Salesforce Buys Momentum to Automate Data Ingestion for Agentforce
Ai4
Eliminating manual CRM entry through background signal capture enables autonomous AI agents and real-time coaching to boost rep selling time and forecast precision.
## Background
Published on YouTube by Ai4 on August 20, 2026, this presentation features Salesforce's Jordan Nettles and Ashley Blackwell-Guerra. According to the page description, it addresses how Agentforce Sales, powered by Momentum, eliminates revenue blindspots by automatically capturing and analyzing multi-channel customer interactions natively within Salesforce.
## TL;DR
Ashley Blackwell-Guerra and Jordan Nettles detail how fragmented, non-native point solutions cripple sales organizations with poor data quality and forecasting blindspots. They introduce Agentforce Sales powered by Momentum, a native three-layer architecture covering automated capture, structured CRM enrichment, and agentic workflows. Illustrating real-world enterprise implementations like Ramp and Zscaler, they show how automated background ingestion populates methodologies like MEDDPICC and drives real-time coaching via Slack and Teams. Ultimately, this native orchestration model eliminates administrative burden, sharpens forecast accuracy, and aligns cross-functional GTM teams.
## Atmosphere & style
The session carries a polished, authoritative, and strategic tone typical of high-level enterprise technology presentations. Ashley delivers a clear, analytical framing of RevOps pain points with operational pragmatism, which Jordan seamlessly complements with practical, scenario-driven demonstrations and relatable anecdotes. The narrative moves smoothly from highlighting structural industry friction to delivering a confident, forward-looking vision of proactive sales execution.
## Key takeaways
- [00:04](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=4s) Sellers spend only 40% of their time selling due to administrative burden, while 46% of sales teams fear poor data quality severely limits AI agent performance.
- [00:04](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=4s) Operating 15 or more non-native point solutions creates fragmented reporting, operational friction, and severe forecasting blind spots for RevOps leaders.
- [03:23](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=203s) Agentforce Sales powered by Momentum operates as a native three-layer architecture: Capture (interactions), Analyze (structured extraction), and Action (autonomous AI workflows).
- [06:44](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=404s) The platform automatically intercepts multi-channel customer interactions—including phone calls, emails, Zoom meetings, and in-person lunches—to eliminate rep manual data entry.
- [06:44](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=404s) Ramp leverages Momentum to automatically populate over 25,000 custom CRM fields weekly, unlocking detailed projection metrics such as 6-, 12-, and 18-month customer lifetime value.
- [12:16](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=736s) Zscaler deployed Agentforce Sales across 4,000 GTM employees to deliver personalized data feeds and real-time deal alerts directly within Slack and Microsoft Teams.
- [12:16](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=736s) Automated coaching loops monitor discovery calls and immediately deliver private 5-minute video clips in Slack when reps fail to secure concrete next steps.
- [17:15](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=1035s) Replacing manual CRM record-keeping with native automated data capture saves sales teams tens of thousands of hours annually.
- [17:15](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=1035s) Unified customer context in Salesforce standardizes global QBRs, product marketing launches, and cross-functional pricing or messaging strategies.
## Detailed walkthrough
### [00:04](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=4s) The Broken State of the Modern Revenue Tech Stack
Ashley Blackwell-Guerra opens the session with a standard corporate forward-looking statement disclaimer before introducing herself alongside co-speaker Jordan Nettles. Jumping straight into market realities, Ashley presents two sobering metrics: competitors are rapidly adopting signal-based selling, while **46%** of sales teams worry their AI agents' efficacy is severely constrained by poor data quality, and sellers waste so much time on administrative overhead that they spend only **40%** of their total time actually selling. Drawing on her background in operations and technology procurement, she emphasizes that most companies do not have a single source of truth; instead, they operate with 5, 6, or even 15+ disparate point solutions. ICs (individual contributors) and managers are forced to cobble these tools together manually, causing severe tool fatigue, friction, and fragmented reporting across executive dashboards. Ultimately, revenue leaders are left relying on guesswork over certainty, creating massive blind spots in forecasting.
```
┌────────────────────────────────────────────────────────────────┐
│ FRAGILE POINT SOLUTIONS │
│ Gong / Chorus │ Salesloft / Outreach │ Zendesk / Gainsight │
└───────────────────────────────┬────────────────────────────────┘
│ Manual updates & fragmented data
▼
┌────────────────────────────────────────────────────────────────┐
│ STRESSED AI LAYER │
│ Messy Data • Forecasting Blindspots │
└────────────────────────────────────────────────────────────────┘
```
The typical RevOps stack relies heavily on disconnected point solutions: call intelligence platforms like Gong or Chorus, outbound engagement platforms such as Salesloft or Outreach, and post-sales ticketing or customer success tools like Zendesk or Gainsight. Because these systems are not natively integrated on top of the Salesforce CRM, they force organizations to adapt to external data models rather than their own. This places a immense strain on the enterprise AI layer, leaving critical revenue initiatives either purely aspirational or limping along. Consequently, gaining a real-time pulse on pipeline, delivering live frontline coaching, predicting renewals or expansions, and routing key post-sales signals remain frustratingly out of reach.
