For the better part of a decade, “improving the customer experience” meant hiring a UX consultant, redrawing a journey map, or A/B testing a button color. The assumption was always the same: a human being was on the other end, clicking through your website, filling out your form, waiting on hold. Design it better, and they’d feel it.

That assumption is breaking down. Fast.

Shopify merchants can now set up their product catalog once and sell inside a ChatGPT conversation — no separate integration, no custom app, no additional storefront. The buyer didn’t navigate to a website. An AI agent surfaced the product, answered their questions, and completed the transaction inside the conversation they were already having. Shopify and Google co-developed an open standard — the Universal Commerce Protocol — so that agents across platforms can natively complete checkout on a customer’s behalf. Hundreds of millions of ChatGPT users can now shop from Shopify merchants without ever leaving the chat window. That’s not a better checkout experience. That’s a different model entirely. The experience didn’t get redesigned. It got executed by something that never sleeps, never gets impatient, and never forgets what you told it last time.

The B2C proof of concept is already live. B2B is next.

What Shopify demonstrated in retail, agents are now building toward in procurement. Forrester predicts that buyers’ procurement teams will deploy agents capable of scaling negotiation across hundreds of suppliers simultaneously — turning static pricing pages into dynamic, real-time negotiation interfaces. A mid-market manufacturer’s purchasing manager won’t manually compare three vendor quotes and follow up by email. Their agent will evaluate all three, score them against pre-defined criteria, and initiate the next step — potentially before your sales rep has even opened their laptop.

This is where the conversation gets real for revenue leaders. If an AI agent is evaluating your quote response on behalf of the buyer, your 48-hour turnaround and a PDF attachment aren’t a minor inconvenience. They’re a disqualification. AI agents don’t call to clarify missing information or give you the benefit of the doubt on a vague line item. They move to the next supplier with complete, structured, machine-readable data. The experience your buyer has isn’t just shaped by your sales team anymore — it’s shaped by whether your systems can respond to agents at the speed and structure they require.

The scaffolding is being built in real time.

Three things happened in the last few months that, taken together, signal how fast this infrastructure is moving.

Zapier’s CEO announced the open beta of the Zapier SDK on April 7th, calling it the most powerful thing the company has shipped in years. Point your coding agent at it and it gains programmatic access to 8,000+ app integrations — Salesforce, Slack, NetSuite, you name it — without a single OAuth setup. Agents can now do things across your entire software stack through a single, standardized interface. The integration complexity that used to require months of development work is becoming a configuration problem.

Anthropic launched Claude Cowork in January, extending the same agentic architecture that powers Claude Code to everyday knowledge workers. It runs on desktop, works inside your local files and applications, synthesizes information across sources, and completes multi-step tasks without the user managing each step. In March, they added full computer use — Claude can now open apps, fill spreadsheets, export files, and complete workflows autonomously while you’re in a meeting. One demo showed a user running late, messaging Claude from their phone, and Claude exporting a pitch deck as a PDF and attaching it to a meeting invite before they walked in the door. That’s not a productivity feature. That’s a new category of coworker.

What connects all of this — Shopify’s agentic storefronts, Zapier’s SDK, Claude Cowork — is that the tooling for agent-mediated experience is now available to anyone. The barrier isn’t technology anymore. It’s whether your underlying systems are ready to participate.

The infrastructure problem hiding in plain sight.

Here’s the part most companies aren’t talking about yet: the companies that will win in an agent-mediated world aren’t the ones with the best-designed experiences. They’re the ones whose data is clean, their pricing rules are structured, and their systems can respond to an automated request in real time.

For mid-market manufacturers and distributors, this is where the stakes are highest. Your product catalog, your CPQ configuration, your quote-to-cash process — these aren’t back-office concerns. When an AI agent is evaluating suppliers on behalf of a procurement team, your ability to surface accurate pricing, available inventory, and complete product specs automatically is your experience. A CPQ that runs on batch processing and exports to Excel isn’t just slow. It’s architecturally incompatible with how buyers will increasingly be buying.

The companies investing in clean revenue infrastructure today — structured product data, real-time pricing logic, connected quote-to-cash systems — are building the foundation that agents run on. Everyone else is hoping the old way holds long enough to figure it out later.

Design doesn’t disappear. It bifurcates.

None of this means great UX stops mattering. It means the canvas has split — and the highest-leverage design work looks different than it did three years ago.

There are now two distinct surfaces that need to be designed well, and they require completely different thinking. The first is agent-facing: structured data, clean product schemas, machine-readable pricing, brand voice captured in a knowledge base an agent can actually read and act on. Shopify’s agentic storefronts let merchants define exactly how their products, policies, and brand identity appear inside AI conversations. That’s a design problem. It just doesn’t look like a Figma file.

The second surface is where human-facing design gets more important, not less. When routine transactions get handled by agents, what’s left are the high-stakes, high-judgment moments — a complex configuration decision, a contract negotiation, an onboarding experience, a renewal conversation that could go either way. Those touchpoints need to be exceptional precisely because they’re now chosen interactions, not forced ones. The buyer who shows up in person, so to speak, is already past the commodity layer. They’re there because the decision matters. Mediocre UX at that moment is no longer forgivable because “the process is just clunky.” The clunky parts got automated. What remains is a direct reflection of your brand.

The companies that will define this era aren’t choosing between agent-readiness and great design. They’re doing both — and they understand that the two are now inseparable.

What this means right now.

The experience era isn’t ending. It’s being handed off — and bifurcated. Routine interactions are moving to agents. Human-facing moments are becoming rarer and more consequential. And your brand’s ability to show up in an agent-mediated conversation depends entirely on whether the underlying architecture is ready.

Better UX alone won’t save you if your data is a mess. But clean infrastructure without a point of view on the human moments you still own is just plumbing. The companies that win will have both — and they’ll have started building before most of their competitors realized the canvas had changed.

Most companies manage outcomes directly.

Revenue. Pipeline. Retention. Growth.

They set targets, track metrics, and push teams to perform.

But outcomes don’t behave that way.

They’re not controlled.

They’re produced.

Outcomes Are Downstream

Every result in your business is a reflection of how customers experience it.

• Pipeline quality reflects the buying experience

• Win rates reflect the sales experience

• Retention reflects the customer experience

When those experiences improve, outcomes follow.

When they don’t, no amount of internal pressure changes the result.

Where Things Break

Most organizations are not designed around experience.

They’re designed around functions:

• Marketing

• Sales

• Customer Success

• Operations

Each optimized independently.

But customers don’t experience functions.

They experience the gaps between them.

That’s where friction shows up:

• Inconsistent messaging

• Slow handoffs

• Confusing processes

• Delayed value

And that friction shows up later… as missed outcomes.

AI Is Compressing the Gap

AI is accelerating every part of the business:

• Faster outreach

• More content

• More automation

• More interaction

But speed doesn’t fix experience.

It exposes it.

If the experience is fragmented, AI makes it more visible—and more frequent.

If the experience is aligned, AI makes it more effective.

AI doesn’t change the system.

It amplifies it.

What Actually Drives Results

The shift is subtle, but important:

Stop managing outcomes as targets.
Start managing the experiences that produce them.

• How easy is it to engage?

• How consistent is the journey?

• How clear is the value?

• How quickly do customers see results?

These are not soft concepts.

They are the system behind every hard metric.

Closing Thought

You don’t improve outcomes by focusing on outcomes.

You improve outcomes by improving the experiences that produce them.

Because in the end, results aren’t managed.

They’re experienced.

Most organizations agree on outcomes.

Grow revenue. Improve retention. Increase efficiency.

But agreement isn’t the problem.

Execution is.

Because while outcomes are discussed at the top, most of the organization is still operating on activity. And when that happens, even the right strategy breaks down in practice.

Why Outcomes Break Down

There are a few consistent patterns:

• Goals are too high-level to act on

• Teams are measured on activity, not impact

• Marketing, sales, and customer teams operate in silos

• No one clearly owns the outcome

So work expands. Activity increases. But results stay flat.

The Outcome Cascade

Organizations that consistently deliver results do something different. They connect strategy to execution through a clear chain:

Business Outcome → Driver Metrics → Activities → Systems

For example:

• Outcome: Increase net new revenue

• Driver Metrics: Pipeline coverage, win rate, deal velocity

• Activities: Targeting, messaging, sales execution

• Systems: CRM, automation, reporting

If these layers aren’t tightly aligned, teams default back to what’s easy to measure—activity.

