AI in sales has moved past the optional stage, but access is not the problem for most teams. Adoption is. Twenty-six percent of sales leaders surveyed by HubSpot in 2026 named difficulty adopting new tools as their top operational challenge, ahead of pipeline visibility and forecast accuracy.
![Download Now: 2026 Sales Trends Report [New Data]](https://no-cache.hubspot.com/cta/default/53/9cdc68ed-d735-4161-8fea-0de2bab95cef.png)
The cost of inaction shows up at month-end. Forty-three percent of teams lose six to ten hours a month reconciling revenue data across systems, and 22% lose eleven to twenty. AI can return at least one of those business days to every rep.
This guide explains the four categories of AI in sales, describes eleven practical use cases, and outlines a four-step process for implementing AI in your sales organization.
Table of Contents
- What is AI in sales?
- Types of AI Used in Sales
- Why AI in Sales Is No Longer Optional
- The Benefits of AI and Automation in Sales
- 11 Ways Your Team Can Use AI in Sales
- Frequently Asked Questions About AI in Sales
- How to Get Started with AI in Sales
- Getting Started with AI in Sales
What is AI in sales?
AI in sales refers to how both buyers and sellers use artificial intelligence in the purchasing process. Sellers use it to automate research, data entry, outreach, and forecasting. Buyers use it to investigate vendors, compare options, and form opinions long before a rep enters the conversation. Any honest look at the term has to cover both sides, because the buyer’s half is what makes the seller’s half urgent.
The phenomenon of buyers and sellers both using AI tools emerged more recently than artificial intelligence itself. Sales teams have been using predictive scoring and automated data capture for years. The shift is that buyers now have access to AI tools comparable to those used by sellers, and they are deploying those tools earlier in the buying cycle.
HubSpot's 2026 Sales Trends Report
This in-depth report includes sections, covering:
- How buyers are becoming more self-informed
- How sales teams are using AI and automation
- Adapting to tighter budgets
- And more!
Download Free
All fields are required.
Form not available
AI is further empowering buyers.
AI-powered search and chat tools let buyers research vendors, compare products, and set expectations before contacting a sales rep. The first meaningful conversation about a product now frequently happens between a prospect and a chatbot, not a prospect and a rep.
HubSpot’s 2025 State of Consumer Trends Report tracked the depth of that shift. Seventy-two percent of consumers plan to use generative AI-powered search when shopping, and 79% of those already doing it said the experience beat traditional search. Fifty-nine percent prefer to gather information independently rather than talk to a person. Google’s search market share fell below 90% in January 2025, the first time in a decade.
Trust lags behind adoption. One in three consumers uses AI tools like chatbots and ChatGPT, but 17% strongly distrust them and another 16% somewhat distrust them.
Prospects arrive with AI-shaped opinions they are not fully confident in, and in my experience working hand in hand with sellers and as a buyer myself, that gap is the biggest and best opening. While leads are better qualified than ever thanks to AI, their information is rarely complete or accurate. That is where the seller steps in to confirm what is right, correct what is not, and supply the context that no chatbot has access to.
Salespeople are concerned about job displacement.
Job displacement is a real concern in sales, but the evidence points toward augmentation rather than replacement. HubSpot’s 2026 State of Sales Report found that 62% of sales leaders are neutral about AI’s impact on team staffing, and only 7% expect a negative impact on staffing. Most described AI as an enhancement to the sales process rather than a driver of headcount reduction.
Sales leaders reinforce this by identifying the judgment-based decisions they still expect reps to make independently, without AI assistance. The same report found reps are expected to own these decisions independently.
- Which accounts to prioritize (30%)
- The strategic angle or hypothesis for an account (29%)
- The decision to disqualify or deprioritize a prospect (27%)
- Which contacts within an account to reach (25%)
- Timing and sequencing of touches (20%)
Only 4% of leaders said AI is involved in all of those decisions. AI handles the research and the drafting, and reps keep the calls that require reading a situation. Complex, trust-based selling still rests on judgment, and what AI changes is how much surrounding work a rep does by hand.
