feels inconsistent, strengthen the narrative function.

But in complex B2B markets, clarity is rarely solved through messaging alone.

Clarity depends on sustained exposure to real deal dynamics, buyer economics, competitive behavior, and ecosystem constraints. It also depends on authority to shape tradeoffs upstream — not just document them downstream.

Product Marketing can sharpen how strategy is expressed. But without structural authority and consistent visibility into market reality, it is often asked to solve a problem that extends beyond any single function.

This is not a failure of Product Marketing. It reflects a structural gap between responsibility, authority, and sustained exposure to real market dynamics in scaling organizations.

Why Clarity Has No Natural Home

The deeper issue is structural.

Clarity is cross-functional. It evolves as markets evolve. It is shaped by deal outcomes, competitive shifts, buyer economics, and operating constraints.

Organizations, however, are built around functional accountability. That structure enables scale — but it leaves the one thing that must remain shared across functions with no natural owner.

So clarity becomes assumed rather than maintained.

Leadership teams can remain aligned in meetings. Plans can look coherent. Execution can remain intense.

And yet interpretation begins to diverge.

The Real Cost: Inconsistent Stories

When clarity fragments, the consequences show up in ways leaders recognize immediately.

Internally, teams begin defending metrics in isolation. Tradeoffs are debated repeatedly. Energy shifts toward reconciliation rather than forward motion.

Externally, the organization begins telling different stories — about who the buyer is, what differentiates the product, and why it wins.

Marketing says one thing.
Sales adapts it.
Product introduces nuance.
Leadership reframes it for investors.

Buyers notice.

Markets respond quickly to inconsistency. Positioning becomes reactive. Sales cycles elongate. Competitive differentiation blurs. Strong technology loses leverage because the narrative around it lacks conviction.

Strong tech is rarely the limiting factor.
Clarity is.

Leadership as the Anchor

If clarity does not belong to a function, it ultimately becomes a leadership discipline.

Not in the sense of controlling every message — but in the sense of maintaining shared context. Ensuring that buyer reality, competitive dynamics, and strategic tradeoffs remain coherent across product, sales, and marketing as the organization scales.

This is not easy work. It requires time, exposure, and repeated reinforcement — especially as markets change and organizations grow.

But when leadership treats clarity as something to sustain, rather than something to assume, the organization regains leverage. Narrative, positioning, and execution begin reinforcing one another again.

Clarity as a Growth Multiplier

At Liberis, we view GTM clarity as a growth multiplier because it creates durable alignment across narrative, positioning, and execution — alignment the market can recognize and trust.

When teams tell a consistent story grounded in real market dynamics, scale accelerates.

When they don’t, complexity compounds.

In scaling organizations, clarity is not a deliverable. It is an executive responsibility — and one that often benefits from an outside perspective to make durable.

If this dynamic feels familiar, we welcome the conversation..


 Learn more about Liberis’ perspective on GTM strategy at Liberis Consulting.

Navigating B2B GTM Strategy with Elevated Clarity

Posted on by Bill DeMartino

Across B2B solution providers, go-to-market decisions have always required judgment. What has changed is how much uncertainty organizations can afford.

As technical differentiation erodes and markets crowd, the margin for GTM ambiguity has collapsed. Product advantage alone no longer absorbs misalignment or incomplete understanding. In this environment, clarity—how well an organization understands and acts on its buyer reality—has become a primary determinant of GTM effectiveness.

In early stages, clarity is often implicit and held by a small group of leaders. As organizations scale, that implicit clarity becomes a constraint unless it is made shared and durable.


Yet many teams respond to this shift by increasing activity. More messaging. More enablement. More campaigns. AI and automation make it easier than ever to generate output at scale. What they do not guarantee is shared understanding.

This is not an execution problem.
It is a clarity problem.

Clarity Is More Than Knowing the Buyer

When organizations talk about clarity, they often reduce it to knowing the buyer: defining personas, articulating value propositions, or refining messaging. That understanding is necessary—but insufficient.
In complex B2B environments, buying decisions are shaped as much by ecosystem reality as by stated needs. Buyers operate within constraints that GTM strategies frequently overlook:

  • Entrenched vendors and long-standing relationships
  • Prior investments and sunk costs
  • Historical failures that create skepticism
  • Internal politics and risk aversion
  • Category maturity and fatigue

Most GTM strategies implicitly assume a rational, open buyer evaluating solutions in isolation. In reality, buyers are navigating legacy decisions, institutional memory, and competing priorities that materially influence how value is perceived—and whether change is even possible.