And the rocking horse shows up again.

What to Measure (and What to Ignore)

The shift is simple, but not easy:

Move from volume metrics to impact metrics.

• Not emails sent → qualified pipeline generated

• Not meetings booked → opportunities advanced

• Not campaigns launched → revenue influenced

Activity can support outcomes. But it should never be mistaken for one.

Where AI Fits

AI is a force multiplier.

But it multiplies whatever system it’s applied to.

If your operating model is aligned to outcomes, AI accelerates performance.

If it’s not, AI just produces more activity—faster.

More content. More outreach. More analysis.

Same results.

AI should accelerate a defined strategy, not replace one.

Outcomes Are Delivered Through Experiences

Outcomes don’t happen in isolation.

They are the result of how customers experience your business across every interaction—marketing, sales, onboarding, delivery, and support.

• Pipeline quality is shaped by the buying experience

• Win rates are influenced by the sales experience

• Retention is driven by the customer experience

When experiences are fragmented, outcomes suffer.

When experiences are intentional and aligned, outcomes compound.

Many organizations try to improve results by optimizing internal activity instead of improving the external experience that drives those results.

The Role of Advisory

This is where most organizations get stuck.

They don’t need more effort. They need alignment.

• Translating business outcomes into measurable drivers

• Aligning teams around shared metrics

• Identifying what to stop doing

• Ensuring systems support outcomes—not just activity

• Designing experiences that consistently produce results

That’s the difference between motion and progress.

Closing Thought

Most organizations don’t fall short because they lack effort.

They fall short because their effort isn’t connected to outcomes.

The companies that win aren’t the busiest.

They’re the most aligned—around the outcomes they want to drive and the experiences that deliver them.

That’s going to be our focus / theme next month, “Experiences!”

Your Salesforce AE probably made it sound urgent. Maybe alarming. And if you’ve been searching for what the Salesforce CPQ end of sale actually means — not the spin, not the pitch — you’ve likely found the same thing we have: a wave of blog posts from Salesforce partners explaining why Revenue Cloud Advanced is your obvious next step.

This isn’t one of those posts.

We implement both Salesforce and ServiceNow. We don’t have a platform to sell you. What we do have is a clear-eyed view of what’s happening in the market right now — and the honest read is that the CPQ end-of-sale announcement landed in the middle of a genuine strategic crossroads.

Not a migration project. A revenue architecture decision.

What “End of Sale” Actually Means for Salesforce CPQ

On March 27, 2025, Salesforce officially moved CPQ to End of Sale status. That phrase does specific work — and it’s worth being precise about it.

End of Sale does not mean your CPQ stops working tomorrow. It does not mean your contracts are void. What it means: Salesforce is no longer selling new CPQ licenses. No new features are being built. The product is in maintenance mode.

Renewals are a different question. Salesforce AEs are currently quoting August 2026 as the end of renewal window — that’s the community signal, not an official Salesforce statement, and it’s worth verifying against your actual contract terms. But the directional message is consistent: the runway is shorter than you think.

Here’s the part people miss on both ends of the spectrum.

Some teams hear “end of sale” and panic. They’re ready to rip and replace before they understand what they’re replacing it with. Others hear it and shrug — “CPQ still works, we’ll deal with it later.” Both responses miss the real issue. The problem with staying put isn’t that CPQ breaks. It’s that CPQ becomes a ceiling. Companies remaining on Legacy CPQ are locked out of Agentforce integration. That’s not a minor limitation in 2026. That’s a strategic constraint on where your revenue operations can go.

The clock is ticking. It’s just not the fire alarm everyone is treating it as.

The Salesforce Path: Real, But Honest

If you’re a pure Salesforce shop, the natural path is Revenue Cloud Advanced — or, in the latest naming iteration, Agentforce Revenue Management (ARM). Let’s be direct about both what’s compelling and what isn’t.

What’s compelling: ARM is architecturally built into the Salesforce core. It’s not an appended product. It’s native. That matters for data integrity, and it matters significantly for Agentforce readiness. If your long-term roadmap involves AI-assisted quoting, contract intelligence, or autonomous revenue workflows, you need a quoting layer that can actually participate in those architectures. ARM is designed to do that.

What’s less clean: the naming alone should give you pause. This product went from Revenue Lifecycle Management to Revenue Cloud Advanced to Agentforce Revenue Management in short succession. That kind of naming churn usually reflects something real happening in the product — prioritization shifts, capability consolidation, strategic repositioning. It doesn’t mean avoid it. It means go in with clear eyes.

The architecture itself represents a genuine shift. Salesforce CPQ was rule-based. ARM runs on constraint-based logic. The migration isn’t just a data migration. It’s a re-engineering of how your pricing and quoting logic works. For orgs with complex configurations — and most enterprise CPQ orgs have more undocumented logic than documented — that’s a significant undertaking.

Implementation timelines are running longer than partners are currently quoting. That’s a pattern we’re seeing consistently.

We have successfully deployed 2 projects!

The Market Context Nobody Is Writing About

Here is what is completely absent from the conversation happening in the Salesforce ecosystem right now.

On April 3, 2025 — one week after the CPQ end-of-sale announcement — ServiceNow acquired Logik.ai, one of the more capable AI-native CPQ platforms in the market. In October 2025, ServiceNow launched its own CPQ product built on that acquisition.

That timing is not coincidental. ServiceNow watched the CPQ market open up and moved immediately.

This doesn’t mean ServiceNow CPQ is the right answer for your organization. It launched six months ago. It’s not fully mature. We’re not here to pitch it. But if you run both Salesforce and ServiceNow — or if you’re one of the organizations actively questioning whether your Lead-to-Cash stack should consolidate onto a single platform — the conversation looks different than it did eighteen months ago.

The CPQ market just opened up. There is now more than one credible path.

Every other post you’re reading right now was written by someone who has already picked a side. Their answer is already determined before they start writing. We haven’t picked a side, and that’s not a weakness — it’s the only way to give you advice worth taking.

The Lead-to-Cash Question Everyone Is Skipping

Here’s what the CPQ-focused conversation consistently misses.

CPQ is not a standalone tool. It sits inside a process. Quoting touches pricing approvals, order management, billing, ERP, and fulfillment. It’s the middle of the revenue motion — upstream from legal, downstream from pipeline, and connected to systems that may not be on your Salesforce AE’s radar at all.

When organizations treat an EOS event as a migration project, they pick a new quoting tool and move the configurations over. The L2C process itself doesn’t get examined. And that’s exactly where migrations create lasting risk.

A CPQ migration that doesn’t account for what happens to contract terms downstream, how pricing changes flow to billing, or how orders connect to fulfillment isn’t a modernization. It’s a lateral move with fresh technical debt.

More than 70% of CPQ logic in enterprise orgs is undocumented. That’s not an exaggeration — it’s a pattern that’s been confirmed consistently by teams doing CPQ discovery work across the industry. When you’re scoping a migration, you’re scoping a process that most of the people involved haven’t fully mapped.

This is where an advisor who knows the full stack earns their fee. Not by picking the right tool. By helping you understand what the right question actually is before you start.

How to Frame the Decision

The wrong question: which CPQ tool should we migrate to?

The right question: what does our Lead-to-Cash architecture need to look like in three years — and what’s the cleanest path to get there?

Here’s how we think about it for three common org types:

Pure Salesforce shop. Your data lives in Salesforce. Your teams are trained on Salesforce. Your future roadmap includes AI-assisted workflows. For you, ARM is probably the right destination — but the timeline and complexity of getting there is likely being undersold. Start the discovery process now. Map what’s documented and what isn’t. Don’t let urgency push you into a scoping conversation before you understand what you actually have.

Salesforce + ServiceNow. You’re running both platforms, and there’s a real question about where L2C should live long-term. ServiceNow CPQ is new enough that you shouldn’t treat it as a proven enterprise solution — but you also shouldn’t ignore it. The acquisition of Logik.ai was a signal about where ServiceNow is investing. If you’re already consolidating workflows onto ServiceNow, that conversation is worth having before you lock into a Salesforce migration.