- **46%** of sales organizations express deep concern over data quality impacting AI agent efficacy.
- Sellers spend only **40%** of their working hours actively selling due to administrative friction.
- RevOps tech stacks routinely split customer context across **15+** non-native point solutions.
---
[03:23](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=203s) Agentforce Sales Powered by Momentum: The Three-Layer Architecture
To eliminate these structural revenue blind spots, Salesforce introduced **Agentforce Sales**, powered by its acquisition of **Momentum**. Ashley positions this as the industry's only native Salesforce revenue orchestration system capable of capturing every customer interaction in real time. Rather than fragmenting data across multiple repositories, Momentum creates a unified foundation that operates as a "three-layer cake":
Looking across the horizontal revenue lifecycle—from stage-one SDR/BDR outreach to pre-sales engineering handoffs, capacity planning for professional services, deal close, and post-sales renewals—organizations consistently fail because cross-functional context gets lost. By automating the capture and analysis layers natively inside Salesforce, Agentforce Sales enables autonomous AI agents to execute specialized workflows across every role. SDRs receive automated prospecting assistance, frontline managers gain automated pipeline hygiene and forecasting precision, AE and SE teams receive seamless handoffs, and CSMs gain predictable renewal and expansion signals.
- **Layer 1 (Capture):** Silently monitors every context stream including email exchanges, Zoom meetings, and phone calls.
- **Layer 2 (Analyze):** Automatically structures raw conversation text and maps it directly into standard and custom CRM fields.
- **Layer 3 (Action):** Empowers autonomous agents and human reps to execute revenue workflows based on governed, real-time data.
---
[06:44](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=404s) Automated Data Capture and Structural CRM Enrichment
Jordan Nettles takes over the presentation to demonstrate how Momentum operates in practice, illustrating how unstructured buyer conversations are converted into structured CRM intelligence. Whether a rep is on a Zoom call, having a face-to-face lunch, making a cold call through a dialer, or emailing a prospect, Momentum automatically intercepts the interaction at the source. In 2026, simple meeting summaries are no longer sufficient; data extraction must strictly align with a company's specific business process. For instance, during an active product pilot, Momentum identifies open technical questions, buyer success criteria, and key blockers required to move the deal toward signature.
In traditional sales environments, critical CRM fields remain blank because reps dislike manual data entry. Momentum eliminates this dependency by writing enriched context back to Salesforce autonomously. Sales frameworks like MEDDPICC, BANT, SPICED, or Challenger Sale are dynamically populated from raw transcripts and emails without rep intervention. For example, enterprise fintech customer **Ramp** uses Momentum to automatically update over **25,000 CRM fields per week**. By automatically logging metrics like estimated card spend, expected monthly transaction volume, and total physical cards needed during early SDR calls, Ramp can accurately project customer lifetime value at 6-, 12-, and 18-month intervals.
- **Ramp** automatically updates **25,000 CRM fields weekly** via Momentum's background ingestion.
- Recalls a story from SaaStr where a prospect paused sales conversations 8 months prior due to a budget freeze; using Momentum, the team showed the prospect an automated agent scheduled to follow up precisely when their competitor's contract expired.
- Automatically captures hyper-specific deal terms, competitor contract expiration dates, and executive quotes across 7+ hours of multi-channel dialogue.
---
[12:16](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=736s) System of Action: Live Pipeline Management and Slack Coaching Loops
Moving beyond record-keeping, Jordan demonstrates how Agentforce Sales turns captured data into proactive sales action. Instead of forcing reps or CROs to log into separate point solutions, Momentum works invisibly in the background. Enterprise deployments—such as **Zscaler**, which runs Momentum across **4,000 go-to-market employees** spanning SDRs, AEs, account managers, and professional services—provide each role with personalized data feeds seamlessly. The system analyzes historical deal records to surface actionable pipeline updates, such as flagging closed-lost accounts from 8 months ago and scheduling outreach for September because the prospect's current vendor contract expires in November.
