AI for Sales: 9 Ways to Transform Your Sales Process

Artificial intelligence in sales and marketing is driving measurable results for the teams that use it well. Reps who rely on AI tools are closing more deals, spending less time on admin, and giving managers real-time visibility into what's actually happening on calls.

Automation frees reps from time-consuming admin work so they can focus on the conversations that move deals forward. And when those conversations go well, customers often stick around longer and spend more, which shows up directly in the bottom line.

I've seen firsthand how artificial intelligence in marketing and sales changes what a rep's day looks like, and we're still only scratching the surface of what it can do.

Types of AI used in sales

Not all AI does the same job in a sales context. Different types of AI serve different functions across the sales process, from predicting which deals will close to drafting the follow-up email after the call. Understanding the difference helps you evaluate tools on what they actually do, rather than on the word "AI" alone.

Gartner predicts that 70% of customer experiences will involve some machine learning within the next three years, and sales is one of the functions where that shows up first.

Machine learning

Machine learning identifies patterns in historical sales data to improve predictions over time. It's the technology behind lead scoring models that get more accurate the more data they see, and forecasting tools that learn which signals actually correlate with a closed deal.

Predictive analytics

Predictive analytics uses that historical data to train models that surface the next best action, flag deal risk, and project revenue. A sales tool with strong predictive analytics can tell a manager which deals in the pipeline are most likely to slip before the forecast call, not after.

Generative AI

Generative AI, built on large language models, drafts emails, summarizes calls, and personalizes outreach at scale. It's best suited to summarization and prompt-based tasks. It won't close a deal on its own, but it removes a lot of the writing and note-taking that used to eat into selling time.

The business case for AI in sales

The return on AI adoption in sales is measurable, and increasingly hard to ignore. According to Salesforce's 2026 State of Sales report, 87% of sales organizations now use some form of AI for tasks like prospecting, forecasting, lead scoring, or drafting emails. Among sellers already using it, 89% say AI deepens customer understanding, and 87% say it makes their job less stressful.

That same 2026 State of Sales report found that top-performing sellers are 1.7 times more likely to use AI agents for prospecting than underperformers. That gap tends to widen, not close, as adoption matures, since teams that start capturing conversation data earlier have more of it to work with.

AI in the workplace can do everything from predicting which prospects are most likely to close, to forecasting, to recommending the next best action, all of which removes a lot of guesswork from decisions that used to rely on gut feel.

It's worth being realistic about the limits too. AI can't yet handle complex problem-solving or human relationship-building on its own, so it works best combined with a rep's judgment rather than in place of it.

One Dialpad customer, ConstructConnect, saw new-hire ramp time drop by nearly 40%, from four to five months down to about three, after rolling out Dialpad's real-time coaching tools, alongside a 28% year-over-year increase in inbound lead conversion rates.

AI use cases in sales: 9 ways to transform your sales process

One of the most useful things about AI is its ability to speed up repetitive processes like data entry, which gives sales reps more time for human-focused tasks and closing deals. Here are nine of the most common ways sales teams put AI to work.

1. Sentiment analysis

When a sales manager has a team of reps, and multiple active sales calls, to oversee, staying on top of every conversation in real time isn't realistic without help.

That's where an AI sales tool with a sentiment analysis feature comes in. It can transcribe calls in real time and track the sentiment of the conversation as it happens. (Learn more about sales call planning.)

If a prospect says things like "I'm confused" or "I'm frustrated," an AI sales tool with sentiment analysis can pick up on that and mark the call sentiment as negative, and vice versa if the prospect is saying positive things like "I love that!" Dialpad AI applies this directly to calls and chats, giving managers a live read on how a conversation is going.

Armed with this insight, a sales leader can keep an eye on tens or even hundreds of active calls and quickly see which ones have negative sentiment. If they spot one, they can open the real-time transcript, scan it for context, and decide whether to jump in to help save the deal.

2. Coaching at scale

A good AI sales tool does more than transcribe calls in real time. It can pick up on specific keywords and phrases and track how often they come up across a whole team's calls. For example, a rep interested in tracking mentions of a specific competitor can set up a custom tracker for that term.

In Dialpad, this is built through Custom Moments. Set one up for a competitor's name, and Dialpad AI tracks it automatically, giving you analytics you can use to dig into specific calls and see exactly what prospects are saying.

From there, you can create AI Live Coach Cards for your sales reps, set to trigger automatically when a certain keyword is spoken. Taking the competitor example above, an AI Live Coach Card with notes on how to position against that competitor can pop up on a rep's screen the moment the competitor's name comes up on a call.

Dialpad's AI Scorecards take this further by automatically reviewing sales calls for whether reps covered everything on the scorecard criteria.