Overbuilt CPQ org needing a reset. Your CPQ configuration has accumulated ten years of band-aids. Nobody knows why half the rules exist. Your quoting process is slower than it should be, and everyone knows it. For you, this EOS moment is actually an opportunity — but only if you treat it as a chance to redesign, not just re-platform. The goal isn’t to move your complexity to a new system. It’s to build something cleaner.

Mountain Point’s Read

Here’s what we’d tell a client in a kickoff meeting.

Don’t let the end-of-sale timeline drive a rushed decision. The urgency is real — you have a finite window before staying on Legacy CPQ starts closing doors — but not so real that you should skip the architecture conversation.

Understand your current state before you scope a future state. If you don’t know what logic is documented and what isn’t, any timeline you’re given is a guess.

Be honest about which platforms you’re actually running. If you’re on both Salesforce and ServiceNow, the answer to “where does CPQ live?” is a revenue architecture question, not a feature comparison exercise.

And be skeptical of any partner who gives you an implementation timeline without first doing the discovery or a Proof of Concept. This work takes as long as it takes to do correctly. The partners who are telling you otherwise are either inexperienced with ARM or optimistic for reasons that serve them.

The CPQ end of sale is not the end of the world. It’s a forcing function. Used right, it’s an opportunity to get cleaner about your revenue process than you’ve ever been.

That’s the work. We’re here if you want to do it.

Frequently Asked Questions

Is Salesforce CPQ dead?

Not dead — but sunsetted. Salesforce CPQ moved to End of Sale on March 27, 2025, which means no new licenses and no new features. Existing CPQ customers can continue running it through the end of their renewal windows, with community signals suggesting August 2026 as the likely cutoff for renewals. The more meaningful issue: organizations staying on Legacy CPQ are locked out of Agentforce integration, which makes it a strategic ceiling, not just a technical liability.

What is Agentforce Revenue Management?

Agentforce Revenue Management (ARM) is Salesforce’s current name for its next-generation CPQ and revenue platform. It was previously called Revenue Cloud Advanced (RCA) and before that Revenue Lifecycle Management (RLM). It’s architecturally native to Salesforce core — designed to support AI-assisted quoting and revenue workflows through Agentforce. No certifications for ARM currently exist; implementation experience is the only reliable qualification signal when evaluating partners.

There’s a concept in investing called the barbell strategy. Instead of putting your money in the safe middle - bonds, balanced funds, moderate-risk portfolios - you split your bets to the extremes. Ultra-safe on one end. High-risk, high-reward on the other. Nothing in between.

The logic is counterintuitive but sound: the middle isn’t safe. It’s where you get the worst of both worlds - not enough upside to matter, not enough protection to survive a shock.

I’ve been thinking about how this applies to lead-to-cash processes, and specifically to CPQ. Because most companies aren’t failing because they picked the wrong technology. They’re failing because they’re living in the middle.

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One End of the Bar: Ruthless Standardization

The left side of the barbell is everything that should run without human judgment. Not could run without it - should. These are your repeatable, predictable, low-variance transactions. And if a human is touching them, you have a process problem, not a staffing problem.

For manufacturers and distributors, this typically means:

A clean product catalog. SKUs, bundles, pricing tiers - locked down, validated, non-negotiable at the point of quoting. If reps are building quotes with the wrong product codes or manually calculating volume discounts, the catalog is broken.

Guided selling that constrains, not just assists. A good configurator doesn’t just help reps find the right product. It makes it impossible to build an invalid configuration in the first place. No exceptions. No “just this once.”

Rules-based approvals below the threshold. Set your discount floors, your margin minimums, your approval triggers - and then automate everything underneath them. If a quote meets the criteria, it should never touch a human inbox.

Contract templates that don’t require legal review. Pre-approved language. Auto-populated fields. The goal is a quote that becomes a contract that becomes an order without anyone retyping anything.

Clean ERP write-back. Order data flows downstream automatically. No manual handoff. No email to the operations team with a PDF attachment.

The goal on this end of the bar is zero friction, zero variance, maximum throughput. Every dollar you invest here pays compounding returns - because every standard deal that runs cleanly is a deal your team didn’t have to touch.

The Other End: Disproportionate Investment in What’s Complex

The right side of the barbell is the deals that genuinely don’t fit the mold. And here, the strategy flips entirely. Don’t automate. Don’t constrain. Invest heavily.

For most mid-market manufacturers, this is a smaller percentage of deals - but often a disproportionate share of revenue. These are your engineered-to-order configurations, your multi-year strategic agreements, your named accounts with custom commercial structures.

These deals deserve:

A real deal desk. Not a queue. Not a shared inbox. Dedicated people who understand the margin implications, the precedent risks, the relationship dynamics - and who are empowered to structure something non-standard with intention.

CPQ as a workspace, not a cage. On this end of the bar, the system should support flexibility. Custom line items. Bespoke pricing logic. Scenario modeling. The configurator gets out of the way.

Legal and finance engaged early. Not as a last-minute checkpoint before signature, but as architects of the deal structure. The handoff from sales to legal shouldn’t be a bottleneck - it should be a collaboration.

Executive alignment. For relationships that reshape the account, get senior people in the room early. This isn’t overhead. It’s leverage.

The goal on this end of the bar isn’t speed. It’s win rate and deal quality.

The Middle Is Where Deals Go to Die

Here’s the problem: most CPQ implementations - and most lead-to-cash processes - live in neither place. They’re in the middle. And the middle is expensive.

You know you’re in the middle when:

• Your CPQ is “mostly automated” but reps still have to intervene to fix something before sending a quote

• Every deal touches an approval chain, regardless of size or complexity

• Your configurator is technically flexible but so complicated that reps have built their own quoting process in a spreadsheet

• Your deal desk reviews everything, not because they need to, but because no one ever defined what doesn’t need review

• Legal sees contracts for the first time at the finish line

This is the rocking horse problem: motion that looks like progress, but isn’t. The system runs, the process moves, deals get done - but you’re burning cycle time and human attention on transactions that should be touchless, while simultaneously under-resourcing the complex deals that actually require judgment.

The middle isn’t neutral. It actively pulls resources away from both ends of the bar.

The Practical Question

The barbell strategy isn’t really a technology question. It’s a segmentation question.

Which deals belong on the left side of the bar - and what would it take to make them genuinely touchless? Which deals belong on the right - and are you actually investing in those the way they deserve?

Most companies have never explicitly answered either question. They’ve let process accumulate organically, and the result is a quote-to-cash motion that treats a $2,000 repeat order and a $2,000,000 custom engagement with roughly the same friction and roughly the same neglect.

The barbell strategy is a forcing function. It demands you sort your deal types, and then build differently for each end.

Once you do that, the CPQ conversation changes. You’re no longer asking “how do we configure the system?” You’re asking “what are we actually trying to automate, and what are we trying to enable?” Those are different questions - and they lead to much better answers.

Mountain Point helps design and implement revenue lifecycle processes that are built for both ends of the bar. If your CPQ is stuck in the middle, we should talk.

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You’re moving. You’re busy. The calendar is full. The dashboards are updating. The team is in motion.

But you’re not actually going anywhere.

A rocking horse is the perfect metaphor for a common problem in organizations: motion that looks like progress but produces no real outcome.

The Illusion of Progress

Most companies don’t struggle with effort. They struggle with alignment between effort and outcomes.

Consider how much time organizations spend on:

• Internal meetings

• Status updates

• Slide decks

• Reporting cycles

• Tool implementations

• Process discussions

All of these can be useful. But too often they become activity loops that create the feeling of productivity without producing meaningful results.

The business equivalent of rocking back and forth.

The uncomfortable truth is that activity is easy to measure.
Outcomes are harder.

It’s much simpler to track:

• Number of calls

• Number of emails

• Number of campaigns

• Number of meetings

• Number of dashboards

But none of those are outcomes.

Outcomes are things like:

• Revenue growth

• Pipeline quality

• Customer retention

• Shorter sales cycles

• Higher conversion rates

• Improved operational leverage

Outcomes change the trajectory of the business. Activity just burns energy.

When Tools Become Rocking Horses

This shows up frequently in technology and operations.

Companies invest heavily in systems - CRM platforms, marketing automation, analytics tools - expecting transformation. But tools don’t produce outcomes on their own.

Without the right strategy and operating model, tools often become very expensive rocking horses.