```
┌────────────────────────────────────────────────────────────────┐
│ MOMENTUM EVENT LISTENER │
│ Monitors call audio & transcript for key coaching moments │
└───────────────────────────────┬────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────┐
│ SYSTEM OF ACTION (SLACK / TEAMS) │
│ • Private Manager DM: 5-min clip of missing next steps │
│ • Public Channel: SDR objection handling win broadcast │
└────────────────────────────────────────────────────────────────┘
```
To drive immediate behavioral change, Momentum connects directly into communication hubs like Slack and Microsoft Teams. Rather than requiring enablement managers to spend hours scrubbing call recordings for coaching moments, Momentum automatically clips relevant audio/video segments (e.g., 7-minute curated clips of reps positioning a newly launched product) and pushes them directly to sales leadership. Jordan shares a personal anecdote from Momentum's pre-acquisition days: after noticing a sharp conversion drop between Stage 2 and Stage 3, leadership used Momentum to discover reps were routinely failing to secure firm next steps at the end of discovery calls. The VP of Sales set up an automated rule that triggered a private, automated Slack message containing the exact 5-minute call clip directly to the AE and VP whenever a call ended without a concrete next step, turning enablement into an automated, real-time coaching loop.
- Deployed across **4,000 go-to-market staff** at **Zscaler** across SDR, AE, AM, and Professional Services teams.
- Replaces manual call auditing by surfacing curated 7-minute video clips of product positioning directly to enablement leads.
- Automatically sends private coaching alerts in Slack when discovery calls lack actionable next steps, while broadcasting SDR objection-handling wins to public team channels in Microsoft Teams.
---
[16:22](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=982s) Transformational Impact on Revenue Operations and Culture
Ashley closes the session by projecting what revenue organizations look like 6 to 18 months after deploying Agentforce Sales powered by Momentum. When data capture, analysis, and execution are fully automated and governed natively within Salesforce, operational teams experience a fundamental cultural shift. Internal roles and responsibilities evolve: revenue leaders move away from tactical data chasing and pipeline auditing, allowing operational teams to proactively discern critical business trends and execute strategic programs alongside AI agents.
- Shifts organizational culture from reactive CRM data entry to proactive, agentic execution.
- Enables revenue leaders to make confident forecast calls backed by real-time customer signals rather than seller speculation.
[17:15](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=1035s) Shifting GTM Roles and Operational Behaviors
With the removal of manual data entry, sales organizations must redefine expectations across every role and phase of the revenue pipeline. Frontline managers need clear guidance on how to leverage automated insights during 1-on-1s, team calls, and pipeline reviews. Because automation saves tens of thousands of hours annually while improving overall data quality, leadership must proactively guide teams on how to reallocate this newly freed time. Enablement teams—responsible for defining and translating sales methodologies into field execution—must collaborate closely with revenue leaders. They need to supply real-time insights, complete with video clips, directly to frontline managers and individual contributors (ICs) so sellers can pivot their performance dynamically.
Beyond direct field coaching, this consolidated customer context unlocks broader organizational consistency. Product marketing gains direct visibility into field execution to ensure reps stay on message during strategic launches. Standardizing metrics and reporting structures enables global Quarterly Business Reviews (QBRs) and monthly retrospectives to operate from the "same hymn sheet," replacing siloed pod-level formats with faster global alignment. Additionally, Chief Revenue Officers (CROs) and RevOps executives can leverage objective conversation data to align with cross-functional partners in marketing, pricing and packaging, and corporate PR. The presentation concludes with an invitation to visit the Salesforce booth to discuss key takeaways.
- Automated data capture saves tens of thousands of hours per year across go-to-market teams while increasing CRM data accuracy.
- Coaching programs leverage actionable field video snippets to correct methodology gaps instantly.
- Unifying customer context in Salesforce standardizes global QBRs, monthly retrospectives, competitor analysis programs, and process breakdown reviews.
## Notable quotes
> [00:04](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=4s) 46% of sales teams worry their AI agents' efficacy is severely constrained by poor data quality — Ashley Blackwell-Guerra
> [00:04](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=4s) sellers waste so much time on administrative overhead that they spend only 40% of their total time actually selling — Ashley Blackwell-Guerra
> [06:44](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=404s) automatically update over 25,000 CRM fields per week — Ramp
> [12:16](https://www.youtube.com/watch?v=qLAlee1Cbjc&t=736s) Zscaler, which runs Momentum across 4,000 go-to-market employees — Jordan Nettles
## Mentioned
Agentforce Sales, Salesforce, Momentum, Jordan Nettles, Ashley Blackwell-Guerra, Gong, Chorus, Salesloft, Outreach, Zendesk, Gainsight, Ramp, Zscaler, Slack, Microsoft Teams
## Video description
Jordan Nettles - Account Solution Engineer, Salesforce
Ashley Blackwell-Guerra - Director, Product Marketing, Salesforce
Every customer interaction holds a revenue signal. Most never get captured in your CRM. Agentforce Sales, powered by Momentum, changes that — automatically capturing and analyzing every call, email, meeting, SMS, and more natively in Salesforce. The result? A unified data foundation connected to the systems your go-to-market team already uses. No manual data entry. No missed signals. Just complete, governed customer context that powers AI agents and revenue workflows.
