The most common question I got: “Okay, but which Tomorrow bets should we actually be making?”

Fair question. You can’t chase every shiny possibility, and most partnership teams are already stretched thin on their Today deals. You need a systematic way to identify and prioritize the Tomorrow opportunities worth your limited bandwidth.

Future Bets: What’s Changing That Could Reshape Partnerships?

The key question isn’t “What partnerships should we do?” It’s:

“What dynamics are changing that will fundamentally alter how commerce gets done?”

Here are the shifts I’m watching most closely — along with why they matter:

• AI-Native Shopping Experiences – Discovery, evaluation, and purchasing happen inside a single conversational interface. When someone asks an AI assistant “what’s the best camera for travel photography under $800?” and completes the purchase in the same conversation — you want your payment rails there.

• Creator Economy Platforms – Financing unlocks entirely new income streams and business models. The YouTuber with 2M subscribers launching a course, or the TikTok creator needing inventory financing for their dropshipping business.

• Cross-Border Commerce in Emerging Markets – Maturing regulatory frameworks in Southeast Asia, Latin America, and Africa are opening previously inaccessible audiences. Getting embedded early in these payment infrastructure plays could be massive.

• Gigification of White-Collar Work – As fractional executives, project-based specialists, and on-demand talent marketplaces expand, what partnerships will serve this new labor economy? Think payment rails for gig consultants or financing for freelancer equipment.

• B2B Software Buying Disruption – Will CRM and ERP sales be disintermediated by AI tools that source, vet, and integrate software automatically? If buying shifts from “demo → trial → negotiate → sign” to “query → auto-provision,” where does that leave traditional B2B partnerships?

• Fragmentation of the SaaS Ecosystem – As microtools proliferate, what infrastructure partners will stitch together payments, compliance, and analytics across dozens of point solutions?

The Size of Prize vs. Likelihood of Success Matrix

Even with a Tomorrow mindset, you can’t chase every possible future partnership. One way to prioritize is using the classic strategy consulting framework: Size of Prize vs. Likelihood of Success.

• Size of Prize = Potential revenue, market share, or strategic advantage if the bet succeeds

• Likelihood of Success = Confidence in timing, partner execution, and your ability to win

Here’s how it looks with current examples:

Quadrant Guidance

• Quick Wins (High Size, High Likelihood) – Prioritize early pilots and scale fast.
  Example: Cross-border commerce in proven regulated markets

• Big Swings (High Size, Low Likelihood) – Place lightweight bets to learn and position early.
  Example: Immersive commerce (VR/AR retail experiences)

• Safe Plays (Low Size, High Likelihood) – Use to complement larger initiatives, but keep investment modest.
  Example: Niche SaaS integrations with small TAM

• Nice-to-Have (Low Size, Low Likelihood) – Avoid or cut quickly.
  Example: Experimental pilots with no clear revenue path

How to Apply This Framework

1. Map your current Tomorrow bets across the four quadrants — be brutally honest about both dimensions.

2. Load up on Quick Wins while they’re still available.

3. Place 2–3 Big Swings with minimal upfront investment until you see tipping signals.

4. Use Safe Plays strategically to fill gaps, not as core bets.

5. Ruthlessly cut Nice-to-Haves that drain time without impact.

Starting Small: The Lightweight Bet Approach

Tomorrow partnerships don’t need to start as heavy lifts. Some of the best begin with:

• Limited product integration – One feature, one use case, minimal engineering time

• Co-marketing experiment – Joint content, shared webinar, cross-promotional email

• Data-sharing pilot – Small-scale analytics or insights exchange to test mutual value

• Sandbox partnership – Closed beta with select customers before broader rollout

The goal isn’t to ink a massive partnership contract on day one — it’s to learn, build relationships, and position yourself for when the market tips.

The Long Game

Your biggest competitive advantages often come from partnerships you start before anyone else sees the opportunity.

The companies winning at Tomorrow bets aren’t necessarily the smartest at picking winners — they’re the most systematic at placing small, smart bets across multiple possibilities, then scaling quickly when something shows clear momentum.

Your Track 1 partnerships fund these experiments. Your Track 2 partnerships position you for the next wave of Track 1 opportunities.

Which Tomorrow bets are you watching most closely? Have you used frameworks like the 2×2 matrix to prioritize partnership opportunities? Share your approach in the comments.

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If you’re finding this series helpful, hit subscribe. Next week: The metrics that actually matter for each track — and why most partnership teams are measuring the wrong things entirely.

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Why the best partnership leaders never choose between immediate wins and long-term bets

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Partnership leaders live in two time zones at once.

You're closing deals that drive this quarter's revenue while planting seeds for markets that won't mature for years. You're optimizing for today's performance while positioning for tomorrow's category leadership.

Most people see this as an impossible tension. The best partnership leaders see it as their competitive advantage. Geoffrey Moore calls it "surfing the wave and finding the next one before the first has crashed." In Zone to Win, he shows how market leaders organize around this exact challenge.

From Geoffrey Moore's Zones to the Two-Speed Mindset

In Zone to Win, Moore describes three organizational "zones" that must coexist for a company to grow and defend its market position:

Performance Zone – Maximize current revenue streams

Incubation Zone – Experiment in emerging markets to test new plays

Transformation Zone – Scale the next big thing when the market tips

Applied to partnerships, Moore's zones become our two-speed tracks:

Track 1: Performance Zone Partnerships ("Today")

Goal: Squeeze maximum value from current ecosystems

Large-scale, high-confidence deals that deliver measurable results in the next 6–12 months.

Examples: Regulated vertical platforms, high-volume distribution partners, enterprise commerce marketplaces

Tactics: Deepen integration, expand into new use cases, lock in exclusivity, co-market to drive adoption

Track 2: Incubation Zone Partnerships ("Tomorrow")

Goal: Plant seeds in platforms that could define the next wave

Small, strategic moves into markets that may not mature for 2–5 years.

Examples: AI-native shopping assistants, creator monetization platforms, cross-border fintech rails, immersive commerce

Tactics: Lightweight pilots, sandbox integrations, niche feature co-development, targeted A/B testing

Note: When a "Tomorrow" bet shows clear signs of tipping, you scale it rapidly (Moore's Transformation Zone) — but the core discipline remains two-speed thinking.

Your Twist: Make Every Partnership Two-Speed

Here's where most companies miss the mark — and where you can turn Moore's framework into everyday partnership execution.

It's not just about having a "Today Team" and a "Tomorrow Team." It's about embedding the Two-Speed Mindset into every single partner relationship, even your largest, most established accounts.

Every partner manager should be asking:

• What can we do together to drive more revenue today?

• What seeds can we plant now that might turn into a breakthrough in two years?

That way, even your most mature partnerships are compounding — delivering present-day performance while setting up future waves of growth.

Why It Works

Disciplined allocation – Moore's zones force you to consciously invest in both the urgent and the important.

Cultural habit – The Two-Speed Mindset turns it into repeatable behavior across the entire partnerships org.

Future-proofing – You never get caught flat-footed when the current wave starts to crest.

The Compound Effect

Run this model well and the tracks don't just coexist — they fuel each other.

Track 1 ("Today" partnerships) gives you the revenue, credibility, and execution muscle to place bigger bets.

Track 2 ("Tomorrow" partnerships) gives you early access to the ecosystems that will power your next generation of Track 1 deals.

Treat your partnership portfolio like an investment portfolio: some assets pay dividends now, others appreciate over time, and together they create a position far more resilient than either alone.

The Payoff

Partnerships aren't just about who you work with — they're about where and when you place your chips.

