Salesforce embarked on a yearslong sales transformation journey to prepare ourselves for a future grounded in agentic AI. See what we learned and how it can help your own transformation.
After 20-plus years of helping companies streamline and overhaul their sales operations, we were confronted with the need to modernize and future-proof our own business. Process complexity and tech debt were slowing us down, hurting both customer and seller experiences.
We knew that if we wanted to leverage the power of AI (especially agentic AI) and simplify processes for our sellers and customers, we would need to rebuild Sales Cloud, our core CRM, from the ground up. That transformation journey began in 2023, and we’re already seeing increased revenue, higher customer satisfaction, and faster time to close.
In this guide, we’ll share how we did it, what we learned, and our tips for executing a sales transformation journey of your own.
Fuel your sales transformation with Sales Cloud, a unified platform that boosts productivity from pipeline to paycheck — with AI and agents to help along the way.
Startups might rely on easy solutions early on, but over time, these result in disconnected processes and tools. That means a lot of ad hoc fixes and manual work — a sign your business is ripe for sales transformation.
If you’re seeing the same downward trends in customer satisfaction survey results quarter over quarter, it could be a sign of endemic issues you need to address.
Sales teams spend too much time on routine tasks like outreach and quoting, while slow deal approvals stall deals.
Missing fields, outdated information, and disconnected systems lead to a lot of data porting and repairing — and low confidence in your reporting.
Data errors, failed messages, and constant support issues plague operations. Recurring problems tie up support teams.
New AI tech and tool integrations hit technical roadblocks, requiring manual fixes that lead to significant repair and maintenance work — not time saved.
We regularly conduct internal surveys to gauge seller experience, and noticed that they revealed persistent issues: slow quote generation and approvals, confusing products and pricing, and insufficient seller enablement.
Further interviews and surveys revealed the core issues: outdated tech and processes. Sales Cloud, our core CRM, had become overly complex due to extensive development and integrations. This patchwork system made our sales process cumbersome and slow — while also making it difficult for us to effectively utilize AI.
This isn’t just about process. It’s about helping sellers succeed and helping customers buy from us more easily.
Jay DesaiVP & Program Lead, Salesforce
With evidence that there’s a problem, you have to dig into the specifics. The following actions can help clarify those problems into an actionable framework.
Begin by asking questions based on your insights to understand the root of the problem, focusing on key details (using who/what/where/when/why/how as your guide). In our case, the questions were largely tied to validation of our hypothesis: Tech was at the root of our struggles.
Next, meet with sales and operations leaders across all product verticals to gather qualitative feedback. Ask them for specific information about where they’re seeing problems.
Lastly, analyze sale performance data, looking for trends like declining revenue, stagnant win rates, and longer deal cycles, to identify problem areas needing attention.
After compiling notes from your interviews and reports, break down your issues into clear, actionable categories based on the following:
Track problems in a table or spreadsheet, categorizing each and rating its impact on sales targets, internal efficiency, and customer satisfaction using a 1-5 scale (1=low, 5=high). Sum the scores and prioritize addressing the issues with the highest combined impact.
Map high-level solutions to problems using the who/what/where/when/why/how framework as a guide. For example, if managers struggle with swivel-chairing between systems to track deal status, hindering performance insights and strategic adjustments, propose a solution like this to address it:
Pro tip: Be forward-thinking in your solution mapping. For example, if you want to make the most of agentic AI (you do), consider tech solutions that allow you to grow into new agentic innovations without having to switch up your tech stacks again in a few years.
With problems identified and solutions mapped, engage stakeholders to gain buy-in on the need for a sales org-wide transformation. At a minimum, set up meetings with your VP of sales or chief revenue officer, the head of your sales operations team, and your IT lead or CTO/CIO. Share your outline of problems and solutions.
Start by aligning on the priorities you've established. Then, confirm the solutions at a high level. Listen to objections, take notes, and adjust your plan accordingly.
Starting in 2023, we conducted extensive internal research with sellers, leaders, and ops teams to identify problems and map solutions. We compiled feedback and iterated on our transformation plan, then shared it with leadership for approval.
We needed a unified platform to consolidate functionality and data across our products: Sales Cloud (CRM), Revenue Cloud (quote-to-cash), and Salesforce Spiff (incentive compensation). That meant rebuilding Salesforce technology with a single platform vision, allowing us to streamline workflows for our sales teams and giving us the foundation to build out AI capabilities — including agentic AI.