The dashboards look impressive.
The workflows are complex.
The reports are detailed.

But the core business metrics haven’t moved.

AI Is Making the Problem Bigger

Artificial intelligence is accelerating this pattern.

AI dramatically increases the speed and volume of activity an organization can produce:

• More content

• More emails

• More outreach

• More reporting

• More analysis

• More automation

But producing more activity faster does not automatically create better outcomes.

In fact, AI can unintentionally amplify rocking horse work. Teams can generate ten times the output without improving the underlying business results if they aren’t clear about what outcomes they’re actually trying to drive.

The risk isn’t that AI replaces work.

The risk is that it scales the wrong work.

The Outcome Question

A useful discipline for leadership teams is simple:

What business outcome is this work supposed to produce?

Before launching a project, implementing a tool, or redesigning a process, the answer should be clear.

For example:

• Are we increasing qualified pipeline?

• Are we improving forecast accuracy?

• Are we reducing customer churn?

• Are we enabling faster deal cycles?

• Are we scaling revenue without scaling headcount?

If the outcome isn’t clear, the work often drifts into activity for its own sake.

And the rocking horse starts moving again.

The Role of Advisory

This is where experienced advisory support becomes valuable.

Many organizations already have capable teams and strong technology. What they often lack is an external perspective focused purely on outcomes.

Advisory work is most effective when it centers on questions like:

• What business outcome actually matters here?

• Which activities directly drive that outcome?

• What should we stop doing?

• How do we align people, process, and technology around measurable results?

The goal isn’t to create more motion.

It’s to create direction.

Because the difference between activity and progress is simple:

Progress moves the business forward.

Rocking horses just keep you busy.

And in a world where AI can generate nearly unlimited activity, the organizations that win won’t be the ones doing the most work.

They’ll be the ones relentlessly focused on business outcomes.

If you’re leading growth, sales operations, or customer strategy inside your organization, it’s worth asking a hard question:

How much of our work is actually moving the business forward—and how much of it is just rocking the horse?

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We looked at where it hides — in the handoffs between people, in the gaps between systems, in the organizational habits that make execution slower and less predictable than it should be.

We talked about why CRM, CPQ, and Sales Order Management break down at the seams, not at the center. We mapped the compounding effect: one delayed quote becomes a missed quarter. One pricing workaround becomes margin erosion. One disconnected order becomes fulfillment rework.

And underneath all of it, the same pattern: most organizations aren’t losing revenue because demand is weak. They’re losing it because execution leaks.

That’s the friction story.

But friction isn’t the destination.

Removing Friction Is Means to an End — Not the End Itself

There’s a temptation to treat friction reduction as the goal. Fewer steps. Faster approvals. Cleaner handoffs. Tighter integrations. All of that is real work that creates real value.

But if you ask a VP of Revenue or a CFO what they’re actually trying to accomplish, it isn’t “less friction.” It’s outcomes.

Faster time to revenue. Higher win rates. Better margin performance. Forecast accuracy they can actually defend in a board meeting. A customer experience that drives renewal and expansion without heroics from the account team.

Friction is the obstacle. Outcomes are the destination.

The reason this distinction matters: organizations that focus only on reducing friction often end up optimizing processes that don’t move the needle on the outcomes that matter. They build cleaner workflows for transactions that aren’t creating value. They accelerate throughput on deals that shouldn’t be in the pipeline at all.

Removing friction without measuring outcomes is just making it easier to do the wrong things faster.

What Connected Revenue Execution Actually Produces

When the revenue lifecycle runs as a connected system, from opportunity through quote through order through fulfillment, something changes.

Not just operationally. Strategically.

Visibility improves before problems become losses. When CRM, CPQ, and order management share a single data model, leaders see where deals are actually stalling, where margin is actually being lost, and where fulfillment is actually breaking — in real time, not in the post-mortem. They stop managing lagging indicators and start acting on leading ones.

Revenue becomes predictable, not just reportable. The difference matters. Reportable revenue tells you what happened. Predictable revenue tells you what’s coming — with enough confidence to make decisions, commit resources, and set expectations that hold. That predictability only exists when the systems that generate revenue data are the same ones executing the process.

Growth becomes operational, not just aspirational. Most growth plans fail not because the strategy is wrong but because the operating model can’t absorb the volume. When execution relies on tribal knowledge, manual handoffs, and disconnected systems, adding pipeline doesn’t accelerate revenue — it amplifies chaos. Connected revenue execution creates the capacity to scale without a proportional increase in cost, headcount, or risk.

Customer experience becomes a competitive asset. For manufacturers, the buying and fulfillment experience is increasingly part of the product. Customers who receive accurate quotes quickly, whose orders execute cleanly, and whose issues are resolved without them having to call three people — those customers renew. They expand. They refer. The revenue lifecycle doesn’t end at the order. It either reinforces or erodes the relationship that drives the next one.

March: Outcomes

In March, we’re shifting the lens.

Not away from execution — execution remains the foundation. But toward the outcomes that execution should produce: the business results that justify the investment, earn the confidence of leadership, and make the next initiative easier to fund and implement.

We’ll look at how to measure the impact of revenue lifecycle improvements in terms that matter to the business. How to build the case for connected execution when the instinct is to fix one system at a time. How to know whether the changes you’ve made are actually moving the numbers that drive growth.

Friction tells you what’s broken. Outcomes tell you whether fixing it worked.

The organizations that win in 2026 won’t just be the ones that removed the most friction. They’ll be the ones that connected friction reduction to outcomes improvement — and built the systems, the data, and the operating discipline to prove it.

That’s March.

Mountain Point helps mid-market manufacturers and distributors connect their revenue lifecycle — from CRM and CPQ to Sales Order Management — so execution is faster, more accurate, and built for growth. Let’s talk about what outcomes look like for your business.

Most organizations don’t lose revenue because demand disappears. They lose revenue because execution breaks down between the moment a buyer engages and the moment revenue is realized.

That breakdown is friction.

The Revenue Lifecycle Is Only as Strong as Its Handoffs

The revenue lifecycle is not one function’s responsibility. It spans:

• Marketing engagement

• Sales qualification

• Product configuration and pricing

• Approvals and contracting

• Order capture

• Fulfillment and delivery

• Renewals and expansion

Each stage depends on clean handoffs between teams, systems, and information. When those handoffs introduce delay, rework, or inconsistency, revenue slows down.

Friction doesn’t just create inefficiency. It creates revenue leakage.

More on Lead to Cash

People Friction: When Execution Depends on Tribal Knowledge

In many organizations, critical steps in the revenue process live in someone’s head:

• Which discount requires approval

• How to structure a complex bundle

• Who owns the next step after a quote is sent

• How to resolve an order exception

When knowledge is informal, outcomes become inconsistent. New reps struggle to ramp. Deal cycles vary widely. Customers experience different levels of responsiveness depending on who they talk to.

Revenue becomes dependent on heroics rather than process.

The result is slower execution, higher risk, and limited scalability.

Process Friction: When Workflows Aren’t Designed for Speed or Control

Revenue processes often evolve organically. Over time, they become layered with exceptions:

• Manual approvals

• Offline pricing decisions

• Unclear ownership between teams

• Redundant steps to “be safe”

What started as flexibility becomes complexity.

Instead of accelerating deals, the process creates bottlenecks:

• Quotes stall waiting for review

• Orders are delayed due to missing data

• Renewals slip because no workflow triggers action

Process friction increases cycle time and reduces predictability — especially as volume grows.

Systems Friction: When Revenue Runs Across Disconnected Platforms

Most revenue organizations operate across multiple systems:

• CRM for pipeline

• CPQ for quoting

• ERP for orders

• Billing for invoicing

• Support tools for service delivery

When these systems aren’t connected, teams are forced to bridge gaps manually:

• Re-entering order details

• Reconciling pricing differences

• Tracking fulfillment through email

• Updating customer status in multiple places

Every disconnected system introduces delay and error.

Instead of a revenue lifecycle, the organization operates as a set of isolated workflows.

Data Friction: When the Source of Truth Isn’t Trusted

Even with the right tools, revenue performance breaks down when data is inconsistent:

• Product rules differ across channels

• Pricing logic isn’t applied uniformly

• Customer records are duplicated

• Forecasts rely on incomplete activity data

When teams don’t trust the data, they stop using it. Decisions revert to spreadsheets and side conversations.