Some chips have to pay off now. Others should sit quietly in high-potential corners, compounding until the timing is perfect.

The companies that master this don't just ride the wave. They spot the next one, paddle early, and are already in position when it breaks.

How do you balance short-term wins with long-term bets in your partnerships? Have you embedded this thinking into your day-to-day partner management? Share your examples in the comments.

If this framework resonated, hit subscribe for more insights on building partnerships that actually move the needle. Next week: I'm diving into the specific "Tomorrow" bets worth considering right now — and the simple 2x2 framework for prioritizing which ones deserve your attention.

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They've got the cause of death wrong. The funnel didn't die a natural death—it was dismantled piece by piece and scattered across your partner ecosystem. Each piece still works brilliantly in isolation, but in enterprise B2B, no single company owns enough of the buyer journey to win alone anymore.

The only way to compete now is to stitch those fragments back together into what I call the networked funnel—a living, shared system where AI coordinates partner signals faster than any traditional quarterly business review could dream of.

The Misdiagnosis: Why "Funnel Death" Analysis Misses the Point

Here's what I see in partnership meetings across Silicon Valley: Everyone's treating the symptoms while ignoring the disease.

The symptoms: Longer sales cycles, more stakeholders, buyers ghost after demos, deals stall in procurement.

The common diagnosis: "The funnel is dead because buyers are more informed/impatient/demanding."

The real disease: Your buyer's journey now spans five different companies, but you're all acting like you're the only one in the relationship. It's like five world-class chefs trying to cook a seven-course meal in the same kitchen without talking to each other. Everyone's making something delicious, but the customer gets five appetizers and no main course.

The buyer experiences this as chaos. They're getting different value propositions, different timelines, different contact points, and zero coordination. By the time they reach a buying decision, they're exhausted from stitching together a coherent solution from fragmented vendor pitches.

The companies that win aren't the ones with the best individual funnel optimization. They're the ones who've figured out how to coordinate their piece of the journey with everyone else's pieces so seamlessly that the buyer experiences one integrated solution, not a vendor beauty contest.

The Real Problem: When Funnel Fragments Move at Manual Speed

Picture this scenario I watched play out last quarter: A cloud provider notices their fintech client just spun up the biggest ML cluster in company history. Meanwhile, a SaaS vendor sees that same account logging back into their dormant platform for the first time in months. Two massive buying signals pointing to the same seven-figure opportunity.

The gap: It takes two weeks for this intelligence to surface in a quarterly business review. By then, a faster competitor has already booked the demo, scoped the project, and started the POC process.

This is the brutal reality of enterprise B2B in 2025: Your buyers are getting AI-personalized everything except from your partnerships. While you're debating attribution in spreadsheets and scheduling quarterly syncs, your competitors are sharing live signals and coordinating responses within hours.

The delay isn't just about efficiency—it's about relevance. When your follow-up arrives a week after the buying signal, you're not just slow, you're obsolete. The buyer has already moved three steps ahead in their mental timeline.

The Solution in Action: Three Networked Funnels That Actually Work

Let me show you what happens when companies stop treating partnership coordination like an afterthought and start building it into their growth engine:

Case 1: When Real-Time Signals Turn Fragments into $14M Wins

At 9:00 AM on a Tuesday, a cloud AE gets a Slack notification that makes him put down his coffee: "Your fintech account just spun up its biggest ML cluster yet—and they're back in our app for the first time in months."

This isn't magic. It's what happens when a Fortune 500 SaaS vendor and a hyperscale cloud provider decided to stitch their funnel fragments together through AI-powered data matching. Both companies' usage data flows into shared models that identify coordinated buying signals in real-time, not quarterly cycles.

By 11:00 AM, they're on a joint discovery call—not because someone remembered to check a lead list, but because AI flagged this as a high-propensity moment and both teams were automatically notified. By Friday, they're running a unified proof of concept that shows the integrated workflow, not two disconnected demos competing for budget allocation.

Here's what I find fascinating about this play: It wasn't just faster lead-sharing. The buyer experienced the partnership as one coordinated answer to their AI infrastructure challenge, not two separate vendors trying to sell them pieces of a solution they'd have to integrate themselves.

The numbers: POC cycles dropped from 90 days to 34, generating $14M in net-new ARR within two quarters. Their Partner Signal Velocity (PSV) went from 2 weeks to 36 hours. Cross-Brand Intent Delta (CBID) jumped 11 percentage points.

But the real win? The buyer never had to play vendor matchmaker. The solution presented itself as one integrated offering.

Case 2: Here's how real-time stitching transforms routine transactions into growth moments

Renewal conversations are typically the business equivalent of watching paint dry—until you know the customer just tripled their API usage and started experimenting with advanced workflows they've never touched before.

A CRM giant and their top ISV integration partner figured out how to turn this intelligence into expansion revenue by combining renewal timing data with feature usage surges. When their shared AI scoring detected expansion readiness, it triggered coordinated outreach that transformed routine renewals into growth opportunities.

The magic moment: A customer success manager gets a 90-day pre-renewal alert that reads like a gold mine: "This account just tripled API calls and launched five new automation workflows—expansion opportunity incoming." Meanwhile, the ISV partner was already seeing the same usage explosion and had ROI calculations ready to go.

What normally would have been a "hope they renew" conversation became a "here's how to 3x your results" expansion play with pre-built business justification.

From the buyer's seat: Instead of receiving a generic renewal reminder, they got a strategic consultation backed by their own usage data. The message wasn't "please renew"—it was "here's the ROI you're missing and how to capture it."

The insight that matters: Networked funnels don't just accelerate timing—they elevate context. When partners share intelligence about customer behavior, routine transactions become strategic growth moments.

The impact: Expansion close rates rose 22%, renewal risk dropped 15%, and both partners booked incremental revenue in the same quarter. Their PSV dropped from 5 days to 4 hours, CBID increased 8 percentage points, and Partnership Friction Index hit just 3%.

Case 3: Sometimes the best way to understand networked speed is to watch coordination break down

Sometimes the best way to understand networked funnels is to watch what happens when they break down. A cybersecurity vendor and their global systems integrator partner had all the right signals—security downloads, RFP submissions, budget allocation discussions—but fell back into quarterly meeting syndrome when it came time to coordinate.

The missed signals: The vendor saw a Fortune 500 account downloading threat assessments and attending zero-trust webinars. The SI partner received an RFP for security infrastructure consulting from the same buyer. Classic coordinated buying behavior.

The coordination failure: Instead of acting within hours, they waited for their scheduled quarterly alignment meeting. Each assumed the other would take the lead. Classic partnership paralysis.

Meanwhile: A competitor with networked coordination had their teams texting each other from airport lounges. They booked discovery calls within 48 hours, completed a security assessment, and presented a joint proposal before the original partners had even scheduled their alignment call.

From the buyer's perspective: The winning competitor felt like a unified team with a clear plan. The losing partners felt like two separate vendors who couldn't get their act together. Same technical capabilities, completely different coordination experience.

The brutal ending: $9M ARR deal signed with the competitor during fiscal year-end budget season. The failed partnership's PSV was 10 days; the winner's was under 24 hours. CBID differential: 9 percentage points in the winner's favor.

The lesson that keeps me up at night: In enterprise B2B, networked funnel speed isn't just about efficiency—it's about existential survival. The partnerships that can't coordinate at AI speed don't just lose deals; they get locked out of relationships that take years to rebuild.

