AI in B2B: how companies are using it to scale smarter

Everywhere you look today, we bet all you're hearing about is artificial intelligence (AI). It’s the technology that seems to power just about every industry around the world.

In the business-to-consumer (B2C) companies, it helps personalize shopping experiences, recommend products, detect fraud, and drive chatbots that can answer customer questions instantly—to name a few.

In the business-to-business (B2B) world, it’s less about front-end features and more about improving how work gets done. AI is being used to automate customer support workflows, generate sales summaries, flag billing issues, detect churn risks, score leads, optimize supply chains, and pull insights from huge amounts of unstructured internal data.

With AI doing so many amazing things, it probably comes as no surprise that the global AI market is projected to reach 244 billion U.S. dollars by the end of this year and grow beyond 800 billion by 2030. Clearly, AI is no longer optional.

If you're a B2B company, read on to learn how you can use AI across your operations to maximize benefits and propel your business.

To start, let’s quickly go over the key definitions to ensure we're on the same page.

What is AI?

Artificial intelligence (AI) is a branch of computer science focused on building systems that can simulate human intelligence. These systems are capable of learning from data, identifying patterns, making predictions, and improving performance over time without being explicitly programmed for every scenario. 

AI includes a range of subfields, such as:

• Machine learning

• Natural language processing (NLP)

• Computer vision

In practical terms, it allows software to automate decision-making, analyze complex inputs, and interact with users or data in more adaptive ways.

What is AI in B2B?

Artificial intelligence in B2B companies refers to the use of AI technologies to streamline internal operations, improve customer-facing workflows, and enable smarter decision-making across departments.

Unlike B2C applications, which often focus on personalization and front-end user experiences, AI in B2B is typically used behind the scenes to make complex processes more efficient and scalable. And, of course, to drive cost savings.

In a B2B context, AI powers a wide range of tasks, including:

• Classifying support tickets 

• Forecasting sales pipelines

• Detecting customer churn

• Optimizing pricing models

• Automating internal reporting

These B2B AI systems are often integrated into existing tools like CRMs, help desks, communication platforms, and analytics dashboards. The complexity and high-value nature of B2B transactions make AI particularly valuable, especially when dealing with large datasets, multi-step workflows, and long customer lifecycles.

What can AI automate in B2B?

One of the biggest advantages of artificial intelligence in a B2B setting is its ability to automate repetitive, high-volume, and decision-based tasks across different departments. 

For many companies, the first instinct is to associate AI with flashy, customer-facing features, but its real strength in B2B often lies behind the scenes, quietly powering workflows, surfacing insights, and reducing manual effort.

1. Customer support workflow automation

One of the biggest gains AI delivers is in customer support operations. It plays a critical role in transforming support for B2B SaaS companies, where interactions often happen across real-time messaging platforms like Slack, as well as more traditional tools like Freshdesk and Intercom. The high volume of inbound requests, combined with the expectation of fast, contextual responses, makes support a natural fit for automation.

AI can automatically triage incoming tickets, categorize issues based on urgency or topic, and route them to the appropriate team members. Natural language processing models can summarize long message threads, helping support reps get context instantly. AI can also detect patterns in user queries, identify repeat questions, and even suggest automated or human-reviewed replies.

For example, our tool, Thena, integrates directly into Slack and support platforms to streamline this entire process, reducing response times, improving SLA tracking, and giving support teams a more scalable way to manage conversations across multiple channels.

2. Lead scoring and sales enablement

AI can be equally super useful in B2B sales. It can automate the tedious but critical task of qualifying leads. Traditional lead scoring systems often rely on fixed rules, which can quickly become outdated. AI-driven models, on the other hand, can continuously analyze a wide range of signals, such as website behavior, email engagement, and CRM activity, to score leads based on likelihood to convert.

This enables sales teams to focus on the highest-priority accounts while automating the follow-up process for lower-intent leads. McKinsey and Company define a wide range of gen AI use cases. For example, it can surface next-best actions, recommend when to reach out, and even generate personalized outreach messages using generative language models trained on past successful communication patterns.

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3. Finance and billing workflows

B2B companies often deal with complex billing cycles, invoicing systems, and procurement workflows. AI can streamline these operations by automating tasks such as invoice matching, error detection, contract analysis, and payment reconciliation.