Data friction leads directly to:

• Poor forecasting

• Margin erosion

• Order fallout

• Inconsistent customer experience

Revenue becomes harder to measure — and harder to manage.

The Compounding Impact of Revenue Friction

Friction is rarely catastrophic in isolation. The real damage is cumulative.

A delayed quote becomes a missed quarter.
A pricing exception becomes margin erosion.
A disconnected order becomes fulfillment rework.
A lack of visibility becomes forecast risk.

Across the lifecycle, friction compounds into:

• Longer sales cycles

• Higher cost of selling

• Lower win rates

• Increased discounting

• Operational disruption

• Slower revenue realization

Revenue doesn’t just slow down. It becomes harder to scale.

Reducing Friction Requires Connecting the Revenue Lifecycle End to End

The organizations that outperform are the ones that treat revenue as a connected system, not a set of departmental tasks.

They focus on:

• Guided workflows instead of tribal knowledge

• Automated approvals instead of manual escalation

• Seamless quote-to-order execution instead of rekeying

• A single source of truth for products, pricing, and customer data

• Real-time visibility across sales, operations, and finance

• Analytics that identify bottlenecks before they become losses

Reducing friction isn’t about adding more tools. It’s about orchestrating work across the lifecycle.

Revenue Growth Is an Execution Problem

Most companies don’t need more pipeline. They need less friction.

The next stage of revenue performance comes from removing the hidden barriers between:

• People and process

• Systems and execution

• Data and decision-making

When the revenue lifecycle moves as one connected workflow, organizations close faster, fulfill cleaner, renew more predictably, and grow with control.

Revenue doesn’t just increase. It flows.

A CRM gets deployed. A CPQ engine gets layered in. An ERP handles order execution.

On paper, the stack looks complete. In reality, the revenue workflow fractures at every handoff — and each handoff introduces friction that compounds downstream.

The Pattern We See Over and Over

Sales builds a complex configuration in CPQ. Pricing requires exception approvals that live outside the system. The approved quote gets exported — sometimes as a PDF, sometimes a spreadsheet — and someone on the operations side re-enters it into the ERP.

Fulfillment discovers the configuration doesn’t match what was actually quoted. Engineering flags a constraint that CPQ never enforced. Finance reconciles an invoice against a quote that was manually revised after approval.

Each system did its job. The friction between them is where revenue breaks down.

This isn’t an edge case. For engineer-to-order and configure-to-order manufacturers, this is Tuesday.

The Downstream Damage is Financial

The symptoms are predictable, and they all trace back to the same root: friction at the handoffs between systems.

Deal velocity drops. Not because reps are slow, but because every complex quote requires manual checkpoints between systems that don’t share data. Each checkpoint is a friction point. Each friction point is time.

Discounting loses governance. When pricing approvals happen in email threads instead of a controlled workflow, margin protection becomes optional. The friction of getting a proper approval makes the workaround feel justified.

Margin erodes quietly. Misconfigurations, pricing overrides, and manual order entry errors compound. By the time finance catches it, the order has shipped. These aren’t mistakes — they’re the predictable result of processes that require humans to bridge gaps between systems.

Forecasts detach from operational reality. The CRM says the deal closed at $400K. The ERP says the order is $370K. Nobody knows which number is right until the quarter closes. Two systems, two versions of the truth, and no clean path between them.

This is an Architecture Problem, Not a Configuration Problem

Most organizations treat this as a tuning exercise. Tighter validation rules in CPQ. Better dashboards in CRM. More integration middleware between systems.

That doesn’t reduce friction. It just moves it.

CRM governs the opportunity. CPQ governs configuration and pricing. Sales Order Management governs execution and fulfillment. If those three functions are implemented as separate systems with bolted-on integrations, friction is structural. No amount of tuning eliminates it.

What manufacturing organizations actually need isn’t better configuration logic or another integration layer.

They need continuity — a single governed workflow from opportunity → quote → approval → order → fulfillment where data, rules, and approvals carry forward without manual re-entry or translation. The friction disappears when the handoffs do.

The Question Worth Asking

Before investing in another system upgrade or integration project, the more useful question is: where does data stop flowing and humans start translating?

Every point where that happens is friction. Every instance of friction is a place where revenue leaks, velocity slows, or accuracy degrades.

If you’re ready to pinpoint where data stops and humans start translating, Mountain Point can help you map the friction and build a cleaner path from opportunity to fulfillment. Let’s talk.

In January, we focused on meeting customers where they are.
Understanding context. Assessing readiness. Proving value before asking for commitment.

That work matters because it exposes a bigger truth many organizations are now facing:

Most deals don’t stall because the solution is wrong.
They stall because doing business is hard.

As we move into 2026, friction has quietly become one of the biggest competitive differentiators.

Friction shows up everywhere:

• Long evaluation cycles that exhaust internal teams

• Demos that look impressive but don’t answer real questions

• Decision-makers asked to take risk without proof

• Processes that add steps instead of removing uncertainty

None of that builds confidence. It creates hesitation.

Reducing friction doesn’t mean cutting corners or oversimplifying complex decisions. It means designing the buying experience with the same care we put into designing the solution itself.

It starts before the first demo.

When we slow down to understand where a customer actually is; what they’re trying to solve, what constraints they’re operating under, and what success really looks like - we eliminate entire loops of rework later. Advisory conversations aren’t about adding meetings; they’re about removing unnecessary ones.

The same is true for proof of concepts.

POCs aren’t friction. Uncertainty is.

When buyers can see something work in their environment, with their data, against their real use cases, decisions accelerate. Internal alignment improves. Risk moves from assumed to measured. That’s not delay—that’s momentum.

In 2026, the expectation is clear:
Make it easy to evaluate.
Make it easy to justify.
Make it easy to move forward.

The companies that win won’t be the ones shouting the loudest about innovation. They’ll be the ones quietly removing obstacles; simplifying decisions, reducing risk, and respecting the reality buyers operate in.

February is about reducing friction.

Not by pushing harder, but by designing better paths forward.

A few years ago, “the roadmap” was enough.

A demo.
A vision of what could be.
An art-of-the-possible presentation that assumed momentum, budget, and patience.

That’s not where most teams are now.

They’re carrying too much.

Years of software investments.
Long implementations that took more out of teams than expected.
Systems that technically work, but require constant care to keep moving.

So when new ideas show up—new tools, new capabilities, new use cases—the reaction isn’t excitement.

It’s skepticism.

Not because teams don’t believe in the idea.
Because they’ve already lived through the promise.

This is why proof of concepts matter more now than they used to.

Not as a checkbox.
Not as a sales step.
But as a way to reduce risk in an environment where trust has been stretched thin.

A POC says:
Show me, don’t sell me.

It acknowledges something important:

• Capacity is limited

• Patience is thinner

• The cost of being wrong is higher

Teams don’t want to hear how something could work in theory.
They want to see how it works in their system, with their data, under their constraints.

Good POCs don’t aim to impress.
They aim to orient.

They answer practical questions:

• Does this reduce or add complexity?

• Does it work with the data we actually have?

• Does it create clarity or just another thing to maintain?

In a year defined by carrying too much, that distinction matters.

POCs aren’t about acceleration.
They’re about confidence.

They give teams a way to move forward without committing to another long, heavy initiative.
They make progress visible early.
They turn abstract value into something tangible.

And just as importantly, they surface reality fast.

If something won’t work, a good POC reveals that quickly - before it becomes another system you’re responsible for supporting.

This is what “try before you commit” really means right now.

Not hesitation.
Discernment.

In a market shaped by constraint, proof is how teams protect their future capacity.

Because the goal isn’t to add what’s possible.
It’s to carry only what actually works.

Let’s build off of our previous post - January, The Month of Carrying Too Much

Most companies don’t enter January looking for a bold new vision.

They arrive carrying weight.

Years of software decisions.
Processes built for earlier versions of the business.
Systems that grew faster than clarity.
Teams expected to do more with less inside environments that were never simplified.

This is the reality many customers are in right now.

Which is why “meeting customers where they are” matters more than ever.

Meeting customers where they are doesn’t mean accepting everything as-is.
It means starting with reality instead of aspiration.