Elevated clarity requires understanding not just who the buyer is, but the environment in which buying decisions are made.

When More Tools Create More Noise

AI has dramatically increased the speed at which GTM teams can generate content, insights, and enablement. What it has not increased is contextual understanding.


AI excels at amplifying what can be articulated. It struggles with what is implicit, political, or historically informed. As a result, it often reinforces surface-level clarity while obscuring deeper misalignment.

The paradox many organizations now face is this:
they can say more, faster—but they do not necessarily know more.

When context is missing, teams compensate with volume. Activity increases, but confidence does not.

What We Mean by Elevated Clarity

Clarity is the foundation that makes messaging, alignment, and execution meaningful. It reflects a shared understanding of buyer reality, competitive context, and the tradeoffs inherent in go-to-market strategy—and it shows up in the quality, consistency, and follow-through of decisions across sales, marketing, and product.


Clarity is not an artifact.
It is not a deck, a framework, or a planning exercise.

It is observable in how consistently an organization makes decisions as conditions change.

Where the Need for Clarity Becomes Visible

The need for elevated clarity rarely announces itself directly. Instead, it surfaces through a set of predictable signals as organizations scale and markets shift:

  • Clarity is assumed rather than shared. Direction feels self-evident at the leadership level, but is interpreted differently across sales, marketing, and product.
  • Early GTM success stops scaling. What worked to win initial customers breaks down as organizations move into broader markets, new segments, or more complex buying environments.
  • Founder-led intuition gives way to functional execution. Clarity that once lived implicitly with a small leadership group diffuses without being institutionalized.
  • The ecosystem evolves. New competitors emerge, categories evolve, or buyer expectations shift—altering how value is evaluated.
  • The product expands faster than the narrative. Capabilities grow, but differentiation and internal understanding lag behind.
  • Activity increases, but confidence does not. Teams accelerate output to compensate for uncertainty, often amplified by AI and automation.

In each of these moments, the challenge is not effort or intent.
It is whether clarity is truly shared across the organization.

From Clarity to Action

Sales enablement is often where clarity problems become visible. In many organizations, enablement is treated as a downstream activity—decks, training sessions, and playbooks designed to support execution. When clarity upstream is missing, enablement becomes an exercise in translation rather than reinforcement. Sales, marketing, and product each operate from slightly different interpretations of buyer reality, and enablement attempts to reconcile them after the fact.

When clarity is shared, enablement looks very different. It becomes the instantiation of that clarity—translating a common understanding of buyer context, competitive dynamics, and tradeoffs into consistent conversations, decisions, and motion in market.

This pattern extends far beyond enablement. Wherever clarity exists only in pockets, organizations struggle to turn insight into coordinated action.

Navigating Clarity Over Time

In complex, shifting GTM environments, clarity only matters if it leads to coordinated action. Insight that remains abstract does not change outcomes.


The challenge is not only arriving at clarity once, but sustaining it as markets, products, and organizations evolve—and ensuring it is consistently reflected in decisions across sales, marketing, and product.


This perspective underpins how Liberis thinks about navigating go-to-market strategy in practice—as the ongoing work of maintaining and applying clarity as conditions change.

What Comes Next

In the next piece, we’ll explore why GTM clarity so often has no clear owner—and what that gap reveals as organizations scale.


If this perspective resonates, we welcome the conversation.


 Learn more about Liberis’ perspective on GTM strategy at Liberis Consulting.

Autonomous Procurement and the Question of Oversight

Posted on by Bill DeMartino

What Enterprises and Solution Providers Need to Be Preparing For

Over the past year, the conversation around AI in procurement has shifted noticeably. What began with copilots and assistive tools has moved quickly toward agents—systems that can interpret intent, orchestrate workflows, and increasingly take action on behalf of users.

Much of the market’s attention has focused on what these systems can do: accelerate sourcing, reduce manual effort, improve responsiveness, and help procurement teams operate with fewer resources. All of that matters. But in our view, a more consequential shift is beginning to take shape—one that leading-edge organizations are starting to encounter today, and that will become increasingly relevant as autonomous capabilities mature. It has less to do with user experience and far more to do with enterprise readiness.

As procurement platforms introduce more autonomous capabilities, the challenge is no longer just adoption. It is oversight.