Why Smart Companies Still Leave Their Funnels in Pieces

I've watched this pattern repeat across dozens of enterprise partnerships: Companies intellectually understand the networked funnel concept, then systematically sabotage their own implementation. Here's why:

Legal teams treat real-time data sharing like nuclear waste. They're optimizing for zero liability instead of revenue opportunity. Partner contracts written in 2019 have data-sharing clauses that assume quarterly reporting, not real-time signal coordination. Getting legal approval for AI-speed partnership moves can take longer than the deals themselves.

Sales teams hoard accounts like dragons guarding treasure. Account executives are protective of "their" relationships and suspicious of partner involvement that might complicate commission structures. Partnership teams, meanwhile, lack the authority to mandate changes to sales processes that directly impact quota achievement.

Marketing ops teams can barely get their own systems to talk, let alone coordinate with external partner platforms. Most CRMs and marketing automation platforms weren't built to ingest real-time signals from external partnerships. The technical lift often exceeds the organizational patience for integration projects.

Everyone's optimizing for their own dashboard metrics. Your partner cares about their pipeline velocity and close rates. You care about your expansion revenue and renewal percentages. Nobody's measuring shared outcomes or coordinated response times, so nobody's incentivized to build the infrastructure for networked funnels.

The playbook doesn't exist yet. Even companies with the technical capability to share signals in real-time often have no operational framework for acting on that intelligence at AI speed. They can detect the coordination opportunity but can't execute the coordinated response.

How to Build Your Own Networked Funnel

Based on tracking dozens of these implementations, here's what actually works:

Stage 1: Traditional (Where Most Companies Are Stuck)

Static quarterly reports that arrive weeks after opportunities have passed. Manual lead swaps that sit in inboxes until someone remembers to follow up. Attribution debates that happen long after deals are won or lost by competitors with better coordination.

Stage 2: Integrated (The Dangerous Middle Ground)

API-based data sharing with basic segmentation alignment. Post-campaign reporting that happens in days instead of months. This feels like progress but still operates at human speed, not AI speed.

Stage 3: Ecosystem (The Networked Funnel)

Live signal sharing that triggers coordinated responses within hours. Joint AI decisioning that identifies opportunities faster than any human team could coordinate. Co-triggered campaigns that launch without manual intervention.

The diagnostic that matters: Can you share anonymized customer signals within 24 hours? Can you launch a co-branded play in under 72 hours? Are both teams measuring the same KPIs in real time?

If any answer is no, you're not ecosystem-ready yet.

The Four Metrics That Make Networked Funnels Measurable

Partner Signal Velocity: This measures what I call "text-to-demo" time—how long from when your partner's customer downloads a white paper to when you're both on a discovery call? Best-in-class partnerships do this in under 2 hours. Most take 2 weeks. The difference is the difference between relevance and obsolescence.

Cross-Brand Intent Delta: This tracks the win rate lift when partner signals are added to your qualification process. Adding partner browsing behavior to your email targeting might bump conversion from 12% to 19%. That 7-point lift is pure network effect—intelligence you couldn't generate alone.

AI Attribution Confidence: The percentage of revenue that gets attributed automatically without manual reconciliation fights between partner teams. When both sides can see exactly which touchpoint drove opportunity creation, you eliminate the "he said, she said" attribution debates that poison partnership relationships.

Partnership Friction Index: The percentage of AI-triggered campaigns that need manual intervention to execute. If your systems talk to each other seamlessly, human firefighting becomes rare. If you're constantly debugging sync errors, you're not ready for AI-speed coordination.

When Networked Funnels Unravel (And How to Prevent It)

AI amplifies whatever organizational dynamics already exist. If your partnership coordination is broken at human speed, AI will just help you fail faster and at greater scale.

Over-automation kills deal nuance. AI optimizes for speed and conversion metrics, but enterprise deals often require relationship subtlety that algorithms miss. Keep human review for top-tier accounts where deal complexity exceeds AI's pattern recognition capabilities.

Compliance missteps create legal liability faster than revenue. Sharing the wrong customer data across organizations can trigger regulatory reviews that take months to resolve. Build explicit opt-in frameworks and audit data flows regularly, especially in regulated industries.

Signal misreads waste marketing budget and customer trust. False positives create awkward customer moments—like promoting enterprise software to someone browsing your small business plans. Train AI on historical deal patterns and test extensively before scaling automated responses.

Partner misalignment accelerates at AI speed. When technology moves faster than relationship management, you can trigger coordinated campaigns that conflict with each partner's account strategy. Monthly account strategy syncs aren't optional overhead—they're strategic alignment insurance.

The Choice: Networked or Irrelevant

Here's the uncomfortable truth I see playing out across enterprise B2B: Funnels aren't dead—they're just too big for one brand to carry alone.

The companies learning to carry them together, sharing signals in real-time and responding as one coordinated solution, will control their markets. They'll lock in customer relationships through superior coordination that competitors can't match with individual optimization.

Everyone else will be perfecting their appetizer while the main course gets served by someone else.

B2B ecosystems are consolidating around these networked partnerships. The companies that build AI-coordinated partner networks now will have intelligence advantages that take competitors years to replicate. Once a buyer experiences truly coordinated partnership execution, fragmented vendor approaches feel amateurish by comparison.

Your choice: Keep trying to win enterprise deals with one fragment of the buyer journey, or join the partnerships that see and respond to the whole picture.

The window for building networked funnels is still open. But it's closing fast.

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Let’s be honest—few things get a partnership team more excited than landing a shiny new deal. The pitch, the press release, the LinkedIn buzz. It feels like momentum—until six months later, when no one can quite explain what changed.

Most companies treat partnerships as binary: signed or unsigned, resourced or ignored. Partnerships are products. They evolve, plateau, and require just as much lifecycle discipline.. The companies that understand this don't just have more successful partnerships—they know when to scale, when to support, and when to sunset.

Let's break down the four phases and what actually happens after plateau.

1. Sign: The Spark Phase

This is the romantic beginning. Someone on your executive team or business development org strikes a chord with a strategic partner. There's alignment on vision. Shared market opportunity. Genuine excitement. Maybe even a splashy press release with quotes about "transforming the industry together."

Think about Slack's early partnership with HipChat (before Atlassian acquired it). The announcement was big news in 2014—two chat platforms partnering instead of competing. But without clear value creation beyond the PR moment, it faded into obscurity within months.

At this stage, you don't need a dedicated partner manager burning cycles on relationship maintenance. You need a hypothesis. What specific value are you hoping to create together that neither company can achieve alone? Is it market access? Technical capability? Customer acquisition? Cost reduction?

Litmus Test: If you stopped at the press release and never spoke again, would either company's customers or business actually notice?

2. Launch: Where Intent Meets Reality

This is where partnerships go to die—or come alive.

You scope the technical integration. You coordinate the joint blog post. You prep the co-branded pitch decks and train the sales teams on use cases. Maybe there's some early co-selling with a few key accounts.

Take Shopify's partnership with Facebook (now Meta) for social commerce. After the initial announcement, the launch phase involved deep technical integration, merchant onboarding flows, and coordinated go-to-market efforts. The difference? They moved fast from announcement to activation—merchants could start selling on Facebook within weeks, not months.

The biggest risk at this point isn't outright failure—it's death by neglect. Early-stage partnerships stall not because they were fundamentally bad ideas, but because no one truly owned them. A part-time product marketing manager juggling five other priorities, or a business development lead already chasing the next shiny deal, simply isn't enough if the launch phase stretches on for months.

Tip: The faster you move from announcement to activation—real customers using real functionality—the higher your odds of creating compounding impact.