Too often, advisory work begins with future-state diagrams and roadmaps that assume capacity teams don’t have. When people are already overloaded, that approach adds pressure instead of creating progress.

Good advisory work in this moment looks different.

It starts by helping teams understand what they’re carrying:

• Which systems are load-bearing versus optional

• Where complexity is structural versus self-inflicted

• What work continues simply because no one ever had time to question it

Before recommending change, advisory should create orientation.

What season is this team in?
What constraints are real?
What kind of progress would actually feel like relief?

This matters now more than ever.

Companies want real ownership internally.
They want value from the tools they already pay for.
They want focused movement, not another long initiative.

That requires clarity before action.

Advisory isn’t about telling customers what they should do.
It’s about helping them decide what doesn’t need to happen next.

Sometimes that means slowing decisions down.
Sometimes it means narrowing scope.
Sometimes it means stabilizing what already exists before changing anything.

Meeting customers where they are means respecting the weight they’re carrying—and helping them carry less before asking them to move faster.

January isn’t the month for big plans.
It’s the month for honest assessment.

And advisory, done well, is how teams find their footing without adding more to the load.

January isn’t a fresh start anymore.
It’s the moment the bill comes due.

Over the past few years, companies invested heavily.
New platforms. More licenses. More tools meant to unlock growth.

Most of it was necessary.
Some of it worked.
A lot of it is still sitting there; expensive, underutilized, and quietly demanding attention.

Now the questions have changed.

Not “What should we buy?”
But “Why aren’t we getting more out of what we already have?”

This is where January feels heavy.

Deal cycles have slowed.
Implementations took longer than expected.
Teams are tired - from projects that never quite ended and systems that never fully stabilized.

RevOps didn’t just support this growth.
It absorbed it.

You filled gaps.
You stitched systems together.
You made things work while the business kept moving.

And now there’s a shift.

Leadership wants ownership back inside the company.
Less outsourcing. Fewer long, drawn-out implementations.
More expectation that internal teams can carry the system forward with targeted help, not dependency.

At the same time, expectations are rising.

Projects are supposed to move faster.
Complexity is supposed to feel simpler.
Value is supposed to show up early, not at the end of a six-month plan.

And layered on top of all of it: AI.

Everyone wants to understand where AI and agents fit.
What’s real. What’s noise. What’s safe to move on now.

All while the underlying data, the thing everything depends on, remains messy, fragmented, and under constant pressure.

This is the weight people are carrying into January.

Not one problem.
But the accumulation of many reasonable decisions made under different conditions.

This is why the old playbook doesn’t work anymore.

Big, waterfall-style initiatives feel risky.
Full rebuilds feel unrealistic.
Adding new tools feels irresponsible.

But doing nothing isn’t an option either.

January isn’t asking for a grand plan.
It’s asking for discernment.

What actually deserves attention right now?
What work creates visible progress instead of more surface area?
What complexity can be reduced without oversimplifying reality?

Doing more with less doesn’t mean moving faster.
It means choosing differently.

Shorter horizons.
Clear ownership.
Focused improvements that create confidence instead of exhaustion.

January is the month for naming the weight—not to assign blame, but to set direction.

Because the teams that find their footing this year won’t be the ones that rush to add what’s new.

They’ll be the ones that finally get real value out of what they’re already carrying.

It’s time to meet customers where they are at in their journey!

Most revenue problems don’t start with customers. They start internally—fragmented systems, manual workarounds, unclear ownership, and teams spending more time explaining numbers than acting on them. CPQ (Configure, Price, Quote) sits at the center of these issues, whether companies realize it or not.

When CPQ is implemented well, it becomes more than a quoting tool. It becomes the operational source of truth for how a company sells.

One Reality, One System

If Sales, Finance, Operations, and Customer Success all work from different versions of reality, execution slows to a crawl. Discounts don’t align, margins get questioned, and forecasts turn into debates.

A strong CPQ enforces one commercial reality:

• One definition of a product

• One pricing logic

• One discount policy

• One approval path

This doesn’t eliminate flexibility—it eliminates ambiguity. When everyone trusts the same system, decisions move faster because alignment is built in, not negotiated after the fact.

Visibility Before Control

Many organizations try to control pricing, discounting, or deal structure before they can clearly see what’s happening. That always fails.

CPQ creates visibility first:

• Who is discounting

• Why exceptions are happening

• Where margin is being lost

• Which products create friction in deals

Once visibility exists, control becomes a configuration choice—not a cultural battle. You can’t govern what you can’t see, and CPQ is where that visibility should live.

Traceability Equals Trust

When someone asks, “Why was this deal priced this way?” the answer should never require Slack archaeology or calendar invites.

Trust is built when every decision is traceable:

• Why a price changed

• Who approved a discount

• What rules applied at the time

• Which version of a quote the customer signed

CPQ provides an audit trail by design. That traceability reduces internal friction, speeds up approvals, and prevents second-guessing after the deal is closed. Finance trusts Sales more when the system explains the story without interpretation.

Manual Work = System Failure

Spreadsheets, copy-paste pricing, and “just this one exception” emails are not edge cases—they are signals of system failure.

Manual work means:

• The system doesn’t reflect reality

• Rules aren’t encoded

• Ownership isn’t clear

• The process can’t scale

CPQ should absorb complexity, not export it to humans. If people are routinely bypassing CPQ, the solution isn’t more training—it’s better design.

No Owner = No Progress

CPQ without ownership degrades fast. Products change, pricing evolves, and sales motions shift. If no one owns CPQ as a system, it becomes outdated the moment the business moves.

Effective organizations assign clear ownership:

• Someone accountable for pricing logic

• Someone responsible for product configuration

• Someone empowered to improve workflows

CPQ is not “set it and forget it.” It’s operational infrastructure. Without an owner, it becomes technical debt disguised as a tool.

Tools Must Lower Friction

Every additional click, approval, or workaround increases deal friction. Sales feels it first. Customers feel it last—but most.

Good CPQ design does the opposite:

• Fewer approvals for standard deals

• Faster quoting for common use cases

• Clear guardrails instead of rigid locks

The goal is not control for control’s sake. The goal is to make the right behavior the easiest behavior.

Meetings Resolve Gaps, Not Report Them

If meetings exist to reconcile numbers, explain pricing logic, or debate which quote is correct, the system has already failed.

CPQ should eliminate reporting meetings by answering questions upfront:

• What’s the price?

• What’s approved?

• What’s standard vs. exception?

Meetings should resolve genuine gaps—new products, new pricing strategies, edge cases—not re-litigate what the system should already know.

Customer Friction Reflects Internal Design

Customers don’t see your CPQ, but they experience its consequences:

• Slow quotes

• Inconsistent pricing

• Confusing proposals

• Last-minute changes

Every bit of customer friction maps directly to internal design choices. If quoting is painful internally, buying will be painful externally.

CPQ is where internal clarity turns into external confidence.

Final Thought

CPQ is not a sales tool. It’s a revenue operating system.

When designed correctly, it creates one reality, builds trust through traceability, eliminates manual work, and lowers friction for both teams and customers. When neglected, it becomes a bottleneck that exposes every organizational misalignment.

The question isn’t whether CPQ matters.
It’s whether your CPQ reflects how your business actually sells.

For years, the most valuable work inside companies has lived in people’s heads.
Not in systems. Not in documentation. Not in dashboards.

It lived in instincts. In experience. In knowing how to navigate exceptions, edge cases, and workarounds that were never written down.

That undocumented thinking became job security. It was how people differentiated themselves. It was how organizations functioned, quietly and imperfectly.

AI changes that.

From Tribal Knowledge to Explicit Instructions

AI does not understand nuance unless it can be expressed. It cannot act on instinct. It requires direction, structure, and context.

Which means the moment you introduce AI into a workflow, you are forced to answer a hard question:

Do you actually know how your business works?

Not how you think it works. Not how the policy document says it works. How it actually works when things go wrong, when data is missing, and when customers do something unexpected.

That knowledge has always existed. It was just scattered across Slack messages, side conversations, heroic employees, and “that one person who knows how this really works.”

AI does not eliminate that thinking. It surfaces it. And once it is surfaced, it is no longer protected by obscurity.

Job Security Used to Live in Ambiguity

For a long time, ambiguity was a form of leverage. If only you knew how to fix a certain problem or run a certain report or navigate a certain system, your value was unquestioned.

