Enhance Sales Performance with AI Lead Scoring

Few things are more frustrating for sales teams than following up on leads that ultimately don’t convert into sales. Cold-calling and investigating unqualified leads is a major time burden for many businesses. It can also negatively impact morale; after hitting hundreds of dead ends, team members may struggle to stay motivated.

But there’s a solution. Lead scoring ranks prospects based on their likelihood to convert, freeing up time for sales teams to focus their efforts on the leads that matter most.

And, with artificial intelligence (AI) now fully embedded into business infrastructure, lead scoring is set to become even more powerful. Research from our State of Sales Report indicates that 98% of sales leaders believe AI will improve the prioritisation of leads, resulting in increased time efficiency and revenue.

In this article, we’ll take a close look at what AI lead scoring is and how it works. We’ll also discuss the benefits, provide a comprehensive guide on how you can implement it for your business, and explore some of the best automated platforms that make AI lead scoring even easier.

Let’s dive in.

AI lead scoring is the process of using machine learning (ML) algorithms to determine the likelihood of a sales lead converting into a paying customer. It essentially creates customer profiling for each lead and populates it with metrics that will determine whether a lead should be considered hot or cold.

It will then assign a score for each lead based on these metrics, with higher scores indicating a higher likelihood of conversion.

Several industries have already adopted this form of automated lead scoring, with 83% of early AI adopters reporting moderate to significant financial benefits.

Effective lead scoring pinpoints which leads are most likely to convert, identifying where sales teams should focus their efforts.

Let’s take a look at the four key steps involved in this process.

All AI lead scoring begins with gathering data on everything related to the lead.

First, AI software will gather and unify data across several key data sources, including individual demographics (both professional and personal), behavioural signals such as website visits and email opening rates, customer relationship management (CRM) history and third-party data from sites such as LinkedIn.

Machine learning lead scoring software will then process and interpret the data collected. This process essentially cleans and unifies data so it’s ready for use; it also discards inconsistent data in the process.

This clean data will then be used to train the AI models so that they’re able to recognise the signals that denote a ‘hot lead’ (think frequent website visits and opened emails), and those that might indicate a cold one (like ignored sales calls, a lead outside a target demographic).

Once the AI model has been trained with enough data, it will eventually develop a series of criteria or smart contracts that, once satisfied, will help it arrive at the right lead score.

Once the lead has been analysed, AI will generate a lead score that can be used to determine the likelihood of success when a sales team member tries to convert the sale. The score is generally out of 100; higher scores suggest a stronger lead.

This gives sales teams the tools to prioritise and fast-track leads that are most likely to convert, freeing up time while improving deal success rates. It also allows the business to implement strategies for low-scoring leads, such as long-term drip campaigns to strengthen scores over time.

As with all AI programs, the more clean data the AI lead scoring solution receives, the more accurate it becomes. As more leads are won or lost, it will be able to identify patterns based on what successful leads have in common, driving better results over time.

It’s also worth remembering that customer behaviour patterns change over time, which is why it’s important to continuously modify the algorithm and lead scoring criteria to account for these changes.

Lead scoring has been involved in sales operations for a number of years now. The idea that some leads are better than others isn’t a new concept.

However, the scope of traditional lead scoring is often quite limited, and manual judgments are slow and prone to error. There’s also a higher potential for bias creeping in, with sales teams looking to score based on their preferences rather than objective, data-driven patterns.

In contrast, AI lead scoring is dynamic and adaptive, able to adjust based on data rather than gut instinct. It’s also ideally suited to scaling, with AI machines able to automatically calculate an accurate lead score in seconds, paving the way for far more leads (and far more potential revenue sources) to be examined at speed.

While traditional lead scoring has benefited businesses over the years, AI lead scoring is the necessary step to take a business’s sales operations to the next level.

Businesses across all industries are rapidly adopting an AI lead scoring approach. When implemented correctly, it has delivered five key benefits.

Lead scores tend to be far more accurate if they’ve been generated by an AI lead scoring system rather than through manual evaluation. This is because AI uses in-depth pattern recognition to determine the criteria for high-potential leads.

If scores are more accurate, then businesses are better able to prioritise for success.

When evaluating our own Einstein AI lead scoring platform, we’ve found that our customers have experienced a 32% increase in converting prospects to buyers. This is largely a result of its ability to use past customer interactions to create meaningful insights.

By targeting the right leads at the right time, and in the right order, businesses can see a significant uptick in their revenue potential.

With AI data scoring, all lead scores are devised through data analytics rather than gut instincts. This makes it far more likely that the leads designated as hot will have a positive outcome.

Often, misaligned lead scoring processes and sales and marketing objectives can cause problems for businesses. A marketing team might keep sending across unsuitable leads, ultimately frustrating the sales team. Conversely, a sales team might go against the opinions of the marketing team and not follow up on leads. Either way, it can make accountability much harder to achieve.