AI can free up salespeople to sell more effectively.
A major opportunity for AI in sales is returning time reps waste on data fixes. Sixty-five percent of salespeople lose at least a business day a month reconciling data across systems. That work exists because the underlying systems are siloed. According to HubSpot’s 2026 State of Sales Report, 72% of leaders say their tools can’t access the complete, accurate revenue data they need to act on.
Teams must integrate their data systems before implementing AI tools, because AI produces accurate results only when trained on complete, unified data. AI running on disconnected systems produces confident output from incomplete inputs, so teams that fix the inputs get more out of everything they layer on top. HubSpot’s AI prospecting agent handles one version of this directly — monitoring target accounts for buying signals, researching those accounts, and drafting personalized outreach. Research and drafting move off the rep’s plate. Account prioritization, strategy, and relationship building stay on it.
In particular, I’d caution sales teams not to ignore relationship-building, especially in B2B. Self-service works until it doesn’t, and when it fails, the answer isn’t there, the logic circles back on itself, or it’s confidently wrong. Buyers need a go-to person they can trust in those cases and when they need specific advice, troubleshooting, or other types of support. Buyer trust in a sales rep is often the deciding factor in winning or losing a deal.
Types of AI Used in Sales
Sales teams encounter four distinct categories of AI, and the differences matter when evaluating tools. Generative AI creates content. Predictive AI forecasts outcomes. Conversational AI handles and analyzes interactions. Agentic AI takes action independently.
Most sales platforms combine several of these, which is why product pages describing themselves as “AI-powered” rarely explain what a tool actually does.
Generative AI
Generative AI creates sales emails, call summaries, proposals, and other content from a prompt or a data source. It is the category most reps encounter first because the output is immediate and the use cases are obvious. Common applications include drafting follow-ups, rewriting a pitch for a different audience, and turning a discovery call into a written recap.
The limitation is that generative AI produces plausible text regardless of whether the underlying facts are right. HubSpot’s 2026 State of Sales Report found that 23% of sales managers coach reps specifically on verifying accuracy of AI-generated claims, which suggests the problem is common enough to formalize.
Predictive AI
Predictive AI analyzes historical and real-time data to forecast outcomes. In sales, that means lead scoring, deal risk detection, and revenue forecasting. Rather than generating something new, it assigns probability to something already in the pipeline.
Predictive models depend entirely on the quality of the data behind them, which is where fragmented systems cause the most damage. A forecast built on incomplete CRM records produces a confident number that nobody should trust. HubSpot’s forecasting tools apply this analysis to pipeline data to project attainment and flag deals at risk of slipping.
Conversational AI
Conversational AI supports and analyzes interactions between buyers and sellers. On the buyer-facing side, that’s chatbots handling questions before a rep gets involved. On the internal side, it’s call recording, transcription, meeting summaries, and the coaching insights pulled from them.
This category has the widest current adoption. Seventy-five percent of sales leaders use AI-powered call recording tools to transcribe and learn from prospect and customer conversations. HubSpot’s Conversation Intelligence analyzes recorded calls to surface objection patterns, deal risks, and competitor mentions across a team.
Agentic AI
Agentic AI takes action with minimal rep input. Unlike generative AI, which drafts on request, or predictive AI, which scores what already exists, an agentic AI decides what to do next and does it. That means researching accounts, building prospect lists, monitoring for buying signals, and drafting outreach without being prompted each time.
This is the newest category and the one where the gap between marketing language and shipped capability is widest. HubSpot’s agent builder handles autonomous work across the platform, with the prospecting agent covering the account research and outreach drafting sequence end to end.
Why AI in Sales Is No Longer Optional
AI adoption has moved from advantage to expectation in sales. According to HubSpot’s 2026 State of Sales Report, 69% of sales leaders say adopting AI is a competitive advantage and market differentiator, ranking it ahead of efficiency gains (57%) and better data processing (33%).