Salesforce representatives Ashley Blackwell-Guerra and Jordan Nettles argue that fragmented point solutions and manual data entry degrade AI efficacy and forecast accuracy, proposing native CRM data capture and automated action loops to eliminate revenue blind spots.
Data Fragmentation in the Revenue Tech Stack (0:04)
Modern revenue operations frequently rely on 15 or more non-native point solutions, such as Gong, Salesloft, and Zendesk, to manage distinct stages of the customer lifecycle. Blackwell-Guerra states that this architectural fragmentation forces organizations to conform to disconnected third-party data models, resulting in split context, tool fatigue, and executive dashboards filled with guesswork.
This administrative and structural friction directly impairs operational performance. According to data cited in the presentation, 46% of sales organizations fear that poor data quality restricts the efficacy of their AI agents, while individual sellers spend only 40% of their working hours actively selling due to the time required to manually update systems and reconcile data.
The Three-Layer Native Architecture (3:23)
To prevent data loss across handoffs between SDRs, account executives, and customer success managers, Salesforce introduced Agentforce Sales, powered by its acquisition of Momentum. The system is structured as a three-layer operational model operating directly inside the Salesforce CRM rather than across external repositories.
The base Capture Layer passively logs interactions across phone dialers, Zoom meetings, emails, and in-person discussions. The Analyze Layer extracts unstructured dialogue, matches it against internal business rules, and writes structured records directly into standard and custom CRM schemas. The Action Layer then feeds governed, real-time data into autonomous AI agents and automated workflows to guide rep activities.
Automating CRM Enrichment and Sales Methodologies (6:44)
Nettles explains that unstructured conversations are converted into structured pipeline intelligence without requiring manual rep data entry. The platform dynamically maps buyer statements into standard qualification frameworks—including MEDDPICC, BANT, and SPICED—capturing specific deal requirements, technical blockers, and competitor contract dates directly from raw dialogue.
At enterprise fintech customer Ramp, this mechanism updates over 25,000 CRM fields weekly in the background. By capturing early indicators like card counts, transaction volume, and expected spend directly from initial discovery calls, Ramp projects customer lifetime values across 6-, 12-, and 18-month horizons without relying on seller self-reporting.
Embedded Action and Coaching Loops (12:16)
Rather than demanding that managers and reps log into separate call-recording portals to review meetings, the platform acts as an automated system of action integrated into communication tools like Slack and Microsoft Teams. At enterprise security firm Zscaler, the system delivers role-specific intelligence across 4,000 go-to-market personnel spanning pre-sales, sales, account management, and professional services.
To drive field behavior, the system monitors transcripts for specific performance indicators and automates managerial feedback. For instance, when discovery calls conclude without scheduled next steps, the engine automatically dispatches a private message containing the exact five-minute call snippet to the AE and their manager, replacing retrospective call audits with immediate, closed-loop coaching.
Operational and Cross-Functional Realignment (16:22)
Blackwell-Guerra projects that replacing manual data management with native CRM ingestion redefines go-to-market roles over a 6- to 18-month horizon. Frontline managers pivot from pipeline auditing to data-backed 1-on-1 coaching, while leadership must intentionally reallocate the thousands of hours saved per year back into active selling.
Unifying customer signals inside the core CRM also supports cross-functional execution beyond sales teams. Product marketing gains direct visibility into whether reps adhere to launch messaging, pricing and packaging teams capture direct feedback on customer objections, and global operations can replace inconsistent, localized reporting formats with standardized data across monthly reviews and QBRs.
The presentation concludes that replacing fragmented point solutions with native, automated data ingestion can eliminate forecasting blind spots and improve AI utility; however, realizing these gains requires leadership to actively restructure management behaviors and redirect newly freed rep capacity into selling.
[0:04] Ashley Blackwell-Guerra: 所有采购决策应基于当前已发布的产品功能
[0:04] Ashley Blackwell-Guerra: 依靠猜测而非确信
[3:23] Ashley Blackwell-Guerra: 三层蛋糕（three-layer cake）
[6:44] Jordan Nettles: 它必须契合你的具体业务上下文
[6:44] Jordan Nettles: 看录像和读摘要
[10:54] Jordan Nettles: 现有竞品合同到期日
[13:26] Jordan Nettles: 你们内部先讨论一下，合适的时候再联系
[13:26] Jordan Nettles: 无需主管主动搜寻，问题会自己找上门
Source quotes
[0:04] Ashley Blackwell-Guerra: 46% of sales teams worry their AI agents' efficacy is severely constrained by poor data quality
[0:04] Ashley Blackwell-Guerra: sellers waste so much time on administrative overhead that they spend only 40% of their total time actually selling
[6:44] Ramp: automatically update over 25,000 CRM fields per week
[12:16] Jordan Nettles: Zscaler, which runs Momentum across 4,000 go-to-market employees
Original transcript
[0:04] Just as always, please base all of your purchasing
decisions on what is currently available today.