AI lead scoring can get everyone on the same page and ensure both teams are working with a clear, data-driven definition of hot lead qualification underpinning their decision-making.

When relying on manual processes, sales and marketing teams may be a little slow to recognise changes in customer behaviours or other criteria that previously defined what a hot lead looked like.

AI lead scoring automation will adapt its algorithms and decision-making based on the most recent successful conversions. As long as data feeding remains consistent, it will always be able to quickly pinpoint the most promising leads in spite of behavioural or firmographic changes.

As with most things, it’s important to strike a balance. AI lead scoring can deliver significant benefits to a business, but only if it’s implemented in the right way.

There are also a few notable challenges that businesses will need to be aware of when adopting this technology.

If a business feeds its algorithms with outdated or inconsistent data, inconsistent lead scoring outcomes are almost inevitable. Seventy-six per cent of businesses believe that poor data quality and analytics negatively impact revenue opportunities.

AI lead scoring relies on data from successful and unsuccessful conversion attempts in order to uncover what works and what doesn’t. New businesses or companies with a lack of historical data might not have enough datasets to train a model or algorithm sufficiently.

Available datasets for what was considered a good lead (when engaging with traditional lead scoring) may be affected by bias. Certain behaviours or demographics could have been overlooked.

This will result in the models not picking up on these key insights as well.

AI lead scoring is great, but it isn’t perfect. It still can’t pick up on every nuance or quirk of human behaviour. If sales teams become too fixated on what the AI is telling them, they could miss out on certain opportunities that may present themselves in other ways.

Now that you understand what AI lead scoring is and why it works, we’ll share our suggestions for some of the best providers currently available in 2025.

Let’s take a look at each of these in a little more detail.

Salesforce has been delivering groundbreaking AI software and services for some time now. And with Einstein, users are able to tap into the vast array of features that are indispensable for most modern business operations.

With Salesforce, you get much more than lead scoring. It’s just one aspect of a network of interconnected applications that help to build a truly dynamic AI infrastructure, with Sales Cloud and CRM Analytics enabling a robust workflow process from marketing through sales. Strong predictive lead scoring models, informed by historical CRM data that can be seamlessly integrated into the Salesforce platform, are used to identify high-potential leads consistently.

While it can take new users a little time to get up to speed with the complexity of the platform, it’s well worth the effort to learn.

HubSpot is another big player in the world of AI-driven lead scoring software. What sets it apart from some of the other providers out there is its relatively low barrier to entry in terms of learning the ropes. This makes it a good option for smaller businesses that may not have access to highly technical teams.

It can also be seamlessly integrated into existing HubSpot infrastructure, minimising disruption.

It’s lacking a bit in terms of customisation and the potential to identify leads based on rich behaviour or intent data. And while it does offer a free service, many of the advanced features needed for powerful data insights and lead scoring are only available on the higher-tier packages.

Zapier is a tool that gives you the ability to integrate many third-party apps, which makes it easier to customise your AI lead scoring (as well as the broader sales pipeline) setup. The customisation capabilities are what distinguish it from some of its competitors.

However, it can be a somewhat double-edged sword. While the customisation can suit certain businesses, managing it can also require significant maintenance, which can be tricky for businesses with a limited tech team. The limited number of native features is also an obvious drawback; third-party tools are required for advanced scoring.

As you might expect, there are dozens of potential solutions on the market if you’re looking to incorporate AI lead scoring. Most will generally offer the same features; however, there are a couple of additional products worth noting.

Demandbase is specifically focused on account-based marketing (ABM) . This approach will result in far fewer leads, but each lead is given a high level of nurturing and personalisation. It has strong global coverage, with many sources of buyer behaviour signal metrics to analyse. However, this approach makes it unsuitable for other forms of lead generation.

MadKudu is excellent for its behavioural analytics and lead grading, and it’s ideal for SaaS companies. It comes with many great features geared towards AI lead scoring, but they come with a high price tag and require a high level of technical expertise to set up.

Finally, there’s 6sense . This is another powerful AI platform best suited for an ABM approach, with real-time scoring and API functionality to build close links between sales and marketing teams. However, they don’t list their pricing publicly. Based on what’s available, it’s likely to be considerably more than what many SMEs can manage.

Lead scoring has long been a vital sales strategy. It’s a necessary process that saves businesses both time and money. However, as business and customer relationships become more complex and nuanced than ever, the traditional method of developing custom lead scores is no longer cutting it.

That’s why AI lead scoring is something all businesses should look to adopt in some capacity. Not only will it vastly increase the accuracy and output potential of lead investigation, it will also help to build a resilient and motivated workforce whose attempts to land the sale will end with success much more often than failure.

At Salesforce, we’re committed to helping our customers incorporate AI lead scoring and improve their sales processes. Our platform and products are uniquely designed to help businesses manage complex workflows, and our Sales Cloud application is built to enable advanced lead scoring, data-driven insights and smart prioritisation.

Contact us today to learn how we can help you achieve your AI lead scoring objectives .