In procurement, action equals authority. Autonomous systems that initiate supplier outreach, advance sourcing events, or influence commercial decisions are not simply accelerating work; they are redistributing decision-making authority across the organization. And that redistribution is happening faster than most enterprises—and many solution providers—are prepared to manage.

Autonomy Creates a New Control Challenge

This is not a future in which a single, centralized AI makes procurement decisions for the enterprise. It is far more subtle—and more complex.

What is emerging instead is a model in which many individual users, across a matrixed organization, can easily initiate autonomous actions that operate across shared suppliers, categories, and systems. As the technology lowers the friction to act, it becomes increasingly simple to “kick things off”—often without full visibility into what is already underway elsewhere in the organization.

Each individual action may be entirely reasonable in isolation. But as autonomy scales, these actions compound. The cumulative effect introduces new forms of risk that traditional procurement controls—designed for slower, more sequential, human-led processes—were never built to manage.

Automation improves execution.

Autonomy, however, changes who—and what—has authority to act.

At scale, that distinction matters.

How Autonomy Risk Emerges in Practice—One User at a Time

The risks associated with autonomy are often framed in terms of scale—many agents acting simultaneously during a disruption or sourcing cycle. In practice, the same risks begin earlier, when individual users first gain the ability to initiate autonomous actions, and intensify as those actions multiply across the enterprise.

A category manager asks an agent to explore alternate suppliers following a delivery concern. The agent identifies candidates, initiates outreach, requests pricing, and begins comparing offers. From the user’s perspective, this feels like a reasonable, bounded request. From the enterprise’s perspective, a chain of external actions has already begun.

At that point, a different set of questions emerges:

  • Is the agent still operating?
  • What suppliers have been contacted?
  • What signals has already been sent to the market?
  • Has this activity conflicted with other sourcing efforts?
  • Who can intervene if conditions change?

In many environments today, those questions are difficult to answer in real time.

Autonomous agents also collapse multi-step processes into intent-driven flows. Approval thresholds, negotiation posture, or spend limits may be crossed indirectly as actions chain across systems. The issue is not misuse; it is unintended delegation. Authority is exercised not because someone explicitly approved it, but because the system inferred it.

There is also the quieter problem of persistence. Autonomous processes can continue after the initiating user has moved on, priorities have shifted, or assumptions are no longer valid. Without enterprise-level visibility, organizations risk having agents act on stale intent—well after the rationale that created them has faded.

When many users initiate agents in parallel, the effects amplify. Duplicate supplier outreach, inconsistent negotiation signals, and erosion of leverage become real possibilities. But even here, the root cause is the same: limited visibility into autonomous intent in motion.

Enterprises are well equipped to track transactions. They are far less equipped to track autonomous activity as it unfolds.

Why This Isn’t Widely Recognized Yet

It would be easy to frame this as a market failure. We do not think that is accurate.
The current focus on agents, orchestration, and automation is understandable. These are early capabilities, and much of the innovation to date has been about proving that AI can operate effectively within procurement workflows at all.

Governance, where it appears, is typically framed in familiar terms: approvals, audit trails, explainability. Those controls are necessary—but they were designed for systems where humans initiate and complete discrete steps. They are less effective in environments where intent triggers chains of autonomous action that persist and adapt over time.

The gaps described here are not the result of negligence. They are a natural consequence of capability advancing faster than operating models.

Signals from the Market—and What They Don’t Yet Address

There are clear signals that the market is moving toward more autonomous procurement capabilities. Task-level autonomy, workflow orchestration, and embedded compliance checks are becoming more common.

What is less consistently addressed is how autonomy behaves at the enterprise level:

  • How autonomous actions are supervised as they run
  • How duplication and conflict are prevented across users and agents
  • How authority is bounded and adjusted dynamically
  • How outcomes are monitored over time

These are not trivial problems, and it is reasonable that they lag early innovation. But as autonomy expands, they become harder to ignore.

What “Minimal Viable Oversight” Might Start to Look Like

We want to be explicit: there is no validated model today for governing procurement autonomy at scale. What follows are ideas, not prescriptions—signals of what enterprises are beginning to grapple with as autonomy matures.

At a minimum, oversight is likely to require capabilities in five areas:

Visibility into active autonomous actions
Not just what has happened, but what is currently in motion.

