Ciente > Articles by: Ciente Editorial Team

Anthropic’s Mythos Just Gave Banks a Terrifying Glimpse of the Future

Author: Ciente Editorial Team

Anthropic
News

Anthropic’s Mythos Just Gave Banks a Terrifying Glimpse of the Future

Anthropic’s Mythos Just Gave Banks a Terrifying Glimpse of the Future

Anthropic’s Mythos AI is exposing banking vulnerabilities at machine speed- and regulators are starting to panic.

For years, banks assumed they had time.

Time to patch vulnerabilities. Time to update old systems. It’s time to modernize the infrastructure built decades ago slowly. Cybersecurity was treated like a constant race, sure, but one that still moved at human speed.

AI may have just broken that assumption completely.

According to Reuters, some of America’s largest banks are now scrambling to fix security weaknesses uncovered by Anthropic’s new AI model, Mythos. And the panic is not because Mythos found one catastrophic flaw. It is because the model is apparently effective at connecting hundreds of small, stagnant vulnerabilities into major attack paths.

That changes everything.

Banks traditionally prioritized fixing the critical threats first while lower-risk issues waited in line for weeks or months. But Mythos seems capable of turning those minor flaws into dangerous chains of exploits almost instantly. And these vulnerabilities that once felt manageable now suddenly feel urgent.

And honestly, this feels like one of the first real AI moments that cuts through the hype.

Not another image generator. Not another chatbot demo. It is AI colliding directly with critical infrastructure.

The scary part is how unprepared the system seems to be.

Reuters reports that banks are now patching flaws within days instead of weeks, rushing software upgrades, and even preparing for possible service disruptions caused by the speed of fixes. Some regulators are openly warning that cyber threats are now operating at machine speed while financial institutions still defend themselves at human speed.

That sentence alone should probably alarm people more than it does.

Because banks still run an enormous amount of legacy technology. Ancient code. Old infrastructure. Systems layered on top of systems over decades. AI does not get tired of digging through that mess. It does not overlook patterns. And it apparently does not need much time either.

What is even more interesting is that access to Mythos itself is limited. Only a handful of major institutions currently have direct access because the model is expensive and computationally demanding. Which creates an uncomfortable new divide: the biggest banks may get AI-powered defenses first, while smaller institutions struggle to keep up.

That is probably the clearest signal yet that the AI race is shifting away from novelty and toward power.

Companies and institutions with access to the strongest AI systems will not move faster. They may become dramatically harder to compete against- and dramatically harder to defend against, too.

Venmo
News

After an App Redesign, Venmo Takes User Privacy Seriously

After an App Redesign, Venmo Takes User Privacy Seriously

Venmo is finally making payments private by default- years after turning people’s financial activity into social content.

Venmo is finally doing something that feels painfully obvious: making users’ payment activity less public.

The company is redesigning its app, and one of the biggest changes is that new users’ payment posts will now default to “friends only” instead of being visible to everyone.

Which raises a very fair question.

Why did it take this long?

Venmo treated payments like social media content for years. Your coffee runs, rent payments, breakups, inside jokes, late-night food orders- all casually floating around in a semi-public feed because the app decided sharing by default was somehow normal behavior for a financial platform.

And people mostly accepted it because Venmo made the experience feel playful. Emojis. Comments. Reactions. It turned money into social interaction. The problem is that financial data remains financial data, even when it mimics memes and pizza emojis.

That became increasingly uncomfortable as journalists, researchers, and even random internet users continue to expose how much information could be pulled from Venmo’s public network. In one infamous case, reporters managed to trace connections tied to President Joe Biden through Venmo activity.

Other investigations revealed relationship drama, spending habits, political networks, and personal behavior patterns hidden inside supposedly harmless payment notes.

And the unnatural part was how long the tech industry defended this.

Silicon Valley assumes that people always trade privacy for convenience or social engagement. And Venmo has become one of the clearest examples of that mindset. The app was not accidentally public. Its design is meant to drive visibility through engagement.

But that’s changing.

Privacy-conscious is the norm. Because AI has made data collection feel more invasive than ever. Companies are suddenly realizing users do not necessarily want their financial transactions to function like Instagram stories.

The weird thing is that Venmo is framing this redesign as building “trust.” But trust isn’t created through better privacy settings after years of public-by-default behavior.

Trust is what you protect before users realize they need protecting in the first place.