3. Grow: The Entanglement Stage

If you make it here, congratulations. This is when the partnership flywheel actually kicks in:

• The sales team has concrete use cases and customer stories to reference

• The product team is actively requesting deeper integration capabilities

• Customer support is getting real feedback about joint solutions

• Revenue is (consistently) showing up on the board's dashboard

• Both companies' customers are asking for more

Consider how Amazon Web Services and Salesforce evolved their partnership. What started as basic data synchronization grew into Lambda functions triggered by Salesforce events, joint enterprise sales cycles, and customers building entire businesses on the combined platform.

Now—and only now—is the time to assign a dedicated partner manager or partnership owner. Not earlier, when you're still proving value. Not later, when momentum has already peaked.

Guiding question: Is this partnership creating measurable revenue or retention advantages that justify dedicated headcount and ongoing investment?

4. Plateau: The Inertia Trap

Most partnerships die here—quietly, slowly, without anyone noticing until the quarterly business review.

You did the launch. You captured a few early wins. But now, six quarters later, the joint pipeline is drying up. The original product integrations feel stale compared to newer, sexier solutions. The people who championed and signed the deal have moved to other companies. Customer interest has shifted.

And yet... the partnership persists in planning meetings, resource allocation discussions, and strategy presentations. It's become organizational wallpaper—present but invisible, consuming mindshare without creating value.

Here's the hard truth: Not every partnership deserves to be saved.

The key insight: Plateau doesn't mean failure. It means evolution—or graduation.

The "Then What?" - Strategic Options Beyond Plateau

So what happens after plateau? You have three paths, each requiring different organizational muscles and honest self-assessment:

Option 1: Renewal - The Strategic Reframe

This isn't about throwing good money after bad—it's about recognizing that market conditions, customer needs, or competitive landscapes have shifted enough to warrant a fundamentally different approach.

When it works: Microsoft and Adobe's relationship exemplifies this. Their original partnership focused on basic Office integration. But as both companies pivoted to cloud-first strategies, they renewed with a completely different thesis around enterprise productivity and creative workflows. The key? They didn't just refresh the existing partnership—they rebuilt it from the ground up.

Where companies fail: Trying to renewal-manage their way out of fundamental misalignment. If the core value proposition no longer exists, no amount of reframing will fix it. Organizations often confuse activity with progress, launching "partnership 2.0" initiatives that are really just the same partnership with better slides.

Option 2: Maintenance Mode - The Steady State

Sometimes the smartest move is accepting that a partnership has found its natural equilibrium. Not every partnership needs to become a category-defining alliance. Some just need to work—quietly, reliably, without drama.

When it works: Think about basic API integrations that work reliably but don't drive expansion. Customers depend on them, they require minimal resources to maintain, and they free up your team to focus on higher-leverage opportunities. Zapier’s ecosystem of thousands of SaaS integrations is the perfect example—low-touch, high-impact, and exactly as ambitious as it needs to be.

Where companies fail: Treating maintenance mode as a temporary state while secretly hoping for miraculous growth. The trap is assuming “maintenance mode” means no maintenance at all—until a quiet API change breaks things for 30,000 users.T his leads to under-investment in the relationships that actually deserve attention. The other trap? Maintenance partnerships that slowly become technical debt—still consuming engineering resources but no longer aligned with product direction.

Option 3: Sunset - The Strategic Exit

This is often the right choice but the hardest to execute due to sunk cost fallacy and organizational inertia. The best sunsetting isn't abandonment—it's graduation.

When it works: When companies approach sunsetting as a deliberate strategy rather than failure management. Clear communication timelines, customer migration plans, and internal resource reallocation. The partnership served its purpose, market conditions changed, and both parties move on to better opportunities.

Where companies fail: The slow fade. Letting partnerships zombie-walk through planning decks year after year is a slow bleed—on resources, focus, and credibility. This creates confusion for customers, wastes internal resources, and prevents teams from focusing on partnerships with actual potential.

Partnership Lifecycle Checklist (Dispatch Edition)

Use this diagnostic to evaluate exactly where a partnership sits—and what to do about it.

🟡 SIGN (Hypothesis Stage)

You're here if:

• A partnership has been discussed or signed, but no integration or execution exists yet

• The partnership is based on strategic alignment, not clear customer demand (yet)

Checklist:

☐ Do we have a defined value hypothesis?
☐ Is there a clear "why now" for both parties?
☐ Are we solving a problem better together?
☐ Has someone committed to moving this beyond concept?

🔁 Action: Run a fast 30-day value test. If you can't get to MVP impact quickly, rethink it.

🟠 LAUNCH (Activation Phase)

You're here if:

• The partnership has been announced or integrated, but traction is unclear

• There are slide decks and maybe a demo—but few real users

Checklist:

☐ Has the integration gone live?
☐ Is there a single owner internally (PMM, BD, or otherwise)?
☐ Are internal teams enabled (sales, support, success)?
☐ Do customers or users know it exists?

🔁 Action: Timebox enablement and go-to-market execution. If it's not generating feedback within 90 days, it's time to reassess or deprioritize.

🟢 GROW (Flywheel Phase)

You're here if:

• The partnership is driving measurable impact: revenue, retention, product usage, etc.

• Multiple teams are now involved in scaling it

Checklist:

☐ Are joint customers expanding usage or renewing faster?
☐ Do we have co-selling or co-marketing motions working?
☐ Is a partner manager or program owner assigned?
☐ Are we jointly setting goals or sharing roadmaps?

🔁 Action: Double down. This is where compounding value lives—invest in enablement, feedback loops, and deeper entanglement.

🔵 PLATEAU (Diminishing Returns Phase)

You're here if:

• The partnership worked... until it didn't

• No one is really pushing it anymore, but it still lingers in your deck

Checklist:

☐ Is usage or pipeline stagnant or declining?
☐ Has the original champion left or gone silent?
☐ Are enablement materials outdated or unused?
☐ Does resourcing exceed current impact?

🔁 Action: Sunset, shrink, or re-scope. Partnerships aren't tombstones—they're toolkits. Make room for what's next.

Real Case Study: Slack + Atlassian (2018) → A Quiet Plateau, a Smart Sunset

Want to see plateau management done right? In 2018, Slack and Atlassian killed their integrations. You read that right.

Rather than maintain lukewarm support for competing messaging tools (Hipchat, Stride), Atlassian made a bold move: they shut down their chat products and took an equity stake in Slack instead.

What looked like a partnership breakup was actually a strategic reframe. Instead of pretending their chat tools could compete with Slack's momentum, Atlassian acknowledged the plateau and realigned resources around a deeper relationship. Slack got enterprise credibility and distribution reach. Atlassian got a stake in the future of workplace messaging without the distraction of building a competing product.

The result? Both companies focused on their strengths, customers got better integrated experiences, and resources that were being wasted on maintaining diminishing partnerships got redirected to higher-impact opportunities.

This is partnership lifecycle management at its finest—recognizing when plateau means pivot, not persistence.

The Strategic Discipline

Here's what separates mature partnership organizations from the rest: they understand that partnership portfolios require the same discipline as product portfolios. That means regular lifecycle reviews, honest performance assessments, and the organizational courage to make hard decisions.

The best partnership teams aren't just good at signing deals or managing relationships—they're good at recognizing when market conditions have changed enough to warrant a completely different approach. Sometimes that means doubling down. Sometimes it means finding a sustainable equilibrium. And sometimes it means acknowledging that the partnership served its purpose and it's time to move on.

In a world flooded with partnership opportunities, the real differentiator isn’t hustle—it’s judgment.

The best teams don’t chase every shiny logo. They apply the same discipline to partnerships as they do to products: iterate, measure, retire. They know that saying no—or saying enough—is sometimes the most strategic move of all.