Ability to intervene
The capacity to pause, redirect, or halt autonomous activity when conditions change.

Clear linkage between intent, accountability, and outcome
Understanding who initiated an action, under what context, how responsibility is assigned, and what resulted—particularly when autonomous actions persist or have enterprise-wide impact.

Awareness of overlap and conflict
Identifying when multiple autonomous efforts are operating against the same suppliers, categories, or events.

Tracking of progress and impact over time
Observing whether autonomous actions are delivering intended results, drifting from original objectives, or producing unintended consequences.

As autonomy scales, enterprises may also need greater awareness of the resources these systems consume—from compute and model usage to infrastructure cost—especially when autonomous activity can be initiated easily and in parallel.

This is not about dashboards or tooling. It is about establishing feedback loops that allow enterprises to learn where autonomy works well, where it needs constraint, and how it should evolve responsibly.

Autonomy is not a switch. It is a progression.

A Broader Enterprise Pattern

What we are seeing in procurement mirrors developments elsewhere in the enterprise.
Security and identity teams already manage non-human actors—service accounts, bots, integrations—with explicit controls. HR organizations are beginning to explore models for supervising “digital workers.” Risk functions are reassessing how delegated authority should be governed in automated environments.
Procurement autonomy will not exist in isolation. Over time, it will require collaboration across functions to establish shared approaches to oversight that can scale beyond any single domain.

Implications for Procurement Organizations

For procurement leaders, this is not a call to slow down. It is a call to be deliberate.

As autonomous capabilities are introduced, organizations will need to:

  • Explicitly define where autonomy is appropriate and where it is not
  • Treat external-facing actions as higher risk than internal analysis
  • Pilot autonomy in bounded contexts before scaling
  • Focus on readiness and learning, not just speed

The goal is not to eliminate risk, but to make it visible and manageable.

Implications for Solution Providers

For solution providers, autonomy represents both opportunity and responsibility.

As capabilities mature, enterprise customers—particularly large, matrixed organizations—will increasingly expect support not just for execution, but for oversight. This is not simply a matter of messaging or posture. Supporting enterprise-grade autonomy requires real technical capability—designed, built, and proven over time. Solution providers that invest in these capabilities will be better equipped to act as credible partners as customers navigate unfamiliar operating territory, and that foundation can ultimately support a leadership position in the market.

Engaging with these questions early—alongside customers, and through real product and architectural investment—will matter.

Closing

As autonomous capabilities continue to mature, enterprises and solution providers alike will need to engage with questions of oversight, accountability, and enterprise readiness earlier than they might expect. These are not challenges to be solved overnight, nor are they purely technical. They sit at the intersection of operating models, trust, and long-term partnership between providers and their customers.

At Liberis, we work with startups and growth-stage procurement and supply chain solution providers as they navigate exactly these kinds of transitions—helping them clarify how emerging capabilities fit into enterprise reality, articulate credible points of view, and engage customers as true partners as the market evolves.


 Learn more at Liberis Consulting.

Procurement’s Next Shadow Problem: DIY AI is the New Excel

Posted on by Bill DeMartino

For decades, MS Excel has quietly powered the procurement function in many capacities, both upstream and downstream. It’s flexible, familiar, and—critically—under user control. Even after multiple waves of enterprise tech, teams still default to spreadsheets and email because most systems feel too slow, rigid, or disconnected from how procurement really works.

Now, a new force is accelerating that same do-it-yourself mindset: generative and agentic AI. With tools like OpenAI’s ChatGPT, Anthropic’s Claude, and Google Gemini, procurement professionals can spin up RFP drafts, generate supplier scorecards, summarize market research, or even build mini decision engines—instantly. The friction is gone. The result? A surge in homegrown solutions that look a lot like the Excel shadow systems of the past—but move faster, proliferate wider, and carry even more risk.

Why This Is Familiar—yet Different

AI is unlocking exactly what Excel once offered: speed, autonomy, and flexibility. Need to build a new supplier intake workflow? An AI tool can mock up the first version. Want to summarize contract terms? A prompt can extract key clauses. What’s different now is personalized access to AI at a new level of personalized scale. This next generation of AI in the form of generative and agentic tools make it far easier for every team member, not just the Excel superusers, to create bespoke workflows in minutes.
In the short term, this looks like progress. Teams seem more productive. Ideas get tested more quickly. Processes feel more responsive. But beneath the surface, the same old cracks are starting to show.