Intent-Based Marketing
Blogs

Intent-Based Marketing in B2B: How Buyer Intent Data Changes Customer Acquisition

Intent-Based Marketing in B2B: How Buyer Intent Data Changes Customer Acquisition

Intent-based marketing helps B2B teams identify buyers already researching solutions. Learn how intent data works, why traditional demand generation struggles, and how GTM teams use first-party and third-party intent signals to engage buyers earlier.

Here is something most marketing teams do not want to sit with.

By the time a buyer fills out your form, they have already decided. Not necessarily that they will buy from you. That they are buying from someone. Up to 70% of the B2B buyer journey happens in the dark funnel. The invisible research phase. Where buyers evaluate vendors without raising their hand. By the time they contact sales, they have already shortlisted two or three vendors.

So, all that demand generation? The brand awareness campaigns? The cold outreach sequences? Most of it is aimed at people who are either not looking yet or have already made up their minds. That is not a marketing failure. That is marketing aimed at the wrong moment.

Intent-based marketing is about the moment that actually matters, especially for teams building a stronger data-driven marketing strategy.

What Intent-Based Marketing Actually Means

Intent-based marketing is a strategy that focuses on identifying and engaging buyers based on their real-time behavior, interest signals, and purchase intent, rather than their demographic profile or job title. It aligns closely with behavioral marketing approaches that rely on customer actions instead of assumptions.

Strip the definition down further. Instead of guessing which companies might want what you sell based on industry and size, you find the ones actively looking for it right now. Not because they filled out your form. Because their behavior across the internet is telling you something, and you are finally listening.

First-Party vs Third-Party Intent Data

The data comes from two places.

First-party intent is the easy one. Someone from a target account visits your pricing page three times in one week. They download your comparison guide. They sign up for a webinar. These are behavioral signals on your own properties, and they tell you this account is warm in a way that no demographic field ever could.

Third-party intent is more powerful and harder to action. Platforms like Demandbase, Bombora, and 6Sense aggregate intent data using natural language processing, machine learning, and IP reverse lookups to track topics being researched and identify the companies doing that research, similar to how B2B intent data helps teams uncover hidden buyer research activity. Someone from a fintech company you have never heard from is reading G2 comparison pages featuring your product and your two main competitors. They have not visited your website. They have not downloaded anything. But their research behavior, tracked across thousands of publisher sites, is a signal. And you can act on it before your competitor does.

Explicit and Implicit Buyer Intent Signals

Intent signals fall into explicit and implicit categories, which is why understanding intent signals has become critical for modern B2B marketing teams. Explicit: requesting a demo or signing up for a trial. Implicit: reading five blog posts about a specific pain point. Both matter. Together they start to form a picture of where an account actually is in its evaluation.

Why Traditional Demand Generation Is Losing Effectiveness

The traditional model is not wrong in its intentions. It is wrong in its assumptions.

Define an ICP. Build a list. Craft a message. Reach out at scale. That traditional playbook still influences many B2B marketing strategies today. The logic is sound. The problem is what it assumes about the buyer: that they are passive, waiting to be educated, and that the rep’s outreach is what initiates the consideration.

B2B buyers conduct an average of 12 online searches before visiting a specific brand’s website. 81% of sales representatives observe that buyers increasingly research before reaching out.

The buyer is not passive. They have already done the work. They have a shortlist forming before the first cold email lands. The rep who shows up cold is not introducing a new possibility. They are arriving late to a conversation that started without them.

Why Timing Matters More Than Targeting

Intent data does not just give you better targeting. It gives you timing, which is essential for executing a successful account-based marketing strategy. And timing is what most marketing strategy leaves out entirely.

A CFO at a logistics company doing late-stage research on spend management platforms is a fundamentally different conversation than a CFO who has never thought about the category. Same person, same title, same company size. Different moment. Intent data tells you which moment you are in.

Real-World Examples of Intent-Based Marketing

The Demandbase example is instructive because it is concrete and reflects how teams apply buyer intent data in ABM campaigns to engage accounts at the right stage. A mid-sized fintech company starts reading G2 comparison pages featuring your platform and competitors. Someone from that company downloads an eBook from your site. These are strong signals. The platform recognizes the surge in research activity, scores the account high-intent, notifies the sales team, and marketing automatically adds them to a campaign showcasing fintech use cases. The sales rep reaches out within a day: “I noticed FinBank has been exploring AI solutions for customer support. Happy to share what others in fintech are doing.”

That outreach does not feel like cold outreach. It is not cold outreach. It is a rep showing up with relevant context, at a moment when the buyer is already thinking about the problem. The conversion rate on that conversation is not the same as a cold call. It cannot be.