When the majority of a market treats a capability as a differentiator, the teams without it are the ones being differentiated against.
The harder problem is fragmentation. Eighty-eight percent of revenue and sales leaders agree their sales process would benefit from a single integrated platform, because the disconnect between tools is where deals slow down and slip away. Every new AI tool bolted onto a stack that doesn’t communicate adds another place for context to go missing.
CRM-connected AI works best as the practical starting point rather than standalone tools. Among sales leaders using a CRM:
- 94% report getting value from it
- 80% say their CRM is already fully AI integrated
- 45% track CRM adoption rate as a success metric, and 45% track data quality and completeness
- 95% are confident in their ability to measure CRM ROI
The last figure is the one that matters for anyone still building a business case.
HubSpot's 2026 Sales Trends Report
This in-depth report includes sections, covering:
- How buyers are becoming more self-informed
- How sales teams are using AI and automation
- Adapting to tighter budgets
- And more!
Download Free
All fields are required.
Form not available
The Benefits of AI and Automation in Sales
AI in sales delivers measurable returns. The clearest gains show up in time recovered from manual work, insight surfaced from data reps couldn’t analyze on their own, and personalization at a scale humans can’t match by hand.
HubSpot’s State of Sales report data shows how all three benefits compound. Automating admin work produces cleaner data, cleaner data produces better insight, and better insight is what makes personalization worth doing.
AI automates tedious tasks so reps can sell.
Automating manual processes is the number one use of AI in sales automation, with data entry, note-taking, list building, and follow-up scheduling all running on rules, workflows, and triggers.
The cost of not automating manual processes becomes evident during the month-end close process:
- 65% of salespeople lose at least a business day a month reconciling data across systems
- 20% of sales leaders name reps spending too much time on admin a top operational blocker
- 37% describe the month-end close as high or very high effort
- 32% say finance and sales aren’t consistently closing from the same revenue figures
The payoff is measurable and showing up across customer-facing teams. According to HubSpot’s 2025 AI Trends for Marketers Report, 79% of marketing professionals say AI and automation help them spend less time on manual tasks. Seventy-three percent say it frees them for the most important parts of their role. Respondents saved an average of one to two hours a day across generative AI use cases.
AI surfaces customer insight reps would likely miss otherwise.
AI analyzes volumes of customer data that no rep has time to read. Call transcripts, email threads, product usage, and engagement history become patterns rather than records, which is what makes intent detection and deal risk scoring possible.
Visibility is the gap it fills:
- 22% of sales leaders cite inconsistent pipeline visibility as a top operational blocker
- 21% point to limited visibility into customer interactions
- 72% say their tools can’t access the complete, accurate revenue data they need to act on
- 75% use AI-powered call recording tools to transcribe and learn from customer conversations
The investment case has largely settled. According to HubSpot’s 2025 State of AI report, 75% of marketing leaders whose organizations invested in AI reported positive returns, against only 4% reporting negative ones. Those leaders named better decision-making from data as one of AI’s largest effects. Separately, 95% of sales leaders using a CRM are confident in their ability to measure its ROI.
HubSpot’s Buyer Intent identifies which prospects are actively showing purchase signals, and Data Enrichment fills in contact and company records automatically so reps aren’t researching from scratch.
AI personalizes outreach, but reps still set the angle.
AI personalizes sales outreach by drafting messages against what’s known about each prospect rather than a segment average. While the value lies partially in speed, more specifically it’s found in the ability to write a personalized email without spending twenty minutes doing it.
The bar has moved on what counts as personalized:
- 21% of sales managers coach reps to personalize on insight rather than surface-level signals, moving past name and company merge tags
- 29% coach reps to generate base copy with AI and then inject their own point of view
- 30% coach on when to use AI versus when to write from scratch
The pattern across all three is the same. AI finds the angle and drafts against it, and the rep decides what’s actually worth saying.