Silos, Misinformation, and the Loss of Control

Just like Excel once created pockets of hidden data, these next gen AI tools can quickly fracture the procurement landscape. Prompt outputs live on desktops or in chat windows. Decisions are influenced by models no one can audit. Processes evolve in parallel without coordination. There’s no common language, no shared data foundation, no traceability.

This isn’t just a governance problem. It’s a performance problem. When sourcing decisions are based on local models or potentially hallucinated insights, the organization starts making the wrong bets. And because no one sees the full picture, the errors compound.

The Real Risk Isn’t Security. It’s Suboptimal Procurement

Yes, shadow AI poses security concerns. But those will be addressed—through enterprise controls, policy frameworks, and better tooling. The deeper risk is strategic misalignment. If every team builds their own agentic assistant to handle sourcing, intake, or supplier scoring—without shared logic or data—they’re solving today’s pain at the cost of tomorrow’s cohesion.

Left unchecked, this trajectory leads to an even messier version of the Excel era: fragmented decision logic, duplicated tools, disconnected insights. Procurement gets faster, but not smarter or more efficient.

There’s a Better Path: Reinvent the Operating Model

Generative and agentic AI are not just a faster Excel. It’s a chance to redesign how procurement operates. That means shifting from DIY experimentation toward intentional architecture. From individual agents to coordinated intelligence. From workaround tooling to strategic capabilities.

And that doesn’t mean reverting to rigid systems. A new class of tools is emerging—native AI platforms designed for procurement. These blend generative and agentic capabilities to support tasks like always on intelligence, autonomous sourcing, intelligent Intake and Orchestration (I&O), and dynamic supplier engagement. These aren’t generic copilots—they’re purpose-built systems that can learn, adapt, and connect across the procurement lifecycle.

The opportunity is to move beyond task-level acceleration toward true workflow reinvention. To harness AI not just to replicate the old ways faster, but to architect smarter, more transparent, and value-aligned processes from the ground up.

It starts with a clear mandate: channel the creativity next gen AI enables into a shared system of value. Build an AI operating layer that’s governed, data-rich, and procurement-specific. Let teams create—but on rails. Make it easy to innovate without going rogue.

Done right, next gen AI won’t just automate current workflows. It will change what’s possible. And procurement won’t just survive the next shadow wave—it’ll lead it.

At Liberis we work with startups and growth-stage procurement and supply chain solution providers to clarify and amplify their voice so they can stand out, resonate with enterprise buyers, and scale effectively.

👉 Learn more at Liberis Consulting.

From Hype to Trust: Winning the Positioning Battle for Agentic AI in Procurement

Posted on by Bill DeMartino

The procurement technology market is heating up with agentic AI solutions. New vendors appear almost weekly, racing to stake a claim. The result is a crowded landscape where the sheer number of entrants makes it harder than ever for buyers to separate substance from noise.

At Liberis, we’ve been exploring these dynamics from multiple angles. In The Impact of Compressed Adoption Cycles on Product Strategy, we showed how faster adoption curves force product teams to mature capabilities sooner. And in Beyond Buzzwords: Why AI Startups Should Focus on Outcomes, Not Labels, we highlighted the dangers of leaning on terminology instead of delivering outcomes.

This blog builds on both. The sheer volume of new entrants validates the urgency of these themes: buyers need clarity, vendors need sharper positioning, and the market needs guidance on how to cut through the noise. Even when startups move past buzzwords, most positioning still fails to connect with what enterprise buyers actually worry about.

Positioning defines how your solution should be understood in the market. It sets the frame of reference, establishes credibility, and explains why you matter compared to alternatives. Messaging is the articulation of that positioning — how you communicate your solution’s value to buyers, turning the foundation into tailored proof points, narratives, and stories that resonate with specific audiences.

In fast-moving categories like agentic AI, standing out isn’t about who can promise the most features. It’s about who can speak credibly to the buyer’s anxieties — and prove they can overcome them.

The Compressed Adoption Curve

Technology adoption used to be predictable: bleeding-edge innovators, then cautious early adopters, followed years later by the mainstream. Not anymore.