How Intent Data Improves Sales Conversion Rates

The mechanism works because it closes the gap between when a buyer is researching and when a vendor finds out about it. Most of the time, that gap is large enough for a competitor to get in first. Intent data collapses it.

Why Most Teams Fail With Intent Data

91% of B2B marketers now use intent data to prioritize accounts. Only 24% report exceptional ROI. The gap is not the technology. It is choosing the wrong provider for the specific use case, budget, and go-to-market motion.

That gap deserves attention because it is the most honest thing in the entire intent data conversation. The tool is widely adopted. The results are not widely achieved. Why?

Most sales teams get intent data wrong. They buy expensive signals they cannot activate, drowning SDRs in noise instead of giving them focus, which often creates poor sales and marketing alignment. Having a list of a hundred accounts surging on relevant topics means nothing if the team does not know which ones to call first, what to say when they do, or how to route the information into the existing sales motion without creating more work than it removes.

The Biggest Intent Data Implementation Mistakes

The common mistake: turning on third-party intent data before first-party infrastructure is in place instead of building a proper marketing automation foundation first. You drown in signals you cannot act on. Start with first-party. The expected timeline to ROI for first-party signals alone is 60 to 90 days. Full multi-source implementation takes 90 to 180 days.

Intent data is not a tap you turn on. It is a system you build. The organizations seeing real returns built it in sequence, starting with what they already own.

How Sales and Marketing Teams Use Intent Data

This is where the theory becomes practical and also where most implementations fall down.

Sales does not need a list of intent signals. They need prioritized action supported by stronger lead scoring processes and account prioritization. The strongest intent signal is not a single data point. It is multiple signals from different sources pointing to the same account. Third-party data shows a company researching your category. First-party data shows the same company visited your pricing page twice. Social signals show a VP of Marketing at that company engaged with content on the topic. CRM data shows this account matches your ICP with no existing relationship. Each signal alone is noise. Four signals pointing to the same account at the same time is a buying indicator that warrants immediate activation.

Intent Data for Sales Prioritization

Marketing uses intent to personalize at the moment of maximum relevance. Not generic nurture tracks. Specific campaigns that speak to where this account’s research has been. The logistics company reading about transportation spend analytics gets messaging about logistics. Not the general platform pitch.

Using Intent Data for Content Strategy

Content strategy shifts too. When you can see which topics your ICP is researching before they reach you, you stop guessing what to write, making content marketing metrics easier to align with buyer demand. You write what the in-market buyer is already looking for. The content meets the buyer where they are, not where you hope they will be.

Customer Success and Churn Prediction With Intent Signals

And customer success, the function nobody includes in this conversation, benefits from intent signals on existing accounts, especially within a broader full-funnel marketing strategy. An account suddenly surging on competitor topics is a churn signal before it is a renewal conversation. Knowing it early is the difference between losing the customer and keeping them.

The Dark Funnel and the Future of Buyer Research

In 2026, a significant portion of buyer intent signals originates from unstructured data. Private community discussions. Dark social channels. AI-driven conversational research.

This is the harder problem and it is getting harder. Buyers increasingly research in places that traditional intent data cannot see. Private Slack communities. Closed LinkedIn groups. Conversations with AI assistants that do not leave a trail. The information they are using to build their shortlist is not being captured by publisher networks.

Predictive Intent Modeling and AI-Driven Buyer Signals

The response from leading intent platforms is predictive modeling powered increasingly by AI marketing strategy capabilities and machine learning systems. Advanced machine learning models analyze macro-economic shifts, industry news, hiring patterns, and competitor movements to flag accounts likely to enter a buying cycle, even without direct engagement signals. That is intent inferred from context rather than behavior, and it is still imperfect.

The practical implication is that intent data tells you who is in-market among the accounts whose research leaves a visible trace. A meaningful share of your ideal buyers are researching in channels you cannot see. Which means intent data is a powerful signal, not a complete picture. The teams winning with it treat it as a prioritization tool, not a prospecting strategy.

What Intent-Based Marketing Cannot Solve

Say it plainly. Intent data is not magic. It will not fix a broken sales process, compensate for a weak value proposition, or replace the need for excellent SDRs, even in highly data-driven marketing environments. What it does, when implemented correctly, is give your team an unfair advantage: the ability to engage buyers while they are still making decisions, with context about what they care about, before competitors even know they exist.