11 Ways Your Team Can Use AI in Sales
Artificial intelligence in sales shows up across the entire cycle, from list building through post-call follow-up. These eleven use cases are where teams see returns fastest.
1. Access to advanced data enrichment, analysis, and applications.
AI-powered data enrichment fills CRM records automatically by pulling contact and company details from email conversations, third-party sources, and public data. Reps walk into calls with accurate job titles, locations, and company context instead of researching each one by hand.
HubSpot’s Data Enrichment researches and completes records inside the CRM, and Smart CRM applies the same logic across the platform so enriched data is available wherever a rep is working.
Best for: BDRs and AEs
What we like: Enrichment that runs inside the CRM means nobody has to remember to trigger it. The records are just correct.
For teams already running on Zoho, DataPrep extracts meaning from existing CRM information to generate new data points like lead sentiment and topics of interest, which then feed scoring and prioritization.
2. Summarize meetings and calls with conversation intelligence.
Conversation intelligence records sales calls, transcribes them, and pulls out the parts that matter: objections raised, competitors named, commitments made, and next steps agreed. The summary lands in the CRM without anyone typing notes.
Adoption here is near-universal. Seventy-five percent of sales leaders use AI-powered call recording tools to transcribe and learn from prospect and customer conversations.
HubSpot’s Conversation Intelligence analyzes recorded calls to surface coaching opportunities, deal risks, and objection patterns across an entire team rather than one rep at a time.
Best for: AEs and sales managers
What we like: The team-level view. Individual call summaries save time, but spotting the same objection across forty calls is what changes a playbook.
For teams that want call analysis to feed customer success as well as sales, Claap combines compliant call recording with AI summaries and deeper analysis. Beyond standard transcription, it tracks deal qualification frameworks, objections, feature requests, deal risks, and coaching scores. Claap syncs directly to HubSpot contact and deal records, so insights land where sales and CS teams already work.
3. Improve pipeline management and forecasting.
Predictive AI analyzes historical and current pipeline data to project revenue attainment and flag deals likely to slip. It replaces the gut-feel commit number with something reproducible.
Forecast accuracy is a live problem. According to HubSpot’s 2026 State of Sales Report, eighteen percent of sales leaders name inaccurate forecasting as a top operational challenge, and 32% say finance and sales are not consistently closing from the same revenue figures.
HubSpot’s Forecasting applies AI-driven pipeline analysis to project attainment and surface at-risk deals before they slip out of the quarter.
Best for: Sales managers
What we like: Flagging at-risk deals early is worth more than the forecast itself. A number tells a manager where they’ll land. A flag tells a rep where to go today.
For teams whose forecast lives outside the CRM, Aviso rolls up projections across every rep and team in real time and scores individual deals with WinScore, which weighs CRM data, multi-channel engagement, conversations, and historical deal patterns to show which commits are solid and which are at risk.
4. Create and repurpose sales content.
Generative AI drafts and reformats sales content through an AI sales assistant: outreach sequences, follow-up emails, one-pagers, proposal sections, and call recaps.The bigger win is usually repurposing, where one strong asset becomes five without starting over each time.
HubSpot’s Breeze Assistant writes, expands, and adjusts the tone of copy across sales materials, working from CRM context rather than a blank prompt box. Some teams also find these prompts offer a few ideas as well.
For teams that need the visual side handled too, Canva’s Magic Studio covers writing and design in one place, and Magic Switch reformats an existing asset into a different format or layout in seconds.
Best for: AEs and BDRs
5. Recommend next steps for sales reps with AI-guided selling.
AI-guided selling recommends the best next action for each deal in a rep’s pipeline, based on where the deal stands, what’s happened recently, and how similar deals have progressed. It replaces the mental tracking reps do across dozens of open opportunities.
HubSpot’s AI Guided Selling surfaces real-time next-best-action recommendations at every deal stage, and Playbooks embeds talk tracks and discovery questions directly in the rep’s workflow so the guidance is specific rather than generic.