The problem is that ambiguity does not scale. And AI thrives on things that scale.

What used to make someone indispensable is now what makes a process un-automatable.

So when people say they are afraid AI will “take their job,” what they often mean is this:

They are afraid it will take ownership of what they used to hold privately.

AI Is Not Replacing Work. It Is Rewriting the Instructions

Every effective use of AI starts the same way. Someone has to stop and translate real-world work into clear, testable steps.

That is not a technical exercise. It is an operational one.

It requires people who deeply understand the business and can explain it precisely. What triggers an action. What exceptions matter. What data can be trusted. What decisions are subjective and which must be standardized.

In that sense, AI does not replace expertise. It demands that expertise finally be made explicit.

Why So Many AI Projects Stall

Most AI initiatives do not fail because the models are weak. They fail because the thinking is fuzzy.

Messy processes create messy training data. Messy training data creates untrustworthy outputs. And once people stop trusting the output, adoption dies quietly.

Organizations often mistake “having data” for “having understanding.” Those are not the same thing.

AI is ruthless about this distinction.

The Real Shift

We are moving from a world where value was created by what you knew to a world where value is created by what you can clearly explain.

The most important employees in the next decade will not be those who guard complexity. They will be the ones who have the discipline to simplify without losing meaning.

AI does not take your job.

It takes your undocumented thinking.

And in doing so, it gives you a choice. Hoard what you know and fight the tide, or translate it and help redesign how work actually happens.

Closing Tie-In

In a previous post, We’ve Been Writing in Cursive, and Now We Need to Print , I wrote about how AI is forcing organizations to make their work machine-readable.

This is the next layer of that shift.

Printing is not enough. Once your work is readable, it still has to be understandable. And that requires translating undocumented thinking into explicit instruction.

The companies that win will not be the ones with the most AI.
They will be the ones most willing to explain themselves clearly.

In 2026, organizations will face one of the most important transformation decisions of the decade: continue patching together disconnected revenue processes, or finally modernize Lead-to-Cash (L2C) from the ground up.

Over the past several years, most companies have accumulated a tangled web of CRM customizations, CPQ quick fixes, spreadsheet-driven approvals, and siloed operations. The downstream effects are now undeniable: slow quoting cycles, revenue leakage, frustrated sales teams, and operations leaders drowning in manual work.

But these issues aren’t just technical nuisances.
They represent a structural problem inside the business.

L2C spans IT, Sales, Finance, Operations, and Customer teams—yet most organizations still treat it as isolated systems.

The Shift: Lead-to-Cash Is Now a Cross-Platform, Cross-Team Discipline

For years, companies tried to solve every revenue challenge inside a single platform. But modern L2C problems don’t live in one system anymore—they live in the gaps between them.

• CRM manages pipeline

• CPQ manages configuration and pricing

• Digital workflows manage approvals and orchestration

• ERP manages orders and fulfillment

• RevOps, IT, Sales, and Finance all operate on different truths

The future belongs to organizations that acknowledge a new reality:

Lead-to-Cash is not a “system problem” — it’s an end-to-end operating model problem.

The companies that thrive will be those who redesign their L2C architecture, not just redeploy tools.

What’s Driving the Pressure in 2026?

Three big factors are converging:

1. Revenue Leakage Has Become More Visible — and More Costly

Disconnected quoting, inaccurate pricing, and manual handoffs are now measurable losses. CFOs are paying attention.

2. Buying Cycles Demand Speed and Precision

Customers expect real-time pricing, seamless order management, and clean execution.
Slow, inconsistent processes no longer scale.

3. Platforms Are Evolving Faster Than Processes

Innovations in CRM, CPQ, workflow orchestration, and ERP can’t succeed if the underlying process is still fragmented.

Technology is no longer the limiter.
Process clarity and cross-team alignment are.

Why 2026 Is the Year of L2C Modernization

The market is signaling a clear pattern:

• Businesses want clarity

A roadmap that makes sense, sequencing that reduces risk, and an operating model that aligns IT, RevOps, and Sales.

• Teams want empowerment

The ability to break down internal silos and fix root-cause issues, not surface symptoms.

• Leaders want outcomes

Shorter sales cycles, predictable quoting, lower operational cost, and better customer experience.

In short:
Companies don’t want more systems — they want a connected revenue engine.

What Modern L2C Excellence Looks Like

In talking with hundreds of leaders across IT, Sales Ops, and RevOps, a modern L2C organization has these four traits:

1. A clear L2C architecture

Roles, responsibilities, systems, and handoffs are documented and intentional.

2. A unified process across platforms

CRM, CPQ, ERP, and workflow tools operate as one ecosystem, not siloed components.

3. Advisory-first transformation

Teams diagnose process issues before committing to major implementations.

4. A phased improvement model

Organizations start with clarity, then execute targeted improvements, and finally optimize with ongoing support.

This is how teams eliminate guesswork and achieve consistent revenue execution.

The Mandate for 2026

Whether you are a CIO, IT Director, RevOps leader, or Sales Ops owner, the expectations have changed:

• Simplify processes.

• Align your systems around the customer journey.

• Design L2C as an intentional operating model.

• Invest in cross-platform orchestration, not siloed tooling.

• Build a roadmap your teams understand — and can execute.

2026 will reward organizations that break silos, streamline workflows, and treat Lead-to-Cash as a strategic discipline.

Those who continue patching the old model will fall behind.

Engineer to Order (ETO) companies run on expertise. Every estimate, design, and routing decision depends on someone who remembers how a similar job was handled in the past. That expertise is valuable, but it is also fragile. When tribal knowledge stays in people’s heads, an ETO business becomes difficult to scale, slow to train, and inconsistent in execution.

There is another way to look at this problem. Instead of trying to preserve tribal knowledge for its own sake, you can transform it into a reusable and configurable asset. Once that happens, engineering effort is focused where it truly matters and the rest of the business becomes more predictable.

The Reinvent the Wheel Problem

Most ETO organizations unintentionally often rebuild the same (or similar) solutions. Engineers recall old projects from memory instead of working from a consistent library of rules or patterns. The result is long quoting cycles, poor margin visibility, and repeated engineering hours spent on work that should be routine.

Tribal knowledge creates bottlenecks. If only one senior engineer knows a specific constraint or workaround, every similar order must wait in their queue. Lead times stretch and stress builds across sales, engineering, and operations. As long as knowledge stays tribal, these problems repeat project after project.

A Different Approach: Make Knowledge Configurable

The most scalable ETO companies treat tribal knowledge as raw material. They translate what their experts know into systems that can guide less experienced team members. The goal is not to eliminate engineering judgment. The goal is to automate everything that does not require new thinking.

You can start by capturing recurring patterns. What dimensions tend to drive cost? What combinations of materials cause issues? What options require special tooling? Once these rules are documented, they can be added to a configurator, a rules engine, or a structured template library inside your system of record.

This shift builds a foundation that supports more reliable quoting and more consistent early engineering decisions. A salesperson or project engineer can answer most of the routine questions with confidence because the expertise is already embedded in the system instead of buried in notebooks or memories.

Auto Generating the First 80 Percent

When tribal knowledge is converted into rules, something powerful becomes possible. The system can automatically generate the starting point for new projects. Early BOMs, routings, and design constraints can be created based on what the organization already knows works well.

This does not eliminate engineering involvement. It reduces time wasted on repeatable decisions, which frees senior engineers to focus on true edge cases and innovation. The result is faster quote turnaround, better accuracy, and fewer late surprises during manufacturing.

Continuous Improvement Becomes Easier

Once knowledge is digitized, it can be refined over time. Feedback from past jobs, field issues, NCRs, and margin reports can all loop back into the rules. You can see which configurations consistently overrun hours and which design choices create downstream delays. Updating the rule set or configurator becomes a structured process instead of an informal conversation.

This continuous improvement cycle creates a competitive advantage. Every project not only delivers value to the customer but also strengthens the digital knowledge base that supports future work.

The Vision: ETO That Scales

Transforming tribal knowledge into a configurable asset turns an ETO business into a scalable one. New employees come up to speed faster. Senior engineers spend more time on high impact work. Sales can quote accurately without waiting for experts. Manufacturing sees fewer surprises and fewer changes.

In other words, the business stops relying on heroics and starts relying on systems.