The companies from this content library’s own framing are the hyper-active B2B buyer. Fixated on making the right choice. Under pressure to justify every decision. They go with the vendor that has burned them the least, not necessarily the best one. Intent data gets you into the room. It does not win the deal. The relationship, the relevance of the conversation, the trust built over the engagement, that is what closes.

Intent Data Improves Timing, Not Product Quality

Intent-based marketing is a better way to find who is ready. It is not a replacement for being worth buying from.

How to Start Intent-Based Marketing Without Enterprise Budgets

The vendor landscape is noisy and expensive. Bombora, 6Sense, Demandbase, Intentsify: these are serious platforms with serious price tags. For mid-sized B2B teams, the path to intent-driven marketing does not require a fifty-thousand-dollar platform. Start with first-party signals, website visitors and ad engagement, layer affordable third-party data, and activate with coordinated execution supported by practical marketing automation tools.

Building a Simple Intent Data Workflow

The sequence matters more than the tools. First, install website visitor identification so you know which companies are on your site even when they do not convert. Second, define the topics and behaviors that indicate actual buying intent for your specific solution by studying your own marketing KPIs and conversion patterns. Not generic engagement. The signals that correlate with your closed-won deals. Third, build the activation workflow. What happens when an account hits a threshold? Who gets notified? What do they do with it?

A basic scoring model you actually use beats a sophisticated model that sits in a spreadsheet.

Why Speed Matters in Intent-Based Marketing

The entry point is simpler than most teams assume. The discipline to act on the signals quickly and specifically is what most teams are missing, especially in organizations struggling with marketing and sales handoff. B2B buying cycles are compressed. The window between actively researching and selecting a vendor can be as short as two to four weeks for mid-market deals. If your intent data has a 14-day delay and you take another week to act, you are too late.

Speed is the variable most platforms ignore in their pitch decks and most teams underestimate in their implementation plans.

The buyer is already in motion. The question is whether you find them while it still matters.

Enterprise Sales
Blogs

Detangling the Complexities of Enterprise Sales

Detangling the Complexities of Enterprise Sales

Enterprise sales isn’t about selling products to big companies. It’s about navigating a room full of competing priorities and hidden objections. How does that work?

What is Enterprise Sales?

Ask leaders to define enterprise sales, and you’ll get the same response: “selling to large companies.” Technically true. Practically useless.

Here’s the real definition.

Enterprise sales is the process of convincing an organization, and not an individual, to change. That distinction matters more than any other in this space because organizations don’t have pain.

Individuals within them do. And individuals within them are also the ones protecting their budget, their territory, and their reputation.

Your job isn’t to pitch a product. It’s to find the people who feel the pain most acutely, help them articulate it clearly enough to build a business case, and then equip them to fight for your solution internally- even when you’re not in the room.

If that sounds more like coaching than selling, that’s because it is.

Why Enterprise Deals Break Down Before They Even Start

The most common failure mode in enterprise sales happens before a single demo gets booked.

Reps burn six weeks building a relationship with the wrong person. Someone who’s engaged, responsive, even enthusiastic. But they entail an actual budget authority or political capital within the organization. They’re equal to a friendly gatekeeper.

Nice to know. Won’t close a deal.

This is the qualification problem that separates enterprise reps who consistently hit quota from those who stay perpetually busy but never quite close.

SMB and mid-market sales can be a numbers game. Enterprise sales can’t, especially when your sales goals depend on a smaller number of high-value accounts. There aren’t enough enterprise accounts to spray-and-pray your way to success. Every account you pursue costs real time, real resources, and real opportunity costs.

The ones who get this right build what’s called an ICP not a generic one, but a precise one. They rank prospects based on how closely the account matches their best-fit criteria using structured sales pipeline analysis. Anything that doesn’t make the cut doesn’t get worked. Full stop.

The Four Stages: Where Each One Actually Gets Skipped

Every enterprise deal progresses through four stages- Discovery, Diagnosis, Design, and Delivery. Most SDRs know this. Most reps also rush at least one of them, and it costs them the deal.

1. Discovery isn’t a 20-minute intake call.

It’s an extended research phase before anyone answers the phone. Reading a public company’s earnings call transcript. Understanding how the prospect’s competitor landscape is shifting with support from data analytics in sales. Finding the specific pressure points that the buying team is already navigating internally.

When reps skip this work and show up to discovery cold, they spend the whole call asking questions that signal they haven’t done their homework. Enterprise buyers notice. They don’t forget.

2. Diagnosis is where most reps rush to the pitch.

The prospect mentions a pain point, and the SDR’s instinct is to connect it with their product. Understandable but wrong.