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The controversy wasn't surprising. Partnership teams are often caught in a difficult position: expected to drive significant business value but frequently struggling to precisely articulate how they're doing it beyond "good vibes" and relationship-building.

After dozens of conversations sparked by that post, I want to expand on two critical aspects of next-generation partnership organizations that deserve deeper exploration: structure and measurement.

The Partner Org of Tomorrow: Lean Teams with AI Leverage

The highest-leverage partner teams I'm seeing in 2025 have abandoned the traditional "bodies in seats" approach to scaling partnerships. Instead, they've embraced a hybrid human+AI model with:

• Small, specialized teams of partner operators augmented by AI for selection, routing, and attribution

• AI systems that automatically identify partner opportunities by analyzing customer usage patterns, not just partner referrals

• Dynamic commission models tied to customer lifetime value, not just initial deals

• Virtual Partner Managers handling routine enablement, QBRs, and conflict resolution

What's fascinating: Companies are discovering they can run partner programs with 1/3 the headcount but 3X the impact when AI handles partner matching, account overlaps, and leads.

The obsolete pattern? Dedicated partner managers for each tier with manual processes, random selection criteria, and siloed communications. This approach simply doesn't scale without diminishing returns.

The Partnership Measurement Crisis

Even more fundamental than org structure is the measurement problem. In reviewing dozens of partner programs recently, I found:

• 70% track basic activity metrics (# of deals, partner-sourced pipeline)

• Only 18% track partner influence across the full customer lifecycle

• Less than 5% can quantify the actual ROI of their partner program

• Almost none measure second-order effects (e.g., reduced CAC, improved retention)

The result? Partner leaders fighting for resources with vague promises while other functions show clear ROI. This is the partnership equivalent of "half my advertising budget is wasted, I just don't know which half."

What Works in AI-Native Partnership Measurement

Modern partnership organizations are embracing more sophisticated measurement approaches:

1. Multi-touch attribution models that track partner influence across the entire customer journey, not just deal registration

2. Predictive modeling of which partner motions drive actual outcomes (not all partner-sourced leads convert equally)

3. Ecosystem value metrics that quantify network effects beyond direct revenue

4. AI-powered pipeline analysis showing which partners actually accelerate deals vs. those claiming credit

This isn't just a technical problem—it's a strategic one. The partnership teams winning in 2025 are the ones with measurement clarity that drives focused investment.

Rethinking Partnership ROI

The core question executives should be asking isn't "How many partners do we have?" but rather "What's the marginal value of each additional partner engagement?"

In my research, I've found that partnership orgs often hit diminishing returns after about 20-30 active partners. Beyond that threshold, the effort to maintain relationships often exceeds the incremental value unless you fundamentally change your approach.

AI-native partnership teams can shift this curve dramatically. By automating routine activities and focusing human judgment on high-leverage moments, they can manage significantly more partnerships with better outcomes.

But this only works if you can measure those outcomes clearly.

The Path Forward

If you're leading a partnership function or responsible for partner strategy, consider these immediate next steps:

1. Audit your current measurement approach: Can you clearly articulate the ROI of your partner program beyond activity metrics?

2. Map partner functions to AI potential: Which activities require human judgment versus which could be better handled by intelligent systems?

3. Evaluate your partner-to-revenue ratio: Are you seeing diminishing returns as you add more partners?

In my upcoming report on AI-Native GTM strategies, I'll be diving deeper into frameworks, tools, and organizational structures that leading companies are using to transform their partnership approaches. The future of partnerships isn't headcount-heavy teams running on meetings and Google Slides—it's lean, strategic teams with AI leverage and crystal-clear measurement.

I'd love to hear your thoughts on partnership measurement and AI adoption. What metrics do you use to evaluate your partnership program's success? Drop a comment below or reach out directly.

Next: we’ll break down the partnership lifecycle—and how to know when to double down or walk away.

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I was talking to a partnerships director at a major SaaS company last week, and she told me something that stopped me in my tracks: "We don't even call them partnerships anymore. We call them data arrangements." She wasn't being cynical—she was being honest about what actually drives value in 2025.

The rise of AI has fundamentally rewired what partners bring to the table. Distribution still matters, but it's table stakes. The real question now is: what data are you sharing, and how does it make both companies smarter?

The New Partnership Currency

Here's what's changed: AI doesn't run on good intentions or handshake deals. It runs on data. And the companies building the best AI products aren't just collecting their own data—they're orchestrating intricate webs of data partnerships that would make a 1990s media conglomerate jealous.

Every modern integration has an unspoken second layer. Sure, your CRM might integrate with their email platform to sync contacts. But what's really happening is that their AI is learning from your customer interaction patterns, while your algorithms get smarter about predicting email engagement based on their delivery data.

Partnerships are no longer just business development. They're data architecture decisions.

Three Case Studies Where Data Is the Real Deal

Case 1: Red Ventures and OpenAI - The Content Intelligence Play

Red Ventures runs a portfolio of high-intent websites like Bankrate, Healthline, and CNN Underscored. On the surface, their partnership with OpenAI looks straightforward: Red Ventures' content surfaces in AI assistant responses, driving traffic back to their sites.

But dig deeper and you'll see the real value exchange. Red Ventures has spent years building structured content taxonomies—they know exactly which financial questions drive conversions, which health topics generate the most engagement, and how to present complex information in digestible formats.

For OpenAI, this isn't just another content licensing deal. It's access to a masterclass in human information needs, packaged as training data. Red Ventures gets AI-driven traffic; OpenAI gets a PhD in content optimization across multiple verticals.

Case 2: Snowflake and NVIDIA - Where Your Data Lives Determines Everything

This partnership was announced as a way to bring AI compute closer to enterprise data. NVIDIA provides the processing power; Snowflake provides the platform where that data already lives.

But consider the intelligence NVIDIA gains from this arrangement. They get anonymized insights into how different industries structure their data, which types of queries are most common, and how enterprise AI workloads actually behave in production. This isn't just about selling more chips—it's about understanding the enterprise AI landscape better than anyone else.

Meanwhile, Snowflake gets to offer AI capabilities without customers having to move their data anywhere. The partnership creates a moat around both companies' core businesses while generating valuable insights for future product development.

Case 3: Spotify's Transcription Strategy - Free Tools, Valuable Insights

Spotify offers podcasters free transcription services powered by OpenAI's Whisper technology. Creators get searchable transcripts; Spotify gets to be helpful.

But here's what's really happening: every transcript becomes indexed, searchable data. Spotify is building the world's largest database of spoken content, categorized by topic, sentiment, and engagement patterns. They're learning what topics resonate with different audiences, how successful creators structure their content, and which conversation patterns predict viral episodes.

This data doesn't just improve Spotify's recommendation algorithms—it positions them to become the intelligence layer for the entire podcasting ecosystem. They can tell creators not just what's popular, but what's about to become popular.

Think Strategically About Data Partnerships

Not every partnership needs to be a data deal. But if you're in the AI business—or soon will be—these are the right questions to ask:

→ What data is flowing, and in which direction?
Be clear on what’s shared, what’s enriched, and what’s retained.

→ Who benefits from model improvement?
Is one party’s system getting meaningfully better while the other sees little change?

→ Is the value unique to this partnership?
Great partnerships generate compound insights that neither side could achieve alone.

→ What happens if it ends?
Practical, not pessimistic. What lives on—product features, trained models, user behavior?

The Bigger Picture: Data as Leverage

The companies winning in this new model aren’t necessarily louder about partnerships. But they’re more intentional.