[0:09] I have to say this uh forward-looking statement
before we jump into it, but I'm Ashley. This is
[0:13] Jordan. We're going to give you a little bit of
a preview into Agent Force for Sales powered by
[0:18] Momentum, the demo, and uh a little bit of a
double click into the impact on your revenue
[0:23] teams. So, with a nod to your competitors,
um I've got two different stats. One is 46%,
[0:30] one is 40%. But your competitors are building a
revenue motion that allows them to capture and
[0:37] execute on signals and do all of that before
you're able to. Um, and what we know from the
[0:43] state of sales report is that 46% of you all out
there are worried that your agents efficacy is
[0:51] being impacted by your data quality. And what we
also know is that sellers are spending about 40%
[0:57] of their total time selling. So to me that says
you have a data infrastructure problem, you have a
[1:03] data quality problem and we have a ways of working
problem that needs to be addressed. And there's
[1:10] no secret behind it. I come from an operations
background where I was procuring technologies
[1:14] and implementing them. But what we really have are
five or six or even 15 plus different sources of
[1:20] truth because we're asking our IC's in the field
to work from these point solutions and cobble
[1:27] them together which really just means there are
friction points in your revenue tech stack which
[1:32] leads to messy data tool sprawl and tool fatigue
for your uh individual contributors in the field
[1:39] and your managers who have to learn these tools.
It means that you have a fragmented look at your
[1:44] data in terms of your reporting capability and the
accuracy of your dashboards. And the trickle down
[1:50] effect really means you're relying on guesswork
over certainty and uh you have significant uh
[1:58] blind spots in your forecasting and your revenue
uh capability. So um this is just a snapshot of
[2:05] what a typical RevOps techstack looks like. You've
got some semblance of a call intelligence platform
[2:11] like a gong or a chorus. You have different
technologies where you're doing customer
[2:16] engagement be it uh outbounding through the likes
of sales loft or outreach or even your post sales
[2:23] team handling a lot of communication to customers
through something like a zenesk uh with tickets
[2:28] or gains um as that means of post sales CRM all
the way through to your forecasting mechanisms.
[2:35] But what this really means is these are point
solutions that are not natively integrated in top
[2:40] on top of your Salesforce CRM and they're asking
you to cobble together their version of your data
[2:45] instead of really working with your version of
your data and that puts a tremendous amount of
[2:49] strain on your AI your AI layer which really
means a lot of these critical revenue programs
[2:56] are either aspirational or they're limping
along. So getting to uh like a real-time pulse
[3:02] on your pipeline, bringing frontline coaching
insights to IC's and managers in real time. Uh
[3:09] the predictability of renewals and expansions and
surfacing up key signals to your post sales teams
[3:15] for reasons to engage with customers are hard.
Next slide, Jordan. But introducing Agent Force
[3:23] for Sales that's powered by one of its most recent
acquisitions, Momentum. It is the only native
[3:29] Salesforce revenue orchestration system available
to you that allows for real- time capture of every
[3:36] revenue signal. Um, seeing data and deals clearly
in real time and orchestration happening from one
[3:44] specific platform. And so this happens in what
we like to call our three layer cake. Momentum
[3:50] is really firing on the first two levels. So that
capture piece, where are all of your customer and
[3:56] prospect conversations happening through email,
through customer conversations with your call
[4:02] intelligence uh technologies through Zoom or
email. You need a technology that's listening
[4:09] across all of those different context capture
technologies. And you need that technology to
[4:15] then structure and analyze and send into your
CRM, which really helps you unlock that third
[4:22] layer, which is the action layer and where we
all want to get to with the use of agents that
[4:27] are using reliable real-time data. And so what
this looks like, um, I've used this slide before,
[4:37] thinking about your totality of your revenue stage
cycles. So all the way from stage one where a BDR
[4:43] or an SDR starts to get involved with a customer
or prospect, there are critical revenue programs
[4:50] that you are trying to run that you simply can't
because you can't bring all of the data from those
[4:56] point solutions into one place. So you're missing
critical handoffs. You're missing signals for your
[5:04] sales engineering team. Maybe your professional
services team needs a little bit of a heads up
[5:08] as they start to capacity plan towards the end
of the quarter all the way through to closed one
[5:13] where you have that predose sales handoff all
the way through renewal. So as a revenue leader
[5:19] we need to be looking horizontally. We need to be
thinking about the programs that all of our teams
[5:24] go through by role to accelerate our deal cycles.
And that's quite frankly very hard to achieve if
[5:31] your AI layer is stressed and you do not have
native integrations into your CRM. And so the
[5:38] idea here is going back to that three layer cake.