With agentic AI, hype cycles are louder, analyst coverage is faster, and organizational pressure to act is intense. Enterprises aren’t content to “wait and see.” Boards and executives expect procurement leaders to explore AI opportunities immediately, compressing what was once a multi-year curve into a matter of months. That shift means mainstream buyers are arriving earlier, bringing expectations that used to show up much later in a category’s maturity curve. Enterprise-grade requirements around integration, governance, support, and adoption are no longer “future problems” — they’re immediate. Weaknesses that early adopters might have forgiven are now exposed quickly, and credibility is tested from day one.

Four Buyer Anxieties (and Positioning Opportunities)

In product strategy, these anxieties demand new capabilities. In positioning, they demand new language — because as mainstream buyers show up earlier, these concerns surface faster. It’s not enough to claim you’ve solved them. You have to prove it  with credible evidence buyers can trust.

  1. Time-to-Value: Deployment & Integration

    Buyer fear: “This will take forever, break our S2P system, and ROI will slip away.”

    Market signals:     Analysts cite fragmented data, ERP/S2P complexity, and the burden of “yet another tool” as top barriers. Buyers know speed and ERP-friendliness are non-negotiable.

    Positioning opportunity: Show you can plug into ERP and S2P workflows, supplier networks, and adjacent systems without disruption. Credible positioning highlights rapid integration, workflow fit, and speed to results.

  2. Black-box/Abandonment: Support & Success Model

    Buyer fear: “Once it’s live, we’re on our own. Who co-owns outcomes when things go sideways?”

    Market signals: Procurement leaders link AI adoption to talent, skills, and operating-model support. Without ongoing help, even promising AI agents risk being abandoned.

    Positioning opportunity: Frame AI not as a black box but as a co-pilot model: transparent support, human oversight, success frameworks, and service commitments.

  3. Governance/Obsolescence: Keeping Agents Current

    Buyer fear: “Will these agents keep up with policy, compliance, and supplier changes — or go stale and risky?”

    Market signals: Oversight, closed-loop learning, and guardrails are now seen as essential. Buyers worry about governance as much as functionality.

    Positioning opportunity: Position governance and adaptability as differentiators. Highlight safeguards, auditability, and adaptability as part of the solution’s DNA — not afterthoughts.

  4. Team Trust/Adoption: Community, Training & Adoption

    Buyer fear: “My team won’t adopt it. We lack the skills and trust.”

    Market signals: Analyst studies show AI adoption depends less on technology itself and more on execution, change management, and peer trust.

    Positioning opportunity: Demonstrate investment in enablement, training, and peer communities. Buyers trust solutions with clear adoption pathways.

Positioning Traps to Avoid

Even strong providers stumble when they ignore buyer realities. Four traps come up repeatedly in the agentic AI market:

  1. Empty Superlatives
    Claims like “the world’s leading” or “the first autonomous AI agent” may grab attention but rarely build trust. Enterprise buyers expect proof — case studies, analyst validation, adoption data — not puffery. In compressed adoption cycles, credibility > bravado. Unsupported claims risk turning off sophisticated buyers before the sales conversation even begins.

  2. Feature-First Messaging
    Many vendors lead with “we automate sourcing with AI” but stop there. This leaves the “so what?” unanswered — it doesn’t connect to enterprise anxieties like ERP integration, supplier adoption, or governance. Features without context quickly get lost in the noise. What matters is how you prove those features reduce risk and deliver outcomes in the buyer’s environment.

  3. Analyst Echo-Chamber
    Repeating Gartner or Forrester phrases without anchoring them in customer voice comes across as derivative. Buyers notice when language is borrowed versus grounded in real pain points. Analyst framing can aid credibility, but only when combined with customer proof: adoption stories, community validation, peer evidence.

  4. First-ICP Blindness
    Startups often build positioning around their first customer wins, which usually come from buyers with an early adopter mindset. But they fail to evolve as they sell into larger, more risk-averse enterprises. The result: messaging that resonates with one buyer type but alienates or confuses others. This trap is magnified in compressed adoption cycles. Because mainstream buyers show up earlier, vendors who keep speaking only to early adopters risk being dismissed. These buyers bring enterprise-grade expectations around integration, governance, support, and adoption. Positioning must flex across buyer types — from innovators to enterprise committees — or risk losing relevance with the very audience now driving procurement AI decisions.

Modern B2B buying reinforces these traps. Research shows buyers complete 57–70% of their journey before contacting sales, relying on peer networks, AI search, and user-generated reviews. Analyst influence still matters, but peer validation and customer stories often outweigh it. In this environment, what you can prove carries more weight than what you can claim.