It's straightforward: sales managers and admins can create a QA scorecard from their online dashboard, and as reps talk with prospects, Dialpad AI listens to each interaction and automatically flags when the scorecard behavior is met.

Say a manager has added "Rep asked the prospect who the decision-makers are" to the scorecard. If the rep asks about decision-makers, whoever grades that call sees the activity marked complete immediately.

If not, that's a coaching opportunity. Whether a team is full of new hires or seasoned reps, this kind of AI-driven coaching helps make sure everyone has what they need to move deals forward, without a manager personally reviewing every call.

Dialpad also integrates with CRMs like Salesforce to automatically log sales activities and minimize manual data entry, embedding Dialpad AI features directly inside Salesforce so reps get real-time transcripts and AI Live Coach Cards without leaving the CRM.

3. Predictive forecasting

Forecasting is tough. Human sales leaders are typically good at predicting sales numbers and setting goals, but AI can help do it with additional precision. Advanced analytics, gathered automatically, can surface the bigger picture before a forecast gets finalized.

From this, managers can make more informed decisions. Tracking the busiest times during a quarter for inbound calls, for example, can help with plans for coverage. Dialpad's dashboard gives a clear overview of how things are trending.

4. Automated sales activities

There are several areas of sales where an AI assistant speeds things up. Sales dialers, including predictive or power dialers, help reps make more outbound calls at scale, and automations can pull in activity or call data without reps having to lift a finger.

AI can also handle post-call reporting, one of those essential but tedious tasks. Dialpad automates call notes and highlights key action items so reps don't have to type everything manually.

With Dialpad's AI Playbooks feature, Dialpad AI can suggest behaviors and questions for reps to run through on a call, then automatically check them off in real time as they're completed, whether the team follows SPICED or BANT sales methodology.

5. Price optimization

Finding the right price for each customer can be tricky, but AI can simplify it. It uses algorithms to review the details of past deals, then works out an optimal price for each proposal and communicates that to the salesperson. Dynamic pricing tools use machine learning to gather competitor data and can offer recommendations based on that information and an individual customer's preferences.

6. Sales attribution

Tracking and measuring attribution matters, since it tells you where to target future effort, and AI helps use large volumes of data to attribute results more accurately. From there, you can see which campaigns and customers are most effective at driving ROI.

Reps usually log activities in a CRM, which is especially easy with Dialpad's Salesforce integration, embedding a CTI dialer right inside the CRM:

But plenty of sales activity happens outside the CRM, which means it wouldn't show up in CRM data unless reps remember to log it. This is where AI can help, automatically logging a rep's activities and matching them to the right opportunity.

7. Sales enablement

AI is well suited to sales enablement, giving teams extra resources to help close deals and sell more. That includes analyzing data to identify and surface successful case studies, testimonials, and customer reviews, which reps can use to build trust and credibility with prospects.

Lead generation

There will always be a human element to generating leads, but AI makes the task easier by helping reps reach targeted prospects faster. Sales prospecting tools powered by AI can suggest responses during interactions, using data to personalize and steer the conversation, which helps teams engage more effectively and close deals faster.

Qualifying leads, writing email follow-ups, and sustaining relationships are also time-consuming, but AI cuts down some of that legwork with automation and next-best-action suggestions.

Prioritization and lead scoring

Figuring out which leads to call first eats into sales productivity. Machine learning helps spot patterns to determine which leads are most likely to convert, supporting more logical decision-making.

The algorithms score leads and estimate their chances of closing by analyzing customer profiles and previous interactions like email and social media activity.

Upselling and cross-selling

Rather than trying to upsell or cross-sell every client, AI can help identify who's most likely to be receptive by looking at previous interactions and profiles.

Dialpad AI also helps reps read the sentiment of a call, so they can pick the right moment to offer a complementary product. It's a good example of humans and AI working together rather than AI making the call alone.

Expert insight and recommendation

Using AI is a bit like having an in-house expert on hand for tips and direction. It can evaluate customer relationships, flag ones that need attention, and help identify needs and potential solutions ahead of a call.

Based on data and stated goals, AI can surface which actions make the most sense and advise the sales team accordingly.

8. Performance management and enhancement

Even the best reps need ongoing training, if only to keep up with new tools and techniques. AI gives managers a way to monitor performance in real time, beyond just checking who's making the most calls or closing the most deals.

Interactions can be recorded for training purposes, and sentiment analysis helps confirm reps are saying the right things to customers. Dialpad's AI Live Coach features bring real-time coaching and recommendations directly into the call, helping new reps ramp faster and supporting ongoing coaching for the whole team.