You've achieved capture, you've achieved analyze
[5:44] and send to CRM. And now what you really want to
get to is Salesforce and sales agent that are able
[5:52] to execute on a bunch of different programs given
the role that you're talking about. So things like
[6:00] prospecting for a BDR or an SDR organization,
the the constant need for pipeline hygiene and
[6:07] forecasting accuracy for your frontline managers,
your AEES, your sales engineers, your CSMs,
[6:14] all the way through to the coaching that needs to
happen throughout these sales and revenue cycles.
[6:20] Um all the way through to the predictability again
of these renewals and expansion. So when you can
[6:26] rely on your data layer, you are able to do a
whole lot of unlock and eventually get to reliable
[6:33] agents that can take action inside of your CRM and
really work alongside the people in the field who
[6:39] need that information. And with that, I'm going to
turn it over to Jordan to give you a little demo
[6:44] of what I just talked about. Thanks for that,
Ashley. So the way that we use momentum is I
[6:51] think the most um accurately articulated just by
showing you what it actually looks like. The way
[6:55] that we actually use all of this data, this
insight to unlock all the different use cases
[7:00] in your organization. So it starts with capturing
conversations. So every single conversation that
[7:05] is taking place, whether you're with a customer,
your Zoom call, maybe you're going in person and
[7:10] having, you know, a sitdown lunch together, maybe
you're calling them over the phone or through
[7:14] a dialer with a cold call. That's four or five
different interactions that all might be with the
[7:19] same customer and need to be treated effectively.
And so what momentum does is it sits wherever the
[7:24] conversation is taking place. Be it your customer,
be it you, you're going to capture all of it and
[7:29] you're going to uh add it back to um your system
of record. And so every single conversation we're
[7:35] going to be summarizing and effectively putting
together what it looks like to have this um be
[7:40] captured into a structured format. Um the most
interesting thing about how the the 2026 landscape
[7:47] is shaping out is that uh it's not enough to
summarize information to capture signals. It has
[7:52] to be specific to your business. And so as I go
through this today, I'm going to be talking about
[7:56] not just capturing out, hey, here's a conversation
that took place, but oh, we're mid pilot with this
[8:00] customer. What are their outstanding questions?
What are the things they're looking to achieve?
[8:03] What are we hoping to actually um move forward in
order to get this customer into a good spot where
[8:08] they feel comfortable to sign? And so every
single one of those structured interactions
[8:12] is going to be used in a thousand different ways.
It starts with having your reps being able to have
[8:17] those conversations, capture it, and have this
written back to the CRM. And once this happens,
[8:22] this is where where a lot of the exciting stuff
happens. So a lot of tools uh may be um focused
[8:29] on just this experience right here, right? Show
me a video, summarize a conversation. Momentum
[8:34] and Salesforce um through uh Agent Force for Sales
are thinking of it a little bit differently. So,
[8:39] how do we actually take this information and
now do all of the work that a sales rep would
[8:43] have done before off of their plate entirely?
And so, customers know the type of information
[8:49] they should collect. It's usually one of two
types. Either they're looking to inspect deals,
[8:54] right? How do I make sure I'm forecasting an
opportunity effectively? How do I make sure I'm
[8:57] inspecting pipeline that my reps are working and
make sure they're capturing the correct signals?
[9:03] oftentimes they use a framework something like
medic spy spant challenger sale uh to momentum
[9:09] it doesn't matter right it's information that
needs to be captured out of again those dials
[9:13] those emails and so those five calls and four
email exchanges are actually going to be used to
[9:18] capture a summary of the deal according to what
your business actually needs the other type of
[9:24] information tends to be around um your agentic
base so think about anything I need to then go
[9:30] build a BDR agent to go build an effective handoff
process that belabors context from a conversation
[9:38] that happened 6 months ago to now the person in
charge of that relationship. Now, you'll notice
[9:43] that a lot of this information in your CRM is
probably blank and that's because it relies on
[9:48] someone like myself, a sales engineer, an account
executive, a BDR to be the source of that data.
[9:53] And so, the simple unlock that momentum follows is
that we don't sit um and say, "Hey, Jordan, here's
[9:59] the information we think you should push into the
CRM." We do it aically. And so we capture out the
[10:05] email exchanges, we capture out the conversations,
and we put them into one cohesive data model. And
[10:10] what that allows us to do is start to fill out
the different areas of your CRM that are relevant
[10:16] to you. So a couple of stats, one of my customers,
RAMP, updates 25,000 fields in their CRM per week.
[10:23] What that lets them do is effectively scale
um their onboarding, right? They know what
[10:28] a customer is going to be worth today. They know
what they're going to be worth in six months. They
[10:30] know what they're going to be worth in 18 months.