Best for: AEs
What we like: The nudge on cooling deals. Most reps know what to do with an active opportunity. The value is in catching the one that went quiet three weeks ago.
For teams that want recommendations tied tightly to interaction history, Creatio Sales suggests when to follow up, schedule a demo, or prepare a quote, adjusting as client intent and engagement change.
6. Monitor your competitors.
AI monitors competitor websites, social channels, news, and pricing pages continuously, then flags what changed and why it matters. What used to be a quarterly manual sweep becomes a daily feed.
Competitor intelligence also arrives through conversation data. HubSpot’s Conversation Intelligence flags competitor mentions across recorded calls, which shows which competitors are actually coming up in deals rather than which ones are making noise.
Best for: BDRs, AEs, and sales managers
Crayon is a specialist here. The tool tracks changes across a competitive landscape, scores them by importance so teams aren’t reading everything, and delivers a daily summary. For sales teams specifically, it builds battlecards from what it finds, which is the part that reaches reps in a live deal.
7. Enhance lead scoring and qualification.
Predictive lead scoring ranks leads by how closely they resemble past closed-won deals and how actively they’re engaging. Reps work the top of the list instead of working the list in the order it arrived.
HubSpot’s predictive lead scoring, part of Smart CRM, scores on both fit and engagement, so a strong ICP match that has gone quiet ranks differently from a weak match opening every email. Buyer Intent adds the signal layer on top, surfacing accounts showing purchase behavior before they fill out a form.
Best for: BDRs and sales managers
What we like: Splitting fit from engagement. A single blended score hides which problem a rep is actually looking at, and those two problems call for completely different plays.
Scoring only works if the underlying data does. HubSpot’s State of Sales report shows that 72% of leaders say their tools can’t access complete, accurate revenue data, and a model trained on gaps produces confident rankings that don’t hold up.
HubSpot's 2026 Sales Trends Report
This in-depth report includes sections, covering:
- How buyers are becoming more self-informed
- How sales teams are using AI and automation
- Adapting to tighter budgets
- And more!
Download Free
All fields are required.
Form not available
8. Streamline and optimize sales engagement.
Sales engagement covers every buyer-seller interaction from first outreach through onboarding. AI streamlines it by automating sequence logic, triggering follow-ups on behavior rather than a calendar, and routing leads to the right rep without a manager in the middle.
HubSpot’s Sales Automation runs AI-triggered workflows for follow-up sequences, task creation, and lead routing, which keeps sequences moving without reps managing the mechanics.
For teams running high-volume outbound as their primary motion, Apollo layers automated sequences with A/B testing, prospect interest scoring, and a rules engine that adjusts campaigns when defined criteria are met.
Best for: BDRs and AEs
9. Personalize pitches and emails.
Generative AI personalizes outreach by drafting against what’s known about a specific prospect rather than a segment average. Done well, it means writing to an individual without spending twenty minutes on each email.
HubSpot’s Breeze Assistant drafts from CRM context, so the personalization draws on the actual record: past conversations, deal stage, company details, and engagement history.
What separates useful personalization from merge-tag filler is the input. Our 2026 data shows that 21% of sales managers now coach reps to personalize on insight rather than surface-level signals, and 29% coach reps to generate base copy with AI and then inject their own point of view.
Having managed scaled prospecting campaigns across more than a dozen business units for close to two years, my read is this: AI is phenomenal at shortcutting research and finding personalization opportunities, and even good at drafting them. Where it falls short is executing without sounding formulaic. It still requires human verification, editing, and oversight. Accurate targeting matters more than ever — people don’t tolerate getting it blatantly wrong.
Gregory Rozdeba, CEO of Dundas Life, uses AI to tailor pitches and proposals in insurance. “By analyzing data on customer preferences and behaviors, AI helps us predict needs, enabling preemptive solution offerings. This not only increases engagement but also positions us as attentive and proactive,” Rozdeba says.