Live sentiment analysis shows how calls are going at a glance, and managers can choose to listen in and join if needed. Built-in speech coaching lets reps know if they're speaking too fast or not listening closely enough. And AI Live Coach Cards, mentioned earlier, do the same job in the moment.

Meanwhile, Dialpad analytics offers a wide range of stats, from call activity trends over time to a rep leaderboard with specific call metrics.

9. Playbooks built for your sales team

Coaches and supervisors need their reps following whatever sales methodology the team uses consistently, whether that's BANT, SPIN, or SPICED.

With AI, you can build playbooks for each of these frameworks and let the AI check automatically whether recommended behaviors and questions are being followed, then alert managers to any missed steps to support coaching.

The result: better adherence over time, which tends to translate into stronger pipeline and revenue.

Can AI replace a salesperson?

No, at least not for the parts of the job that matter most in complex sales. AI can't replicate the judgment required to navigate a buying committee, build trust under pressure, or read the unspoken signals in a room. Recent industry commentary backs this up. A July 2026 Forbes Business Development Council piece put it plainly: AI can inform a sales conversation, model outcomes, and guide next steps, but building the kind of trust where buyers are honest about their priorities, risks, and goals still comes down to people.

Where an AI salesperson does perform well is at scale: qualifying leads, running follow-up sequences, drafting personalized outreach, and flagging deal risk before it's visible to the human rep. Think of it as a force multiplier rather than a replacement, one that handles the mechanical parts of the job so reps can spend more time on the parts only a person can do.

That's the model Dialpad is built around. Real-time coaching, automatic transcripts, and AI Playbooks make human sellers more effective instead of trying to remove them from the process.

AI for sales management

AI shifts sales management from reviewing calls after the fact to seeing pipeline health, rep performance, and deal risk in real time, across the entire team, without manual review. That shift changes what a manager's day actually looks like.

• Pipeline visibility: AI can surface deal risk signals before they become lost deals, flagging stalled opportunities and identifying deals at risk by stage or rep.

• Team performance monitoring: AI Scorecards and the analytics dashboard let managers oversee dozens of reps simultaneously, with metrics like call volume trends, rep leaderboards, and sentiment by rep all in one place.

• Forecasting accuracy: Predictive analytics surfaces deal risk and revenue projections from historical pipeline data, giving managers a clearer read on what's likely to close before the forecast call, not after it.

• Coaching at scale: AI Live Coach Cards and AI Playbooks let managers standardize best practices across the team without one-on-one coaching for every single call.

How to use AI in sales: 4 strategies for creating a strong sales AI strategy

If you want to use artificial intelligence in sales, you can get started with a few simple steps. Regardless of which AI sales tool you're evaluating, the strategy has to start with a clear picture of the outcome you're trying to drive.

Set realistic expectations

As with any business goal, sales objectives should be clear, attainable, and measurable. Everyone should know what's expected and why it matters.

Don't expect results overnight. Be realistic about targets while reps are still getting comfortable with the technology, and make sure they know it's fine to ask questions or request extra help.

Customize AI to your business context

There's no point reaching for a cool-sounding AI solution if it isn't suited to your business needs. With more AI productivity tools on the market than ever, it's worth evaluating carefully.

Align your AI strategy and tools with your overall sales goals, whether that's growing pipeline, improving win rates, or a specific target like shortening rep ramp time.

Target quick wins, especially in the beginning

If you want to see the difference AI makes, focus on a project that shows results within six to 12 months. It proves the value of AI to leadership and helps motivate the team.

Look for reps who are already good at using AI for customer service and customer success, and use their calls or interactions as a learning resource for the rest of the team.

Partner with the right experts

To make AI work, choose the best sales enablement tools from the right providers. Look for a sales platform that combines a communications suite with deep CRM integration and sentiment analysis.

You'll also need to know how to make these tools work for your team, and to evaluate the benefits AI brings to the business. It can help to bring in an AI expert who can support the launch and analyze results early on.

AI sales readiness checklist

Ready to increase sales with AI?

Whether it's B2C or B2B sales, face-to-face meetings or inside sales, the landscape is changing thanks to the growing use of artificial intelligence in sales.

With repetitive tasks automated and sales cycles shortened, the future of sales will focus on minimizing time spent on activities that could be automated, and optimizing the steps that require a human touch, such as deciding how to handle objections in sales calls or making judgment calls about whether to push a reluctant lead forward.

The teams pulling ahead are the ones putting AI to work across the entire sales process: coaching reps in real time, surfacing deal risk before it costs a deal, and cutting the manual admin that eats into selling time. That's exactly what Dialpad Sell is built for, with real-time coaching, sentiment analysis, and AI Playbooks built natively into the same platform reps already use to make calls.