Solution Provider Archetypes: Strengths and Gaps

Across the vendor landscape, positioning challenges vary depending on where providers start from. These are not absolutes, but common patterns in how solution providers show up in the market:

  • Established S2P Suites – Strong on governance, compliance, and responsible AI narratives — qualities enterprise buyers trust. But because they are not AI-native, their agentic capabilities often appear as extensions rather than core design. This can make them look slower to innovate and more feature-first in their messaging. Their challenge is to prove agility while keeping credibility.

  • Intake & Orchestration Platforms – Agile and strong on integration/UX narratives, they shine in user experience and speed to adoption. Yet they often lack the depth of core S2P functionality or governance assurances expected by enterprise buyers. Their challenge is to build credibility in robustness without losing their agility story.

  • Niche AI Specialists – Compelling on speed, efficiency, and ROI, they embody the “AI-native” advantage. But they often underplay anxieties around integration, governance, and adoption at scale. Their challenge is to broaden their story to address enterprise-grade requirements without losing their innovation edge.

The winners won’t be defined by where they start — but by how quickly they close these gaps and credibly speak to buyer fears.

The Path Forward

Standing out in a crowded and increasing rapid markets like agentic AI market isn’t about louder claims or more features. It’s about credible positioning:

  • Replace superlatives with proof.

  • Shift features into buyer-context outcomes.

  • Balance analyst framing with customer stories.

  • Broaden messaging to span buyer types.

Those who build positioning around trust, adoption, and governance will separate themselves as leaders — not because they shouted the loudest, but because they proved the buyer’s real fears were solved.

Product strategy creates the foundation; positioning makes that foundation visible, credible, and differentiated. Messaging then carries that positioning into the market, tailored to different buyers and contexts. Together, they determine who scales under compressed adoption cycles — and who stalls.

At Liberis we help growth-stage B2B software companies close this gap. We work with founders and product leaders to sharpen positioning, evolve messaging, and align product strategy to compressed adoption realities — ensuring their solutions resonate with enterprise buyers and stand out in crowded markets.

👉 Learn more atLiberis Consulting.

The Impact of Compressed Adoption Cycles on Product Strategy: Agentic AI in Procurement

Posted on by Bill DeMartino

The Story

Agentic AI is no longer an idea for tomorrow — it’s here today. Solutions already exist across sourcing, supplier management, contract review, intake and orchestration, and post-PO execution. And more are arriving in rapid fire. Each month brings new entrants, new capabilities, and new promises of autonomy.

This flood of solutions coincides with mounting organizational pressure — meaning buyers are evaluating and adopting them far earlier than in past cycles. In past waves of technology, solution providers had the luxury of years to refine products before mainstream buyers arrived. With Agentic AI, that window has collapsed to months. Boards and CFOs are demanding efficiency gains immediately, and procurement leaders are under pressure to “do more with less.” That urgency changes how product strategy must be approached.

The Compressed Adoption Curve

The new wave of AI, including Agentic AI, faces a different kind of adoption cycle — one driven less by technology maturity and more by organizational pressure. Boards and CFOs expect measurable efficiency gains this year, not in three. Procurement leaders are told to “do more with less” and increasingly see AI as the lever. No executive wants to explain why their company is behind peers already piloting these tools.

That pressure has accelerated adoption dramatically, collapsing what once unfolded over years into a matter of months. Bleeding edge, early adopters, and mainstream buyers increasingly overlap, leaving little buffer to refine products gradually.

In past cycles, early adopters might have tolerated rough edges. Today, mainstream buyers are arriving much sooner and expect maturity from the start. Solution providers must be prepared to enter the market earlier than before — with products that not only demonstrate value, but also fit seamlessly into enterprise environments and support long-term scaling.

That acceleration means buyer anxieties around deployment, support, governance, and adoption don’t surface gradually — they converge at once. Providers that don’t design for them up front risk being sidelined before they have a chance to scale.

Four Buyer Anxieties (Opportunities for Product Leadership)

1. Deployment & Integration (time-to-value anxiety)

Fear: “This will take forever, break our ERP or S2P platform, and ROI slips into next year.”

Product reality:
Deployment and integration are two sides of the same coin. Buyers expect rapid deployment — configuration that delivers visible ROI in weeks, not quarters. Long rollouts or heavy customization are immediate red flags. At the same time, integration into ERP, S2P suites, supplier networks, and workflows is no longer optional — it’s the baseline expectation.