Best for: BDRs and AEs
10. Refine sales scripts.
Generative AI drafts and iterates on sales scripts, discovery questions, and objection responses. The value is in the revision cycle rather than the first draft, since a script only improves once real calls show which parts work.
HubSpot’s Playbooks puts talk tracks and discovery questions inside the rep’s workflow during the call, so a refined script actually reaches the conversation instead of sitting in a doc nobody opens. Pairing it with Conversation Intelligence closes the loop: calls surface which objections keep landing, and the playbook gets updated against what actually happened.
Best for: BDRs, AEs, and sales managers
What we like: Scripts refined from call data beat scripts refined from opinion. The objection a team argues about in a meeting is rarely the one costing them deals.
Marco Genaro Palma, freelance CMO and SEO consultant, starts with a basic script and lets generative AI adjust it based on real-time feedback, keeping pitches sharp as they evolve. He’s clear about the limit: “Despite the advancements in technology, the personal touch in sales is still key, but these AI-enhanced scripts help us keep our approach fresh and effective,” Palma says.
11. Train your employees with AI coaching.
AI coaching gives reps repetition without requiring a manager in the room. Roleplay simulations, call scorecards, and automated feedback let new hires practice before they’re on live calls, and give tenured reps a read on patterns they can’t hear in their own conversations.
According to HubSpot’s 2026 State of Sales Report, 51% of sales leaders say AI coaching helps new teammates learn faster, though 12% worry reps lean on it as a crutch. When asked what drives the highest prospecting volume from AI, 16% of sales leaders named coaching reps as their biggest lever.
HubSpot’s Conversation Intelligence surfaces coaching opportunities from recorded calls across a whole team, which is what makes coaching scalable rather than anecdotal.
Best for: Sales managers
What we like: Coaching from call data means the feedback is specific. “Be more consultative” is not coaching. “You answered the pricing objection before they finished asking it, in four of five calls” is.
More apps offering AI sales coaching opportunities:
- For teams that want reps practicing before they’re live, Quantified builds AI avatars for simulated calls, trained on a company’s own messaging and product, and scores performance against a custom rubric.
- Outdoo, formerly MeetRecord, runs roleplay against scenario bots built from real pipeline conversations.
- Gong takes the analysis route instead, reading live calls to deliver rep-level strengths and weaknesses for managers to coach against.
AI in Sales Tools Comparison Table
The tools below solve different problems, so the useful comparison is not which one wins but which category a team needs first. Most sales teams start with what’s built into their CRM and add specialists as specific gaps show up.
Pricing note: HubSpot’s Sales Hub has a free tier, with paid plans priced per seat. Most specialist tools on this list price by custom quote rather than published rates. Current pricing for any tool should be checked against its own pricing page before a purchase decision.
Frequently Asked Questions About AI in Sales
Will AI replace salespeople?
AI will not replace salespeople, but it is changing what the job consists of. Sales leaders still expect reps to own the decisions that require reading a situation — which accounts to prioritize, what angle to take, and when to disqualify a prospect. Only 4% of leaders say AI is involved in all of those calls. Sixty-two percent are neutral on AI’s impact on team headcount, and only 7% expect a negative impact. Most describe AI as an enhancement to the sales process rather than a reason to hire fewer people.
How is AI used in sales?
Sales teams use AI to automate research and data entry, score and prioritize leads, summarize calls, draft outreach, detect buying intent, and forecast revenue. The common thread is that AI handles the work surrounding a conversation while the conversation itself stays with the rep. Adoption is heaviest where the task is repetitive and the data already exists — 75% of sales leaders use AI-powered call recording to transcribe and learn from customer conversations.
What’s the best AI tool for sales?
The best AI tool for sales is usually the one already connected to the team’s CRM. AI is only as good as the data it can reach, and 72% of leaders say their tools can’t access the complete, accurate revenue data they need to act on. Eighty-eight percent of revenue and sales leaders agree their sales process would benefit from a single integrated platform. Most teams are better served activating the AI in the system they already run on before adding another tool to the stack.