Importantly, this rebuild prioritized Slack as our virtual office — the place where our teams could address deal issues together and share ideas in real time, enabling them to close faster.
Solution in hand, we went to executives for their feedback.
Technical debt from 26 years of investment in our legacy CRM instance is hindering our ability to keep up with AI advancements. We need to start fresh rather than continuing to patch the system.
To minimize disruption to sellers during the transition, we propose a two-tier program: one to improve the existing experience and another to build out our future state with extensive change management to keep sellers informed.
We plan to create dashboards to track factors like ACV gains, deal cycle time reduction, and seller productivity (based on metrics like meetings booked) in the new instance of Sales Cloud.
See how we identified key indicators that prompted our transformation, and how we implemented strategies to pave the way for AI and Agentforce.
Executive buy-in secured, it’s time to lay out the steps toward change. Here's our pilot-to-execution approach with change management best practices and key considerations.
Form a core team with leaders from sales, operations, admin/IT, finance, and legal to spearhead the transformation, ensuring diverse perspectives are included. Quick note: Don’t include more than one person from each department. The bigger the team, the slower you’ll be able to execute.
Here are the elements you need to consider as you dig in:
Running smaller pilots with specific targets helps test the waters before preparing to scale. For example, pilots for specific segments or product lines, or in certain regions, can protect revenue and ease the adoption of change, while still showing clear impact.
In 2023, we began our transformation by building a cross-functional “tiger team” of executives, operations pros, IT leads, and sales representatives to plan and execute the transformation. They also handle key support functions like change management and communication.
When we were ready to start, a new internal sales workspace was developed and piloted with a small group, followed by a gradual rollout to larger teams.
Data is the foundation of accurate, impactful AI outputs. When clean and comprehensive, it allows AI reasoning engines and large language models (LLMs) to generate highly relevant, personalized outputs (like deal recommendations or prospecting emails).
That’s why we prioritized cleaning and harmonizing our siloed, multichannel data. We created a cross-functional team specifically responsible for data auditing and governance, and asked them to make sure our data was ready for funneling into a newly built CRM.
In house, we rely on Data Cloud to harmonize all of our data. Since Data Cloud is designed to bring together data from these different sources seamlessly, it improves the quality of AI outputs. To learn more about Data Cloud for sales, click here.
Focusing on how we could improve inefficiencies in our existing processes, we prioritized opportunities to add automation to existing workflows.
Knowing a true sales transformation would require longterm tech overhaul, we also mapped a rebuilding plan for our internal sales CRM. We identified critical feature updates to include in early product development, aligning to our solutions in step 2, and mapped these out over the upcoming fiscal year. (Note: Most teams will not focus on deep product development as part of their transformation like we did, but will lean on outside tools that better serve their needs.)
A pilot group of early-career SMB account executives was chosen to test new-build initiatives. Their feedback led to adjustments in the timing and scope of our "jobs to be done."
Finally, we planned quarterly check-ins with our pilot group to assess new feature releases.
To tie clear value to our work, we made dashboards to track ACV growth, deal cycle time reduction, user adoption, and seller productivity (using metrics like activities completed). We reported these to leadership each quarter.
(To learn more about the KPIs you need to track for sales success, check out this guide.)
To ensure successful seller adoption of the new CRM, regular project updates, training, and support were provided. This included documentation, email alerts, training sessions on new tech functionality, and Slack feedback channels. We also mapped out new workflows to make sure sellers knew where this updated tech would impact their work.
With every release, we baked in time to fix issues our sellers surfaced, and adjusted the next rollouts based on what worked and what didn’t. We conducted seller transformation surveys and review sessions ahead of quarterly drops to help validate pain points and prioritize fixes. We measured feature adoption to assess what was working, and roadmap adjustments were made in response to insights.
Use a structured launch cadence with automated reminders to build awareness and readiness.
Equip leaders with messaging to tell a consistent story, highlighting successes and connecting changes to business impact.
Keep sellers energized with a mix of enablement events and training programs while keeping channels open for feedback.
Align updates to major release moments and create tailored enablement resources in accessible hubs to support adoption.