The rep who pauses, asks one more question, and lets the buyer fully articulate the downstream consequences of that problem will walk out of that meeting with a much clearer picture of what the real business case looks like.

3. Design is the “solutioning” stage- and it’s the most misunderstood. This isn’t building a custom demo. It’s acting as a genuine consultant- understanding stakeholders’ needs, the concerns that arise from IT, Finance, or Operations, and designing a proposal that pre-emptively underscores each of those concerns.

Reps who show up with one-size-fits-all decks at this stage lose to competitors who took the time to personalize their sales collateral.

4. Delivery feels like the finish line but isn’t.

Getting the signature is the first step in a relationship, not the last step. Enterprise clients who feel abandoned post-sale churn. They also don’t renew, don’t expand, and don’t become the case study that gets your next deal through the door.

Multi-Threading Isn’t Optional. It’s Survival in Enterprise Sales.

Here’s a stat worth highlighting: the number of people involved on the buyer’s side nearly triples in enterprise deals that close successfully, compared to deals that don’t.

Most reps are single-threaded.

They find a champion, build the relationship, and rely on that person to carry the deal internally. It feels efficient. It’s actually fragile. That champion leaves the company. Gets pulled onto another initiative.

Gets outvoted in a budget meeting. And a deal that looked like a sure thing suddenly goes dark.

Multi-threading in sales means building meaningful relationships with multiple stakeholders simultaneously. the economic buyer, technical evaluator, end users, and executive sponsor. Not surface-level touchpoints. Real relationships where each person understands the value from their own perspective, in their own language.

Finance wants to know the ROI timeline.

IT wants to know what the integration looks like and who owns the implementation. The operations lead wants to know what the first 90 days look like. Each stakeholder needs a different conversation. The SDR who can navigate all of them without letting any single one become the single point of failure is the SDR who closes more enterprise deals through stronger sales enablement.

The Metrics That Actually Tell You If Enterprise Sales Are Working

Enterprise sales performance isn’t visible in a weekly activity report. A rep can make 60 calls, book 10 demos, and still be heading toward a terrible quarter if none of those accounts are the right fit.

The metrics that matter are the ones that reveal deal quality and velocity, not deal volume.

1. Win rate tells you whether your qualification process is working alongside other sales metrics that truly matter. A low win rate on enterprise accounts usually means one of two things: reps are working deals that shouldn’t be in the pipeline, or they’re losing at an unidentifiable specific stage.

2. Pipeline velocity tells you how efficiently deals are moving through each stage. A deal that stalls between discovery and proposal is a different problem than one that stalls between proposal and close. Diagnosing where the friction lives tells you where to coach.

3. Customer lifetime value anchors everything in long-cycle enterprise environments where sales performance management becomes critical. Enterprise sales are expensive, i.e., long cycles, high-touch selling, and massive pre-sales resources. The only reason that’s justified is that the LTV of a well-fit enterprise customer dwarfs the acquisition cost.

If your average enterprise contract isn’t delivering the ROI, the problem is either the pricing or the fit. And both of those are RevOps problems worth solving before adding more reps to the team.

4. Pipe coverage ratio is what gives revenue leaders actual confidence in a forecast and stronger visibility into sales analysis. Generally, you need three to four times your target in qualified pipeline to reliably hit quota. Below that, the math doesn’t work regardless of how good the rep is.

What Good Enterprise Sales People Actually Look Like

Hiring for enterprise sales based on years of experience and a polished LinkedIn profile is a reliable way to build a mediocre team.

The traits that predict real performance in enterprise accounts are harder to spot in an interview.

Patience paired with urgency: the ability to play a six-month game without losing momentum on the small steps that keep a deal moving. Genuine curiosity about the customer’s business, not just their use case. Grit that survives a hard loss and converts it into pipeline learning rather than pipeline avoidance.

Leadership matters more than most job descriptions acknowledge.

Enterprise selling is a team sport. Solution architects, customer success, legal, product- a good enterprise SDR orchestrates all of them without owning any of them. That takes influence, not authority.

SDRs who can’t lead without a title tend to struggle in accounts where the sale requires the whole company.

Where AI Fits into Enterprise Sales Now

AI’s role in enterprise sales in 2026 isn’t replacing reps but accelerating the evolution of sales teams with AI. It’s eliminating the non-selling work that was eating hours every week.