But integration does not mean blindly automating every existing process. Many enterprise workflows are overly complex or outdated. Simply wiring AI into them risks enshrining inefficiency. The real opportunity is to deploy fast, integrate seamlessly, and reimagine where AI can collapse steps, simplify approvals, or streamline supplier interactions.

Takeaway:
Products that prove quick deployment while fitting into core enterprise environments while also challenging outdated workflows give buyers confidence that AI won’t just bolt onto their world, it will make it better.

2. Support & Success Model (black-box anxiety)

Fear: “Once it’s live, we’re on our own — who co-owns outcomes when things go sideways?”

Product reality: Buyers don’t want “fire-and-forget” automation. They expect providers to stand behind outcomes with clear success models. That means: transparent SLAs, structured onboarding, and co-pilot frameworks that reassure customers they won’t be abandoned post-go-live. Analyst research ties AI adoption success directly to robust customer success programs, not just software delivery.

Takeaway: Products that bake in success models — clear ownership of outcomes, proactive monitoring, and transparent SLAs — shift from selling software to delivering sustained value. This builds trust and accelerates adoption.

3. Governance & Adaptability (obsolescence anxiety)

Fear: “Will agents keep up with policy changes, compliance requirements, and supplier dynamics — or become stale and risky?”

Product reality: Regulations, supplier behaviors, and internal policies evolve constantly. Agents that can’t adapt quickly create risk rather than resilience. To build trust, products must go beyond adaptability and make agent decisions auditable and explainable — with clear logs, transparent reasoning, and update pathways. Embedding human-in-the-loop oversight for high-impact decisions ensures accountability while allowing low-risk processes to flow autonomously.

Takeaway: Products that combine adaptability, auditability, explainability, and human accountability transform governance from a blocker into a differentiator. Governance becomes a visible advantage that reassures buyers, accelerates adoption, and sustains long-term trust.

4. Community, Training & Adoption (team trust anxiety)

Fear: “My team won’t adopt it; we lack skills and trust.”

Product reality: Even the best AI products fail if teams don’t use them. Research shows adoption depends less on technical capability and more on execution, skills, and trust-building. Without confidence and training, teams hesitate — slowing ROI and creating resistance to change.

Takeaway: Products that embed training, peer communities, and intuitive adoption pathways create confidence. When users see peers succeeding and feel supported — not replaced — adoption accelerates. This turns AI from a threatening tool into a trusted enabler.

Product Strategy Checklist: Avoid These Pitfalls

Before committing to market, product leaders should test themselves against these questions:

Deployment & Integration

  • Have we built for ERP, S2P, and supplier network integration from day one?
  • Can we demonstrate fast deployment and time-to-value without breaking existing environments?
  • Are we integrating intelligently, not just replicating inefficient workflows?

Support & Success Model

  • Do we provide a clear co-pilot framework and outcome co-ownership model that reassures buyers we won’t abandon them post go-live?
  • Are our SLAs and success models transparent enough to give buyers confidence in long-term support?
  • Is our product positioned to deliver outcomes, not just software?

Governance & Adaptability

  • Have we embedded auditability and explainability so agent actions are visible and accountable?
  • Is there a mechanism for continuous adaptation to policy, compliance, and supplier changes?
  • Do we enable human oversight for high-impact decisions while allowing low-risk processes to flow autonomously?

Community, Training & Adoption

  • Do we offer embedded training and peer communities that encourage adoption?
  • Have we created trust-building pathways that make teams feel supported, not replaced?
  • Can we show clear adoption success stories that prove teams will use — and value — the product?

From Features to Trust: The Real Edge in Crowded Markets

The solution providers who answer “yes” to these questions aren’t just avoiding pitfalls — they are building the complete solutions procurement leaders are urgently looking for. In an era of compressed adoption cycles, solving for these anxieties as part of a holistic product strategy is what will separate those who scale from those who stall.

And that’s where the competitive edge emerges. Features can be copied, but the ability to combine integration, governance, support, and adoption into a trusted solution is what allows a product to truly stand apart from the competition in a crowded market.

Great product strategy isn’t about chasing the next feature. It’s about building solutions that earn trust, drive adoption, and stand apart in crowded markets. At Liberis Consulting, we’ve done this before, and we know how to help growing companies avoid the pitfalls and scale with confidence. Let’s do it together.