The next step in the sales transformation is the fun part: bringing in agentic AI, like what we offer with Agentforce. Agentforce is already making waves across industries, unlocking higher levels of productivity for sales teams by taking on manual tasks so humans can focus on high-value work like relationship-building and strategy.
Before you start using agentic AI, find where it can have the biggest impact based on your problem and solution mapping. What manual, cumbersome process can you offload to AI sales agents? Or, to be more forward-looking: Where do you need to scale quickly?
It can be helpful to use this framework: AI agent capabilities — what we call “skills” within Agentforce — slot into sales teams to work a lot like humans. Some skills focus entirely on one area in their department, like capturing and arranging data. Others work cross-functionally, doing work across the business (by connecting service and sales teams, for instance). And some come with a highly specialized skill set configured for a specific use case.
With Agentforce for Sales, built on the Salesforce Platform, there are several sales skills available out of the box that can be customized and deployed in under 30 minutes. Once built and deployed in Salesforce, these are also available directly in Slack where sales teams conduct critical deal work.
Additionally, Agentforce skills can be built and configured on the platform to meet specific use cases, leveraging preexisting workflows.
Agentforce skills are available for almost any sales use case, but prebuilt skills are the easiest to set up and deploy; you can use these to help with prospecting and coaching. With a bit more customization work, the quoting skill can help you build custom quotes in a few minutes.
Helps you engage inbound leads, nurturing prospects by asking targeted questions, handling objections, and booking meetings with reps.
Provides personalized feedback on recorded sales pitches, and role-plays sales conversations to help sellers overcome likely objections and improve sales-call performance.
Creates complex quotes with plain-language prompts leveraging pricing, packaging, compliance, and sales data from your CRM.
An analysis of the lead-to-cash flow revealed our quoting process as a key area for productivity improvement. Configuring quotes for complex deals was not only time-consuming, but they took forever to get approved. To address these delays, a pilot group tested a customized Agentforce quoting skill, which built quotes using plain-language prompts.
We’ve already seen big wins. With access to up-to-date product and pricing information — as well as built-in finance guidelines — Agentforce can generate and deliver finance-approved quotes within minutes.
We’ve deployed other prebuilt Agentforce skills, too, and are seeing rapid scaling as reps and agents work seamlessly together to prospect and prepare for sales calls.
Agentforce can act as a team member, creating multiple variations of quotes, giving sellers more flexibility when presenting options to customers.
Meredith SchmidtEVP & GM, Revenue Cloud, Salesforce
(as of April 2025)
Sales transformation, while complex and costly, necessitates clear metrics and regular reviews to identify successes and obstacles. Below, we outline the metrics you need to track and when you need to track them.
At this point, you should have dashboards and reporting structures in place to help you track critical transformation KPIs (this was part of your work in step 3). Report on these to your core transformation team and key stakeholders monthly (at a minimum).
When you present the latest metrics, give stakeholders just the information they care about and link out to the rest so they can review on their own time. Also, be sure to include a plan for addressing any KPIs that are below target.
We made sure performance data was accessible to leadership via executive dashboards in order to track value realization with every quarterly release. That value was based on time savings, process simplification, and compliance improvements.
To smoothly transition sellers to the new CRM, quarterly releases were designed to introduce new functionality gradually. Our goal: full seller adoption of the new technology within a year. During this process, user activity was monitored in both systems to identify workflow issues caused by users working in two CRMs.
We kept sellers and operators at the center of the change by making sure we constantly gathered qualitative feedback via readily accessible channels. For example, we gathered feedback from Slack polls and formal quarterly surveys. We also created seller panels to surface common issues and walk through pain points.
Our years-long sales transformation journey started before the agentic AI zeitgeist. But the rapid proliferation of AI tools helped us realize where we could use AI to solve for inefficiencies and future-proof our product. We’re still exploring this area and rolling out new Agentforce skills regularly.
We’re also piloting functionalities with different seller segments, making adjustments based on key KPIs like adoption rates, alongside qualitative feedback.
Like we mentioned above, Agentforce skills slot into teams a lot like humans. They need onboarding, regular check-ins, and management. With Agentforce deployed, we monitor its activity and report on it weekly to make sure it’s on the right track.
We’re only two years into our multiyear transformation, but we’re already seeing significant wins — and gathering many insights. Below are some high-level learnings you can apply as you begin your own transformation.
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