Why is that? They know that because during the
[10:36] conversations that the BDR had with them, they're
picking up valuable insight like, "Hey, what's
[10:40] your total estimated card spend? How much do you
expect to be running in monthly transactions? How
[10:45] many total physical cards do you need?" These
are three things very specific to a finance
[10:49] space because it's specific to their business.
A lot of us won't have that same parallel. But
[10:54] the idea of capturing the right data, putting it
in my system so that in 18 months I can draw from
[10:58] it effectively, very valuable. When we were at
Saster a couple of months ago, we had customers
[11:03] coming up to us and saying, "Hey, you know, we
ran a cycle with you uh you know, 6 months ago,
[11:08] 8 months ago, we had a budget freeze, so we had to
pause things, but we want to pick things back up."
[11:12] And it was amazing because we could turn around
our CRM and show them, hey, we actually have an
[11:16] agent scheduled to deliver an email to you in a
month because during the deal cycle I worked with
[11:21] you last year, we picked up a quote from your
head of RevOps. We picked up a date that says,
[11:26] "Hey, here's when your contract with your current
competitor expires." And we picked up the ability
[11:31] to um say, "Hey, here's how much this opportunity
is worth, and here's how many resources should go
[11:35] into it. Here's who who we should address that
email to." All of those revolve around a couple
[11:40] of data points that may have been flowing
through naturally in conversation across,
[11:44] I don't know, seven hours of calls and emails.
So, someone like myself who might have seven,
[11:49] eight calls a day, no chance we're getting
that in the CRM, but using a tool like this,
[11:53] being able to listen to those conversations,
pull out the relevant details, it's a big win
[11:58] for anything we want to do agent forward. So in
summary, capture out all of the signal that's
[12:04] already firing inside of the conversations
because if you're not doing it again,
[12:07] your competitors are going to be doing this. So
we're listening for these different details. We
[12:11] know exactly when to execute because our customers
are telling us on these conversations. Now from
[12:16] this we also get to work pipeline. Agent Force
is going to be relying on all this data now to
[12:21] suggest updates saying hey the next step for
this opportunity this account that was closed
[12:25] loss 6 months ago 8 months ago should actually
be reaching out in September because specifically
[12:31] they told you that vendor contract that they have
currently is up in November is up in December.
[12:37] Let's get a two-month deal cycle going and now we
have the opportunity to close a new opportunity.
[12:42] The cool thing is all of this is happening behind
the scenes, right? There's ways that we can make
[12:48] this yes more uh more hands-on for the reps, for
the sellers, for the managers, but in principle
[12:53] um you know a company like Zcaler where we're
deployed across 4,000 of their folks and go
[12:58] to market from SDRs, BDRs, account managers,
professional services um all of them are getting
[13:03] their own slices of data and it's all happening
behind the scenes, right? You don't want another
[13:08] tool that your reps have to go log into that
you have to convince an entire workforce to go
[13:12] spend time in. You want this happening behind
the scenes so that when your agents need data,
[13:16] when your CRO needs to make a call on their
forecast, it's already in the system. All of
[13:21] this is being used to generate insight into that
pipeline. Now, the last thing I want to showcase
[13:26] here is that it doesn't just have to live inside
of a system of record. Very commonly, we want to
[13:31] use this to live inside of a system of action.
Most commonly, this for us is going to be Slack.
[13:36] And so being able to listen for every single
opportunity that's being worked for, hey, how is
[13:41] the market actually positioning this new product
we're going to market with. Old tools will say,
[13:47] hey, come spend an hour of your day, make your
managers go log into a platform, go scrub through
[13:51] all this different content. You're spending
several hours a week trying to effectively
[13:55] find the one moment I need to coach from. Using
infrastructure, this actually flips on its head.
[14:00] So, what we're going to be doing is listening for
the exact moment when your reps are positioning
[14:05] that new product to the market and just sending
that to your head of enablement. They're sending
[14:09] this to your head of uh sales leadership. All
of this is happening behind the scenes. And so,
[14:14] at the end of the day, they're going to have
seven minutes of video content perfectly curated,
[14:19] the thing they're trying to coach on. My favorite
example of this to commonly share is uh Momentum
[14:25] had um before we were acquired by Salesforce last
year had a bit of a a conversion problem between
[14:30] stage two and stage three of our opportunities.
And so we use Momentum, we did some analysis,
[14:34] we're deep diving, we're trying to understand why
are we dropping all of these deals between stage
[14:38] two and stage three. And sure enough, we just
weren't prescribing next steps at the end of
[14:42] these conversations. So we would get to the end of
a call where a customer tells us exactly what they
[14:47] care about. They're telling us their timeline.
They're telling us their criteria to win.
[14:51] but we're not listening and we're saying,
"Yeah, go ahead and circle up internally.
[14:54] Just reach back out when it makes sense." Very
weak ending to the conversation. And so our VP
[14:59] of sales actually put together a clip where he's
listening for the last 5 minutes of conversation
[15:05] for anytime we don't give an actionable next step.