They’re designing interactions—APIs, integrations, co-builds—that accelerate learning across their ecosystem. They’re less focused on surface-level alignment, more focused on defensibility through intelligence.

If you lead partnerships today, the real opportunity isn’t just expanding your footprint. It’s tuning your feedback loops.

Data isn’t a byproduct anymore.
It’s the point.
And the best partnerships reflect that.

On Monday, we’ll explore what modern AI-native partnerships actually look like—lean, measurable, and high-impact

The Old GTM Playbook Is Dead

Your siloed departments, batch-and-blast marketing campaigns, and traditional reseller channels? They're relics of a slower, siloed era—outpaced by AI-native companies that learn, adapt, and engage in real time. It’s not about bolting AI onto legacy processes. It’s about redesigning how your business learns and sells.

The numbers don't lie: companies embracing AI-native GTM strategies are seeing revenue increases of 3-15% and sales ROI improvements of 10-20%. But here's the kicker—these gains aren't coming from simply bolting AI onto existing processes.

Four Pillars of an AI-Native GTM Framework

From my research, there are four essential pillars driving this transformation:

1. The AI-Enabled Tech Stack

This isn't just another tech buzzword. We're talking about building a unified foundation where your data and AI power all customer interactions through four key layers:

• Build a modern infrastructure with four key layers:

  • Data unification: A central customer data platform that breaks silos

  • Intelligence layer: AI/ML models that turn data into insight

  • Integration layer: APIs and orchestration tools connecting systems

  • Application layer: AI embedded directly into workflows2. Intelligent Channels

Forget standard sales routes. We're now seeing:

• AI-enhanced marketplaces with smarter recommendations

• App ecosystems with embedded AI functions

• The rise of truly virtual channels where AI agents interact directly with customers

3. Evolved Partnerships

The partnership game is changing from transactional reselling to strategic co-innovation. Think BMW and NVIDIA co-creating a digital twin factory, or Pfizer partnering with Tempus for AI-driven drug discovery.

Companies are making strategic bets on AI ecosystems—choosing sides between OpenAI/Microsoft, Google/GCP, or open-source alternatives like Databricks/Hugging Face. These choices fundamentally shape your GTM trajectory.

4. The AI-Embedded Organization

New roles are emerging: Chief AI Strategy Officers, GTM Data Scientists, AI Business Analysts. Cross-functional AI squads are breaking down silos, and AI agents are becoming virtual team members.

How AI Is Transforming Each GTM Function

Marketing: From Campaigns to AI-Driven Journeys

Marketing is moving from broad static segments to micro-segments—even segments of one. HubSpot uses AI to predict which content will drive pipeline—resulting in 35% higher conversions from the same spend. Micro-segmentation is the new targeting.

Sales: Augmented Reps, Shorter Cycles

AI enables precision prospecting, finding accounts actively showing buying intent. Salesforce now uses AI to surface deal risks in real time—helping reps shorten sales cycles and close faster. In 2023, they attributed 15% revenue growth to AI-assisted sales motions.

Partnerships: From Channel Sales to Ecosystem Orchestration

The partnership function is moving beyond transactional reselling to orchestrating dynamic ecosystems. Choosing an AI ecosystem—OpenAI/Microsoft, Google/Vertex, or open-source—now determines not just your tech stack, but your GTM motion, partnership roadmap, and even your brand positioning.

Customer Success: Proactive and Scalable

AI continuously analyzes hundreds of signals to predict churn risk with uncanny accuracy. Modern CS platforms generate dynamic risk scores, letting teams intervene before issues escalate. In CS, AI doesn’t just scale support. It shifts you from reactive to preemptive, from tickets to trust.

Real-World Examples That Matter

• OpenAI and Bain: Shows the power of deep strategic alignment plus serious enablement, with Bain training thousands of consultants on GPT models.

• Salesforce Einstein GPT: Demonstrates how an established leader can leverage internal innovation and strategic partnerships to quickly bring AI to their massive customer base.

• Snowflake and NVIDIA: Reveals how two tech leaders with complementary capabilities can create a compelling new value proposition that neither could offer alone.

• HubSpot's Breeze AI weaves intelligence across marketing, sales, and support—without breaking their open partner model

The Balance That Matters

Despite all this powerful tech, the human element remains critical. The winners in this new era will find the optimal balance—leveraging AI's immense capabilities while harnessing the unique strengths of their human talent.

The real challenge isn’t simply adding AI: it’s balancing machine speed with human trust. That tension will define your next era of growth.

Are you ready to reimagine your GTM strategy for the AI era, or are you still clinging to playbooks that worked yesterday but won't tomorrow?

What's your take? How is AI reshaping your go-to-market approach? Drop your thoughts in the comments below.

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Meanwhile, in the executive staff meeting, the CEO asks the uncomfortable question: "How are these partnership activities translating to revenue?" Silence follows.

Sound familiar?

Most partner teams are stuck in a classic Pareto principle trap without realizing it: investing equal resources across dozens of partnerships when 80% of their actual business impact will come from just 20% of their partners. They're busy, they're reporting lots of "activity," but they're spreading their resources too thin to create meaningful business impact where it matters.

At DocuSign, we discovered this truth firsthand. After years of building "strategic relationships" with dozens of companies, our data revealed that just two partnerships were driving 80% of our partnership-influenced revenue. Two out of dozens. The revelation forced a complete rethinking of our approach, ultimately transforming how we designed and executed our entire ecosystem strategy.

The Partnership Mimicry Trap

The partnerships world is filled with carbon copies. AWS Marketplace listings. "Better together" decks. Partner portals. MDF programs.

Stack ten B2B partner strategies side-by-side, and you'll find the same components shuffled in different orders—like tech companies all following the same recipe book but expecting unique results.

These tactics aren't inherently bad. But without clarity on what you're trying to achieve, they become expensive busywork. At best, you waste resources. At worst, you create noise that distracts your entire organization from what actually works.

Most partner teams have copied a playbook that might have worked for a household name, at a different stage, in a different category, with a different value proposition—hoping it somehow translates. But generic doesn't win.

Start With the Press Release (aka "Working Backward")

Amazon has a famous internal practice: before starting any new product, the team writes the press release as if the product already launched and was successful. It forces clarity and alignment on the fundamentals: Who is this for? What problem does it solve? Why is it newsworthy?

Partnership teams should steal this habit—not just for individual partnerships, but for their entire ecosystem strategy.

Imagine writing: "Company X announced today that its partner ecosystem has become the industry standard for solving [specific customer problem], with over 80% of enterprise customers standardizing on its platform and citing partner integrations as a primary reason for selection."

If you can't articulate that level of specific impact for your overall ecosystem strategy, you're still in the generic partnership zone.

Before you ink another MSA or build another integration, write the press release for your entire partnership approach. Picture a future where your strategy worked. What happened? What changed for your company, your customers, and your market position? How did your ecosystem become a genuine competitive advantage rather than just a slide in your pitch deck?

If this exercise feels difficult, you're probably following someone else's blueprint instead of solving a problem that's uniquely yours.

Ecosystem Orchestration: Create Your Own Universe

One of the biggest mistakes partner teams make is starting from someone else's map.

Too many teams begin by asking: what does AWS, Salesforce, or Google want? What's in their ecosystem strategy? While those questions matter eventually, they shouldn't be your starting point.

Ecosystem orchestration isn't about fighting for attention in someone else's universe—it's about creating your own gravitational field where partners are naturally drawn to your orbit.

It's the difference between being another vendor in a crowded marketplace versus becoming an essential platform that other companies build their strategies around.