The pre-call research, which lasted for 45 minutes, now takes five with the help of modern sales prospecting tools. CRM hygiene that required manual entry after every call was automated. Follow-up emails drafted from call transcripts rather than memory. Deal risk signals surfaced before the rep even knew to look.

The reps leaning into these tools aren’t cutting corners. They’re adapting to ongoing digital sales transformation. They’re buying time back- time that goes toward the discovery conversations, the stakeholder mapping, the relationship-building that AI genuinely can’t replicate.

What AI can’t do is read a room.

It can’t detect the hesitation in a CFO’s voice when the conversation shifts to the implementation timeline. It can’t make the judgment call about whether to push for the next step or let a conversation breathe. It can’t build the trust that earns a champion the internal credibility to go fight for your deal in a budget meeting you’re not invited to.

Enterprise sales at its core is still a human sport.

The reps who figure out how to use AI to clear the operational noise will show up to those high-stakes conversations with more preparation, more context, and more time to actually sell.

The Uncomfortable Truth About Enterprise Deals

Most lost enterprise deals weren’t lost at the close but due to breakdowns in the sales process. They were lost somewhere in the middle- a Champion who wasn’t given the tools to advocate internally, a stakeholder whose concerns were never addressed, a problem that was diagnosed too shallowly to justify the budget.

The close is a formality when everything before it was done correctly.

If a deal feels like it’s being dragged over the finish line, something in the earlier stages broke. That’s where the diagnostic work lives, and that’s where the best enterprise teams invest the most attention.

Stop treating enterprise sales like big-ticket transactional selling because sustainable enterprise growth requires smarter B2B sales strategies. It’s not. It’s organizational change management. And your job is to make that change feel inevitable.

Microsoft
News

Microsoft and OpenAI Renegotiate Contract, Agree on a Payment Cap

Microsoft and OpenAI Renegotiate Contract, Agree on a Payment Cap

OpenAI capping Microsoft’s revenue share signals a major shift in the AI alliance that helped create the modern AI boom.

The OpenAI-Microsoft partnership looked almost untouchable for the last few years.

Microsoft has been pouring billions into OpenAI- offering it massive cloud infrastructure through Azure and even integrating its models across products. And in return, it became the company closest to the center of the AI explosion. It was one of the most successful tech partnerships in recent memory.

Now that the relationship is starting to change.

According to reports, OpenAI and Microsoft have agreed to cap Microsoft’s revenue-sharing payments at $38 billion as part of a renegotiated deal between the two companies. And honestly, this feels like a very clear signal that OpenAI no longer wants to behave like a company tied too closely to a single tech giant.

That is the real story here.

The cap reportedly gives OpenAI more room to work with companies like Amazon and Google while also making the business more attractive to future investors ahead of a possible IPO. In other words, OpenAI is trying to evolve from “Microsoft’s AI partner” into something much bigger- an independent AI empire.

And that shift might have been inevitable.

The AI market has become too competitive and politically crucial for OpenAI to remain tightly locked into one ecosystem forever. The company needs flexibility. It needs leverage. And most importantly, it needs access to as much infrastructure and capital as possible.

Because AI at this scale burns money at a terrifying speed.

Training frontier models now costs billions. Datacentres are expanding everywhere. Compute demand is exploding.

OpenAI reportedly expects massive infrastructure spend for the rest of the decade. So while Microsoft remains deeply important to OpenAI, this deal suggests the relationship is becoming less exclusive and more strategic.

There is also something quietly fascinating happening underneath all this.

A few years ago, Microsoft felt like the clear winner in the partnership. It got early access to the hottest AI company and positioned itself ahead of Google in the race. But OpenAI’s rise has been so explosive that the balance of power may now be shifting to the other side.

That tends to happen when your partner becomes one of the most valuable private companies on earth.

The partnership is not breaking apart, far from it. Microsoft still has enormous influence, infrastructure control, and financial upside tied to OpenAI’s success. But this feels like the moment the relationship stopped being dependent and started becoming a negotiation.

And in the AI industry, control is becoming the most valuable currency of all.

Multi-Threading in B2B Sales
Blogs

Multi-Threading in B2B Sales: Why Single-Threaded Deals Collapse in Modern Buying Committees

Multi-Threading in B2B Sales: Why Single-Threaded Deals Collapse in Modern Buying Committees

One contact is not a deal. It is a foothold. In modern B2B sales, multi-threading is no longer optional. Learn how buying committees work, why single-threaded deals fail, and how enterprise sales teams build influence across stakeholders.

Ask any sales rep about their best deal and they will tell you about the champion. The person who got it, who pushed for it internally, who believed in what was being sold enough to spend their own political capital on it.