In other words, did we end a discovery call where
[15:10] a customer gives us all this valuable information
just to let that opportunity die? And every single
[15:16] time that happened, the VP of sales and an account
executive would get a message together in Slack,
[15:20] right? It's private. It's a coaching moment.
Sierra doesn't have to go look for it. It just
[15:25] finds him. So using this tech, think about being
able to find the exact moments we need to coach
[15:30] on, the exact moments we need to work with our
team to get better on. And on the converse, every
[15:34] time an STR overcomes an objection, send that
to a channel where everyone in the organization
[15:39] can see. in Microsoft Teams, send this to a team
where every STR is so they can see examples of,
[15:44] hey, here's what it looks like to actually
overcome an objection. And so all of this
[15:48] that we've been talking through today in terms
of momentum, think of it as infrastructure. We're
[15:53] sitting behind the scenes. We're taking action
all the signal that your customers are already
[15:57] giving you. We're malleating it into the exact
places it needs to go. Be it Microsoft Teams,
[16:03] be it Slack, be it Salesforce, so that your
agents can take action. so that you can call
[16:07] your forecast effectively and you're not left
having to go dig through old conversations in a
[16:12] call library to actually get at that data. Ashley,
I'm going to pass it back to you [clears throat]
[16:22] in just just a slide to leave you with. Right.
So, let's just assume that you adopt everything
[16:28] that Jordan says and you are now a revenue leader
looking, you know, 6 to 12 to 18 months in the
[16:35] future. For all of our best customers, these
are the types of programs and ideas that they
[16:41] are thinking about in the future. So, it sort
of falls into four buckets. Um, Jordan talked a
[16:47] little bit about the shift that happens when your
data is being captured and it's being analyzed.
[16:53] Suddenly your operational teams are able to really
discern critical information about what you should
[16:59] be doing and when you should be doing it. That to
me signals that there's a cultural shift happening
[17:04] in terms of the way the teams internally work
together. This is a roles and responsibilities
[17:09] shift. And we need to think about as highle
leaders what our expectations are for each
[17:15] of those roles in each of those phases of your
pipeline. Right? So, you need to be telling your
[17:21] frontline managers your expectations of how they
now use this information in their one-on- ons,
[17:25] their team calls. We need to be structuring
our internal handoff conversations across
[17:31] the business differently. Um, we need to be
prepping for pipeline calls in a different way
[17:36] simply because a lot of that manual data entry
is now lifted. So, over the course of a year,
[17:41] you're saving tens of thousands of hours and your
data is simultaneously getting better. what are
[17:46] you telling people to shift towards as a result
of this change? Uh Jordan mentioned some coaching
[17:52] opportunities from his own real life experience
inside of Momentum. Um but as an organization
[17:58] working handinhand with sales and revenue leaders
with your enablement team who is um in charge of
[18:04] establishing your sales methodology and how that
actually translates into execution. Being able to
[18:11] bring those insights not only to the enablement
team so that they understand where they might
[18:15] have to tweak their programs or build net new but
immediately to the frontline managers and the IC's
[18:21] so they get real time examples with video clips of
exactly how to pivot and change their performance
[18:28] in real time. Um it also helps everyone across
the organization stay on message. So, you know,
[18:33] if we have brand new products that are launching,
we've got a product marketing org that is really
[18:38] focused on nailing that messaging and they now
have visibility into how that's being executed in
[18:43] the field. So, your coaching programs are probably
the next frontier to start thinking about along
[18:49] with the uh analytical ability that is unlocked
by bringing all of this customer context into your
[18:55] Salesforce in a reliable way, standardizing
your QBRS globally, not just at a pod level.
[19:01] So everyone is speaking from the same format in
him sheet. It makes the efficiency of those global
[19:06] conversations a lot better and you're actually
unlocking global change at a faster rate. Your
[19:12] end of month retrospectives should change because
you should be pulling on this information that's
[19:17] available to you in real time. Competitor programs
uh really figuring out where you're having process
[19:22] breakdowns. Again, that's the relationship with
the enablement team along with frontline managers
[19:27] who are who are responsible for execution and
really what are the relationships that you
[19:32] have as a CRO or a VP of RevOps with those that
are on either side of the aisle from you. So,
[19:39] you now have real data and real context to bring
to your head of marketing and product marketing.
[19:46] Uh your pricing and packaging team to see
what's landing and what's not. um corporate PR,
[19:51] maybe there's specific language that's happening
um within your business that you need to stay on
[19:55] top of. All of that is possible because you have
brought all customer conversations into one place
[20:02] and you are orchestrating natively from your
CRM. So Jordan and I would love to talk to you.
[20:07] We do have a booth. Um, we'd love to know what
resonated and uh, thank you very much for your