Before you worry about anyone else's priorities, design your own ecosystem blueprint. This means starting with your customer's perspective and aligning with your company's strategic direction. At DocuSign, we mapped our partner ecosystem against our key go-to-market plays—identifying which specific partners were most critical for each strategic initiative.

For instance, when we began seeing early traction in specific industries, we didn't spread resources evenly across all potential vertical partners. Instead, we analyzed where customer success patterns were emerging and doubled down on just a few partnerships that could accelerate these bright spots. For each key vertical, we identified the 1-2 partners who already had deep domain expertise and existing relationships with our target buyers.

This shift in posture—from joining someone else's ecosystem to orchestrating your own—means curating a small set of high-impact partners who amplify your strategic GTM plays. The ones where your product shines, your sales team closes the deal, and your customers get outsized impact.

Instead of spinning up generic "co-sell" motions with dozens of partners, concentrate your resources on the few partners already serving your target buyers with complementary solutions. Build joint offerings that feel like magic to your customer because they're designed from a place of genuine joint value.

That's how you move from being another node in someone else's system to becoming the gravity center of your own.

Go All In (Where It Matters)

Despite what we may believe about our teams' capabilities, we will never be able to do it all.

Going all in means treating a partnership like a product launch. At DocuSign, we ultimately discovered that 80% of our partnership value was coming from just two partners. The revelation changed everything.

Instead of spreading resources across dozens of "strategic" relationships, we staffed these two integrations with dedicated in-house engineers, included them prominently in our core sales materials, and made them part of our company DNA. Every sales rep could demo them. Every customer success manager knew how to implement them. One of these partnerships alone drove 15% of our total company revenue—not just partner-influenced revenue, but actual top-line growth.

Take the few places where your business already has traction and ask: what would it look like to go all in? Not just a section on your website or a half-built integration. But a real, resourced play with sales training, customer references, dedicated incentives, and targeted marketing campaigns.

Then be brave enough to say no to the rest.

The 80/20 Mindset: From Activity to Impact

The best partner teams don't just acknowledge the 80/20 rule—they embrace it as their guiding principle. They know exactly which 20% of partnerships drive 80% of their impact, and they're ruthless about directing resources accordingly.

If your partner strategy feels stale, don't add more. Apply the 80/20 lens. Which few partnerships truly move the needle for your business? Where is your leverage strongest? Where do your customers actually see value?

Start by writing the press release for your ideal partnership outcome. Blueprint your own ecosystem based on your unique advantages. Define success in terms of real revenue and customer impact—not just activity metrics on a slide deck.

Most importantly, be brave enough to double down on the 20% that matters and scale back investment in the 80% that doesn't. At DocuSign, this meant focusing intensely on just two key partnerships while maintaining lighter-touch relationships with dozens of others. The result wasn't just more efficient resource allocation—it was a fundamental transformation in how our entire company viewed and leveraged partners.

It’s time to get real about your partnership strategy. Are you spreading resources evenly across many partners, or focusing intensely on the vital few? Are you managing an activity-driven program, or orchestrating an impact-driven ecosystem?

The partnerships that transform businesses aren't built on vanity metrics or number of logos on your website. They're built on the discipline to identify your true centers of leverage, and the courage to go all-in where you can win decisively.

Has your team discovered its own 80/20 rule in partnerships? Which few partners drive most of your impact? I'd love to hear your experiences in the comments.

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Beyond Transactions

Historically, partnerships were often about coverage, reach, implementation, integration. But now that AI can automate much of the operational grunt work, the value of partners is shifting.

AI is amazing at execution. But it’s not great at empathy, nuance, or navigating messy real-world problems. Today, we still need humans to ruthlessly obsess about outcomes.

That’s where high-leverage partnerships come in.

We’re seeing:

• Resellers evolve into strategic advisors who understand how AI tools fit within a client’s business model

• Consultants and SIs designing bespoke solutions for regulated, high-stakes, or industry-specific workflows

• Channel leads and BD pros morphing into cross-functional orchestrators who can speak tech, product, sales, and outcomes fluently

The Rise of the Fewer, Smarter, More Strategic Partner Roles

Partnership teams are actually getting sharper. This means leaner teams and fewer roles, but more impact. So, what might this look like in practice?

Instead of fielding 50 partner managers chasing co-sell spreadsheets, the modern SaaS company needs:

• 5 AI-fluent, outcomes-obsessed ecosystem strategists

• 3 co-innovation architects who build with partners, not for them

• 2 dealmakers who understand how to license data, structure API access, and measure impact across ecosystems

Partnership leaders must combine:

• Strategic thinking + domain expertise

• AI literacy + deal fluency

• EQ + technical understanding

Case in Point: Reddit x OpenAI

This isn’t theory, it’s already happening in the market. Reddit’s partnership with OpenAI shows how the new model plays out:

• Reddit licensed its API and data to OpenAI

• OpenAI enhances its models using Reddit content

• Together, they co-develop AI features and monetize via ads

It’s not a standard reseller deal. It’s ecosystem thinking. Mutual value creation. Deep integration.

TL;DR

🔹 AI is not replacing partnerships. It’s revealing which ones are worth having.
🔹 The winners won’t just have more partners. They’ll focus on ensuring they have the right ones that are deeply integrated, highly strategic, mutually accountable.
🔹 If you’re in a partner-facing role: redefine it, or you may find yourself needing to defend it.

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And the first signs are already here.

The Partnership Purge: What Salesforce’s Layoffs Signaled

When Salesforce cut hundreds of partner-facing roles in early 2024, many dismissed it as part of a broader tech correction. But look closer, and the pattern is clear: these weren't performance-based reductions. They were strategic. Entire divisions—partner success, alliance management, co-sell teams—were deprioritized, consolidated, or eliminated.

Salesforce is far from alone. Major SaaS companies are reshaping how they think about partnerships, moving from large, static teams to leaner, more specialized roles that can adapt to an AI-native ecosystem.

Why? Because the traditional structure doesn’t scale in an AI-first world (edit: frankly, maybe they were never scalable to begin with).

The traditional ‘partner program’—with tiers and MDF and certification gates—is a relic. AI is accelerating a new model.

From Integration to Intelligence

Historically, SaaS partner orgs focused on:

• ISV integrations

• VARs and channel sales

• SIs for implementation and customization

• Strategic alliances for co-sell or market entry

But AI is automating away the value in many of these roles.

• Agentic AI (like Salesforce’s Agentforce or OpenAI’s GPT-powered copilots) can now connect, configure, and orchestrate across tools with minimal human intervention.

• Resellers are being forced to evolve from volume license sales and into strategic advisors who understand how AI tools fit within a client’s business model.

• Systems Integrators (SIs) are no longer just implementation partners— As AI agents automate basic workflows, SIs must prove their value with domain expertise, complex orchestration, and business strategy

The result? The entire partner stack is under pressure.

The Fall of the Legacy Playbook

Most SaaS companies still run bloated partner motions that were built for a different era:

• Onboarding hundreds of partners with limited enablement

• Tiered partner programs that reward volume, not impact

• Co-marketing incentives disconnected from actual outcomes

That playbook is broken.

Today’s leading platforms are moving toward:

• Agent marketplaces (see Salesforce's AgentExchange) where partners build AI-first extensions, not static apps

• Consumption-based incentives, where value creation matters more than badge count

• Co-innovation ecosystems, not cookie-cutter integrations

This isn’t incremental. It’s foundational. And it’s happening faster than most partner leaders are prepared for.