Then ask them about their worst deal. Nine times out of ten, they had a champion there too.

That is the problem. The champion is necessary but nowhere close to sufficient. And the entire architecture of how most sales teams build pipeline, one relationship, one stakeholder, one thread into an account, was designed for a buying process that stopped existing years ago.

What Modern B2B Buying Committees Actually Look Like

The average B2B deal involves six to ten stakeholders. Enterprise pushes higher. Gartner puts some complex purchases at seventeen or more people with meaningful input into the decision. Not all of them have a vote. All of them have a veto.

This is not news. The research has been saying it for years. What remains underappreciated is what that committee actually does during a buying cycle, because it does not behave like a committee in the formal sense. It behaves like a loose coalition of people with different jobs, different pressures, different definitions of what success looks like, who are all trying to make a decision without necessarily ever being in the same room.

The CFO is calculating payback period on a spreadsheet no one else has seen. The IT director is reading the security documentation after hours. The end user is asking colleagues in a Slack channel whether anyone has heard of this vendor. The procurement lead is reviewing standard contract terms against a checklist that was written three years ago. None of them are talking to each other about it as much as you assume they are.

Your champion is one of these people. They are selling internally for you. But they are doing it without your information, against objections you cannot hear, in conversations you are not part of. And they can only go so far before they hit a wall they cannot climb alone.

Why Single-Threaded Sales Strategies Fail

Sales Navigator exists. Intent data exists. ICP research has never been easier. And yet multi-threading remains the thing most sales teams know they should be doing and are not doing well. Teams using tools like LinkedIn Sales Navigator often discover that access to contacts alone does not guarantee account penetration.

Part of it is effort. Building genuine relationships across an account takes more than adding five more contacts to a sequence. Part of it is the comfort of the champion relationship. When someone in an account likes you, it feels like progress. Following up with their colleagues feels like it might upset that dynamic.

But the math is cold. Deals with only one active stakeholder relationship have a higher loss rate than deals where multiple people in the account have had real conversations with the selling team. Strong sales pipeline analysis often reveals that broader stakeholder engagement improves conversion consistency across enterprise accounts. Not because the champion was bad. Because the champion was one person carrying a weight that required a coalition.

The multi-threading piece in this library captures it accurately: organizational power has moved away from single decision-makers. The CSO and the CFO and the VP of IT are not going to rubber-stamp a champion’s recommendation without having their own questions answered. That is not obstruction. That is their job.

The Entry Point Problem in Enterprise Sales

Here is where it gets practical and also uncomfortable.

Most reps get an intro to one person. Sometimes it is warm, sometimes cold, but the entry point is usually a single contact. The instinct is to work that contact hard before broadening out. Establish trust first, then expand. Logical. But modern sales process frameworks increasingly emphasize expanding stakeholder engagement much earlier in the cycle.

The problem is that “establish trust first” often turns into six weeks of nurturing a single relationship while the rest of the buying committee forms opinions about your company from your website, your competitors’ sales reps, and whatever your champion happens to mention in passing.

By the time the rep thinks it is time to expand, the committee has already started filling in blanks. Sometimes favorably, sometimes not. Either way, the rep is walking into relationships where the other person has context the rep did not give them.

Why Multi-Threading Early in the Sales Cycle Matters

The better sequencing: enter wide earlier than feels comfortable. Not with a full sales pitch to every stakeholder simultaneously. That is not multi-threading, that is mass outreach with a custom field. The goal is to have enough presence across the account that each person who matters to the decision has had at least one genuine conversation before the formal evaluation stage. This is where disciplined sales cadence planning becomes critical to maintaining consistent communication across stakeholders.

Mapping the Hidden Influence Network Inside Buying Groups

Every buying committee has a structure that is not on the org chart.

There is the technical evaluator who will never sign but whose thumbs-down kills deals. There is the internal champion for the incumbent vendor who is not loud but whose skepticism matters. There is the person two levels below the economic buyer who does all the actual research and summarizes it upward. There is the legal or procurement contact who arrives late in the process and resets everything.

None of these roles are labeled. They surface through conversation. And you can only surface them if you have conversations beyond the one contact who agreed to take your first meeting.

The multi-threading framework in this content library calls this identifying decision-makers, but the real task is mapping the influence network, which is not the same thing as the authority structure. The person with the most influence in a buying decision is not always the one with the highest title. Sometimes it is the head of IT who has been burned by a bad implementation before and whose “I have concerns” carries more weight than any executive sponsor.