Is sales a safe career from AI?
Sales is changing rather than disappearing, and reps who learn to work with AI have the advantage. The skills leaders still expect from people — account judgment, qualification, negotiation, and relationship building — are the ones AI has not taken over. One-third of managers coach reps on prompting AI for research rather than writing, 30% coach on when to use AI versus writing from scratch, and 22% name knowing when not to use AI as the biggest gap on their teams. The reps who close that gap are the ones who stay valuable.
How to Get Started with AI in Sales
Getting started with AI in sales works best as a narrow first move rather than a platform overhaul. The teams that struggle are usually the ones that bought a category before identifying a problem. Twenty-six percent of sales leaders name difficulty adopting new tools and processes as their top operational challenge, and that difficulty is mostly self-inflicted.
The gap is a process and operational issue: individual AI use doesn’t automatically translate into workflow integration. A recent AI GTM report found that 86% of GTM survey respondents use AI daily or on almost every task, but only 33% have AI automations completing GTM tasks for them. For sales teams, the opportunity is to turn individual productivity gains into repeatable workflows, with clear ownership and measurable outcomes.
Step 1: Audit the repetitive work.
Track where time actually goes for two weeks before evaluating a single tool. Manual data entry, prospect research, note-taking, and follow-up emails are the usual culprits, and they’re the tasks AI handles well because they follow rules. The audit matters because it produces a specific problem to solve rather than a general sense that the team should be using AI.
Step 2: Start with the AI already in the stack.
Most teams own more AI than they use. HubSpot’s 2026 State of Sales report shows that 80% of sales leaders say their CRM is already fully AI integrated, which means the fastest path to adoption usually involves turning something on rather than buying something new. Starting inside the existing system also avoids the data fragmentation that undermines AI output, since 72% of leaders say their tools can’t reach the complete revenue data they need.
Step 3: Pilot one use case.
Pick a single use case, run it for a quarter, and measure it against what the work cost before. AI-drafted follow-up emails and predictive lead scoring both work well as first pilots because the baseline is easy to establish and the results show up quickly. Scaling to a second use case only makes sense once the first one has produced something worth repeating.
Pro tip: When in doubt, ask the AI you’ve been using to help identify and evaluate a potential use case, or to help break a big option down into smaller parts to roll out and test independently. With some irony, I’ve found AI to be one of the most helpful tools in determining which processes to move to AI.
Step 4: Train against the workflow, not the tool.
Adoption follows training that reflects how reps actually work. Short sessions tied to a specific outcome outperform generic tool walkthroughs, and 35% of sales professionals say hands-on personalization training and support would help them use AI more. Coaching on judgment matters as much as coaching on mechanics: 22% of managers name knowing when not to use AI as the biggest gap on their teams.
Pro tip: In my experience, getting sales teams on board with new ways of doing things is often the most important thing. While they’re always interested in better, faster, easier ways of doing things, the process shift is most important. A blend of live trainings, self-service support wikis, and opportunities to get answers makes their lives easier. The other side of the coin is understanding the new KPIs and benchmarks so they can be sure to meet them.
Getting Started with AI in Sales
The practical starting point is the AI already in the CRM. Data enrichment, predictive lead scoring, call summaries, guided selling, and forecasting all run against the same customer record — activating what already exists removes the integration work that slows most rollouts. Sales Hub’s free tools are a reasonable place to test the first use case before committing to a paid plan.
Editor's note: This post was originally published in February 2023 and has been updated for comprehensiveness.
HubSpot's 2026 Sales Trends Report
This in-depth report includes sections, covering:
- How buyers are becoming more self-informed
- How sales teams are using AI and automation
- Adapting to tighter budgets
- And more!
Download Free
All fields are required.
Form not available
![Best AI transcription software for your workflow [2026]](https://blog.hubspot.com/hubfs/blogbot/best-ai-transcription-software-featured-2634187.webp)