Digitizing tribal knowledge is not just an efficiency project. It is a strategic shift in how an ETO organization thinks about expertise and growth. Companies that make this transition position themselves to behave more like mass customizers, with the flexibility of custom engineering and the consistency of a repeatable process.

If you’ve ever lived inside an engineer-to-order sales cycle, you know the pain.
A customer sends an RFQ. You forward it to engineering. Weeks later, drawings come back, questions fly, things change, everyone scrambles.

That process isn’t broken, it’s just dated.

Customers no longer want to wait. They want to see what’s possible, tweak it, and get an answer now.

That’s why so many manufacturers are moving toward Design Labs - digital spaces where customers can explore, visualize, and configure products themselves.

Now there’s a new twist: AI is joining the equation. And it’s quietly changing how we sell, quote, and build.

The Design Lab, evolved

A Design Lab lets customers co-create instead of request.
They can spin a 3D model, adjust options, get pricing, and even generate drawings without sending an RFQ into the void.

But the next evolution is an AI-assisted Design Lab - one that doesn’t just show options, but thinks alongside the user.

Here’s what that looks like in practice:

• AI reads the spec. A customer uploads a drawing or a technical doc. The system scans it, extracts key parameters, and matches it against what you’ve built before.

• AI classifies the opportunity. Is this a repeat customer who bought a similar system? Then reuse that configuration. Is it something new? Route it to engineering with context already filled in.

• AI predicts feasibility. Before anyone touches CAD, the system can flag impossible combos or materials that will blow your lead time.

• AI suggests pricing. It compares the spec against your past quotes, materials, and margins, then recommends a range.

• AI learns. Every project feeds the next. Over time, the system becomes a library of proven designs, constraints, and patterns — turning tribal knowledge into searchable intelligence.

In short: you move from reacting to specs to understanding them instantly.

Why it matters

1. Speed. Customers get answers in hours, not weeks.

2. Focus. Engineers spend their time on real innovation, not near-duplicates.

3. Margin. AI flags risk and helps quote with confidence.

4. Experience. Customers feel guided, not gated.

And maybe most importantly: it builds trust.
When customers can visualize, configure, and get instant feedback, they stop seeing you as a black box. You become a partner.

How we help manufacturers get there

This shift isn’t just about technology, it’s about design thinking, process clarity, and cultural change.

Here’s how we guide companies through it:

1. Understand your data reality.
We start by auditing your specs, RFQs, and historical builds. How consistent are they? How searchable? The better the data, the better the AI.

2. Build a “minimum viable” Design Lab.
Pick one product family. Stand up a visual configurator + CPQ flow. Add a lightweight AI layer to classify specs and flag risk.

3. Connect the dots.
Integrate it with your CRM, CPQ, ERP, and PLM so the AI has full visibility into what you’ve sold, built, and delivered.

4. Measure the impact.
Track quote cycle time, engineering touches, rework rate, and customer satisfaction. Prove the value fast.

5. Scale and evolve.
Expand across product lines. Feed the AI new data. Refine rules. Watch your quoting and design velocity compound.

The bigger story

The best manufacturers aren’t automating to cut people, they’re automating to elevate them.
AI doesn’t replace engineers. It removes the noise so they can focus on what actually requires their expertise.

The winners will be the ones who combine human judgment and machine intelligence, turning RFQs into real-time collaboration.

That’s the future of ETO.
Not waiting for specs to arrive; but helping your customers design smarter, faster, and together.

Bottom line: If your RFQ queue feels endless, it’s time to stop waiting and start enabling.
AI won’t just read your specs… it’ll help you rewrite the playbook.

Most companies don’t fail at strategy. They fail at translation.

For years, we’ve built systems that made sense to us but not to the people or machines trying to use them.
Our data mirrored how we think - messy, contextual, full of shortcuts.
Our ideas lived in slides, conversations, and habits.

We built the modern enterprise in cursive.

AI as the Translator

AI isn’t a new pen. It’s the translator.
It’s taking decades of human shorthand and turning it into something machines can act on.

That process is forcing clarity.
It’s exposing how much of our work lives in the grey; half-defined, inconsistent, assumed.

AI can’t thrive there.
It learns from what’s written clearly and connected.

When Clarity Gets Uncomfortable

In my own business, we never had a perfect sales playbook.
We had strategy and intent, and we updated the CRM to reflect components.
But we still weren’t aligned.

It looked like cursive - familiar to some, confusing to others.
We saw the same in project delivery.
Change wasn’t the core issue. Shared understanding was.

That’s what AI is surfacing across every organization:
It’s not automating the old. It’s revealing what was never clear in the first place.

From Handwriting to Hyperlinking

Every major platform is building the same bridge.
Salesforce, ServiceNow, HubSpot, Microsoft, Google - all chasing comprehension to add value and increase outcomes.

The real transformation isn’t in the software.
It’s in us.

We’re learning to express knowledge so clearly that both humans and machines can act on it.

That’s the shift.
From handwriting to hyperlinking.
From cursive to clarity.

The Takeaway

Transformation used to mean digitizing what we did.
Now it means making what we mean understandable at scale.

AI won’t erase the grey.
It will help us navigate it better.

But only if we start printing what we’ve been writing in cursive.

Over the past decade, service organizations have operated in two main modes: projects and support. One offers structure and milestones but struggles to keep pace with change. The other offers flexibility but often lacks focus and accountability.

Neither model fully fits the way modern organizations want to work.

Executives today face tighter budgets, shorter timelines, and accelerating change from technologies like AI and data automation. They need to see progress quickly, but they also want their internal teams to be involved, learning, and ready to take ownership when the consultants roll off.

This reality is what’s driving many firms—including us—to rethink how we engage.

Why the old models are showing cracks

A project SOW can give clarity, but it often locks everyone into assumptions made months earlier. Change orders, delays, and scope debates can quickly drain energy. On the other end of the spectrum, a support agreement can keep work moving but rarely provides the direction or momentum a leadership team expects.

Neither structure accounts for the level of uncertainty most companies are navigating right now. When priorities shift weekly and AI is changing what’s possible every quarter, fixed-scope planning starts to look like a relic.

Clients aren’t rejecting accountability. They’re rejecting rigidity.

A third option: collaborative, outcome-driven frameworks

The answer isn’t to abandon structure. It’s to evolve it.

A collaborative, outcome-driven framework blends the best of both worlds. It keeps the guardrails of a project (defined time, budget, and outcomes) but allows priorities to evolve through shared decision-making. Instead of a waterfall of tasks, the engagement is organized around short cycles of discovery, delivery, and validation.

This model also recognizes something that’s been missing for years: the client’s own team is a critical part of the solution. Their involvement builds buy-in and ensures the work doesn’t die when the engagement ends. The goal is not to deliver for them, but to deliver with them—building capability and confidence along the way.

Why AI is forcing this change

AI is not just another tool. It’s a forcing function. It’s accelerating decision cycles and reducing the shelf life of assumptions.

In the past, you could plan a six-month project around a defined problem. Today, by the time that project launches, the technology landscape and internal expectations may have already shifted.

This new reality rewards teams that can experiment and validate fast, not those who over-engineer the plan.
A collaborative framework gives clients a way to do that safely: timeboxed, budget-controlled, and transparent.

Where this model works best

It’s not for everything.
Highly regulated, mission-critical implementations still need traditional rigor. But when the goal is to validate a concept, test new capabilities, or build internal alignment, this model excels.

It creates space for experimentation while keeping accountability intact.
It’s a structure for progress under uncertainty.

What success actually looks like

The outcome is not a perfectly finished product. It’s a validated foundation:

• A working prototype that proves feasibility.

• An internal team that knows how to extend it.

• A clear path for what should happen next.

When that happens, both sides win. The client gains clarity and capability. The partner earns trust and repeat business.

A closing thought

The consulting world doesn’t need another buzzword. It needs more honesty about what’s working and what isn’t.

Clients are tired of long timelines, endless documentation, and deliverables that don’t survive contact with reality. They want focus, adaptability, and shared ownership.

We’re evolving not because it’s trendy, but because the way value is created has fundamentally changed. Speed, collaboration, and learning now matter as much as scope and milestones.

The firms that understand that shift, and build frameworks to match, will be the ones still relevant five years from now.

Does this model align with what you’re looking for?

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