How to Identify Hidden Stakeholders in B2B Deals

Finding this person matters enormously. Being introduced to them by your champion before they form independent concerns about your solution is one of the highest-value moves in a complex sale.

Tailoring Sales Messaging for Different Stakeholders

The pitch does not travel. This is the thing most reps learn the hard way.

A compelling ROI case that won over the CFO is not compelling to the end user who is worried about how much their workflow changes. A technical architecture conversation that satisfied the IT director is not the conversation the CMO needs to have. These people are not evaluating the same thing. They are each answering a different question about whether this decision is the right one from where they sit.

Ciente’s email pieces make this exact point about buying committees: marketing messages must acknowledge all the perspectives involved. Strong sales and marketing alignment helps ensure every stakeholder receives messaging that reflects their priorities. That is not just a marketing principle. It is the core sales discipline of multi-threaded selling. Every person in that account is running a different cost-benefit analysis, and your job is to show up to each one with the version of the conversation that speaks to that calculation. Effective sales personalization strategies make those conversations far more relevant across large buying groups.

Stakeholder-Specific Value Propositions in Complex Sales

This requires listening before repeating. The CFO conversation reveals concerns the IT conversation does not. The IT conversation reveals constraints the end user conversation does not. If the rep is carrying information from one relationship into the next and connecting those threads, the multi-threaded approach starts to compound. Each conversation makes the next one more relevant. The account starts to feel like the rep actually understands their organization, because the rep actually does.

Why Your Sales Champion Cannot Carry the Entire Deal

This is the thing champions want to believe and sellers want to believe: if the relationship is strong enough, the champion will carry it.

Sometimes. In smaller deals with less political complexity. In purchases where nobody else really cares. In organizations where one person genuinely holds that much influence.

Not reliably. Not in enterprise. Not in anything where the CFO has a number on the line or the IT director has a security checklist or the end users have a change management concern.

The champion is your ally inside the account. They are not your substitute. Sustainable enterprise selling depends on broader multi-threading in sales practices that reduce overreliance on a single internal advocate. They cannot answer objections from the IT team because they are not the IT team. They cannot make the ROI case to the CFO with the same credibility the selling team can. They can open doors. They cannot walk through them for you.

Building Trust Across the Entire Buying Committee

The buying committee operates on something that Ciente’s email writing calls trust that has to be built through process. The operative word is each. Each person in the committee is making a trust decision independently, not just following the champion’s lead. Multi-threading is the only way to be present for each of those decisions rather than hoping the champion’s recommendation travels intact.

Where Multi-Threaded Sales Execution Breaks Down

Building multi-threaded relationships is messier than building one good one. The communications get more complex. Different contacts know different things about where the deal stands. This is why scalable sales enablement platforms are increasingly important for maintaining consistency across enterprise conversations. One person gets a more detailed conversation than another and later compares notes.

The multi-threading piece in this library is direct about this: these are leaders from the same account and they are talking to each other. The way the sales process is conducted is a reflection of the organization. If three people at the same company are getting different stories, different levels of responsiveness, or different versions of the value proposition, that inconsistency surfaces. It damages trust not just with the person who noticed it but with the whole account.

Maintaining Message Consistency Across Stakeholders

The discipline required is not just building multiple relationships. It is keeping them coherent. High-performing teams often rely on structured sales performance management practices to keep stakeholder communication aligned throughout the cycle. Knowing what each person knows. Following up with each one at a pace that does not feel neglectful or overwhelming. Bringing new information to each relationship without creating information asymmetry that becomes uncomfortable when those people talk to each other.

There is no tool that manages this for you. Sales Navigator gives you the map. The judgment about what to say to whom and when is still the rep’s.

Multi-Threading After the Deal: Renewals, Expansion, and Retention

Maintaining relationships across a buying committee after the deal closes is where multi-threading pays its longest return and where almost no one invests.

The same committee that evaluated and approved the purchase is the committee that evaluates and approves the renewal. The IT director who had concerns during the sale is watching implementation. The CFO who approved the ROI case is waiting for the numbers to show up. The end user who worried about workflow disruption is living with the change now.

The relationships that were built to win the deal are the same relationships that determine whether the deal expands. Long-term account growth often depends on strong sales enablement strategies that continue after the initial close. The rep who disappears after the close and reappears eleven months later for the renewal conversation is the rep managing a 50/50 renewal. The rep who stayed present across the committee, even lightly, is the rep managing a conversation about growth.