A New Kind of Partner Model

To thrive, SaaS companies will need to completely reimagine their partnership strategy:

• Who they partner with (hint: it's not just SIs and resellers anymore—it's data providers, AI model trainers, and vertical domain experts)

• How value is measured (outcomes > ops)

• What enablement looks like (dynamic, embedded, AI-enhanced support)

• Which roles they staff (fewer partner managers, more ecosystem strategists)

And for legacy partner organizations? It’s adapt or be automated.

TL;DR

We’re witnessing the industrial collapse of the old SaaS partner model—and the emergence of a faster, smarter, AI-native one.

The next generation of SaaS companies won’t just “have a partner program.” They’ll be orchestrating ecosystems—fluid, intelligent, and deeply integrated.

Up next: We’ll explore what this means for individual partner professionals—how roles are changing, what skills will matter, and how to avoid becoming obsolete.

Hit subscribe to make sure you don’t miss it.

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On one side, we have the product and engineering teams racing to integrate AI capabilities – embedding models into workflows, reimagining features, and automating processes that once required a human with a coffee habit and a job title.

On the other side, we have marketing and sales teams sprinting to position themselves as "AI-powered" – frantically adjusting messaging, highlighting use cases, and training teams to say "machine learning" instead of "if-then statements" with a straight face.

But in the middle sits a critical function that's being treated like the weird jello dish at Thanksgiving: partnerships.

The Missing Strategic Layer

When we work with founders and growth-stage companies, we consistently find partnership strategies lagging behind product and go-to-market efforts like they're running a completely different race. This creates three distinct problems:

1. Distribution limitations – Your fancy AI can't help customers it never meets. Notion learned this lesson quickly with their AI features, expanding from direct integration to over 65 third-party app partnerships within months of launch.

2. Integration bottlenecks – Value creation is increasingly happening at the intersection of tools, not within them. Look at how Anthropic embedded Claude in productivity tools like Notion, Asana, and Slack – each partnership creating value that standalone AI access couldn't.

3. Ecosystem blindness – Teams operating without a map are just wandering expensively. Jasper's partnership with Anthropic for Claude integration distinguished them in the crowded AI writer space exactly because they saw the broader ecosystem when competitors were still building in isolation.

This gap isn't merely an operational oversight. It's a strategic vulnerability bigger than that unpatched zero-day your security team keeps emailing about.

Why Partnerships Matter More in an AI-Native World

The traditional SaaS partnership playbook had all the complexity of a handshake at a networking event – revenue-sharing agreements and basic API integrations. But AI is reshaping how value flows between companies in ways that would make even the most hardened BD veteran reach for the Advil:

• Data becomes a partnership currency – Reddit's partnership with OpenAI isn't just about distribution; it's about the value of Reddit's vast content for model training. The $60M deal fundamentally recognized that unique datasets now drive partnership value as much as revenue share.

• Workflow embedding creates new leverage points – Microsoft didn't just put Copilot everywhere because they enjoy coding; they recognized that AI capabilities deliver exponential value when inserted at key decision points. Look at how they've embedded Copilot into GitHub, transforming everyday coding workflows beyond recognition.

• Ecosystem positioning determines long-term viability – Midjourney could have chosen to go it alone, but their Discord integration strategy didn't just give them a platform – it created a community that drove their adoption curve far beyond competitors with technically similar offerings.

The companies winning in this new environment aren't just building better AI products – they're playing 3D chess with partnership strategies while everyone else is still playing checkers with features. This is the essence of what we call ecosystem orchestration – the strategic coordination of partnerships, integrations, and alliances to create value greater than any single product could deliver alone.

Ecosystem Orchestration: Three Partnership Models Worth Stealing

Based on our work with clients (and some shameless fan girl-style observation of market leaders), here are three partnership models that specifically address the AI transformation challenge:

1. Model Enhancement Partnerships – Like Databricks' partnership with Anthropic, where Databricks' enterprise data expertise improves Claude's capabilities, while they gain access to customized AI infrastructure. They didn't just integrate – they co-developed specialized implementations that neither could build alone.

2. Workflow Integration Networks – Superhuman's approach to embedding AI capabilities directly into email workflows across Google, Microsoft, and Apple ecosystems isn't just a feature play – it's a recognition that value delivery happens in the tools users already live in. Their partnership strategy expanded their footprint far beyond their direct customer base.

3. Capability Exchange Programs – Stability AI's partnerships with Canva and Adobe exemplify how specialized AI functions can be shared rather than rebuilt. Each company recognized their core strengths (image generation vs. creative workflows) and created exchange partnerships rather than competing head-on.

The specific approach depends on your market position, AI maturity, and business model – but ignoring partnerships as a core component of AI strategy is like bringing a spork to a gunfight.

Moving Forward

In future issues of The Dispatch, we'll explore specific strategies for designing, implementing, and scaling partnership programs built for an AI-native world. We'll share frameworks, case studies, and tactical approaches drawn from our work with founders and operators – with minimal corporate jargon and absolutely zero unironic uses of the term "synergy."

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If you're navigating these challenges now, we'd love to hear from you. What partnership hurdles are you facing in your AI transformation journey? (Besides the inevitable "our API documentation is technically accurate but practically indecipherable" problem that afflicts 98.7% of tech companies.)

Until next time,

Gillian
UNIT9 Collective

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The Dispatch is published by UNIT9 Collective, a boutique consulting firm helping founders and operators navigate transformation in an AI-native world. We're like consultants, but we actually build things and speak in complete sentences.

We created this newsletter for founders, operators, and strategic leaders navigating the messy reality of AI transformation. If you're trying to build something meaningful in a world of rapidly shifting capabilities and endless hype, you're in the right place.

What follows isn't a manifesto—it's an invitation to join us in exploring the gap between AI's technical possibilities and practical strategic execution. A space where we can share insights that don't fit neatly into 280 characters or generic consulting decks.

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Why This, Why Now

The Dispatch emerges from a specific frustration: watching brilliant companies fumble their way through AI transformation without a playbook.

After years of helping founders and operators navigate the shifting terrain of go-to-market strategy, we've noticed a pattern. As AI capabilities move from theoretical to practical, the gap between technical possibility and strategic execution grows wider by the day.

Most consulting approaches still treat AI as a feature to add rather than a fundamental shift in how value is created and delivered. Conventional strategy frameworks fall short. Traditional partnership playbooks miss the mark.

So we're starting The Dispatch to share the frameworks, models, and strategic approaches we're developing at UNIT9 Collective. Consider it field notes from the front lines of AI-native strategy.

What to Expect

This won't be another AI hype newsletter. No breathless coverage of the latest model releases or funding rounds. No generic "embrace AI or perish" platitudes.

Instead, expect specific, actionable frameworks for navigating the strategic challenges of AI transformation – with a particular focus on how partnerships and ecosystem positioning create leverage in an AI-native world.

The Dispatch will arrive in your inbox twice monthly. Free subscribers receive the main editions. When we eventually launch paid subscriptions ($9/month), subscribers will also get access to:

• Strategy template library

• Monthly office hours

• Private Slack community

In our upcoming issues, we'll explore critical topics like:

• The Partnership Gap in AI-Native Strategy

• Ecosystem Orchestration Models for Modern Companies

• Visual Strategy Systems That Actually Drive Execution

• Fractional Leadership in an AI-Accelerated Market

We're excited to build this community with you – one focused on practical strategy for a rapidly transforming landscape.

Until next time,

Gillian & Brynna
UNIT9 Collective

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The Dispatch is published by UNIT9 Collective, a boutique consulting firm helping founders and operators navigate transformation in an AI-native world. We're like consultants, but we actually build things and speak in complete sentences.