For instance, machine learning models can flag anomalies in billing data, identify duplicate charges, or alert finance teams to potential compliance risks. Optical character recognition (OCR) combined with NLP can extract structured data from unstructured documents, such as scanned contracts or receipts.

4. Internal knowledge management

Many B2B teams, especially in SaaS, operate with large, fast-changing bodies of internal documentation, ranging from product specs to support playbooks to onboarding guides. AI can help by automatically organizing, tagging, and surfacing relevant knowledge at the moment of need.

Instead of forcing employees to search through folders or wikis, AI systems can use semantic search and context detection to suggest relevant articles or snippets directly within their workflow. This is particularly useful in support, success, and onboarding teams, where the right answer often already exists.

5. Forecasting and operational planning

AI is also being used to automate and improve forecasting across multiple areas of B2B operations. Machine learning models can ingest vast datasets and uncover patterns that human analysts might miss, improving the accuracy of forecasts and freeing up operations teams to focus on decision-making rather than data wrangling.

According to the Boston Consulting Group, AI can drive significant improvements across key business functions. Companies using AI have reported 30% faster planning cycles, 20% to 40% more accurate forecasts, and a 20% to 30% boost in overall finance team productivity.

What Can AI Do in B2B SaaS?

If we were to look at a more granular level, AI is especially powerful in B2B SaaS companies, where teams are constantly looking for ways to scale faster without compromising service or internal efficiency.

With high customer expectations, growing product complexity, and multiple internal systems in play, AI offers practical solutions to some of the most common challenges. Here are some of the use cases:

• Summarizing and routing support tickets: AI can digest long message threads and generate quick summaries, helping agents understand issues without reading every detail. It can also route tickets to the right person based on keywords, customer data, urgency, or sentiment. Tools like Thena take this even further by unifying Slack, email, and chat into one seamless flow. Instead of jumping between platforms, support teams can auto-triage, route, and resolve tickets directly in the channel where customers first raise the issue.

• Scoring leads and optimizing sales pipelines: AI models analyze CRM activity, engagement signals, and deal history to assign dynamic lead scores. Sales reps can focus on high-priority accounts, automate follow-ups, and reduce time spent on unqualified leads.

• Tracking product usage and driving adoption: AI helps product teams spot usage trends, identify friction points, and trigger nudges to improve feature adoption. This data can also be used by customer success teams to drive retention and upsell strategies.

• Predicting churn and monitoring customer health: AI combines usage data, support interactions, and billing trends to flag accounts at risk of churn. This allows customer success teams to act early and prioritize proactive outreach.

• Automating content and communication: Generative AI tools can write help articles, onboarding messages, or personalized emails based on customer data. This speeds up content creation while keeping messaging relevant and consistent.

What Are the B2B AI Tools and Companies?

As we’ve discussed, the B2B AI landscape is growing fast. That means there are new tools emerging to solve real problems across support, sales, marketing, and operations. Many of these tools are built specifically for the workflows B2B teams rely on every day, making AI easier to adopt and integrate into existing systems.

Take a look at the table below for a quick roundup of some of the leading AI tools and companies in the B2B space today:

What can you custom-develop with AI in B2B?

While off-the-shelf AI tools can deliver significant value, many B2B companies eventually run into limitations. 

Every business has unique workflows, data sources, and customer dynamics that generic tools may not fully support. And that’s when you might want to look into custom AI development, which can offer tailored solutions that match the specific needs of your product, team, or industry.

Here are some of the most common areas where companies are building custom AI capabilities in a B2B setting:

• Custom support classifiers: Instead of relying on prebuilt ticket categorizations, you can train AI models on their own historical support data. That would massively improve the accuracy of ticket tagging, urgency scoring, and routing decisions across your support platforms.

• Proprietary lead scoring models: Rather than using standard scoring logic, your sales and RevOps teams can build custom models that reflect their unique deal cycles, industries, and signals. The main benefit of these models is that you can better prioritize leads and improve sales efficiency.

• Internal knowledge assistants: If your business has large internal wikis or documentation libraries, building AI-powered assistants that can search, summarize, and suggest relevant info to employees across different departments is more efficient than buying an off-the-shelf solution.

• Custom forecasting engines: Finance and operations teams may use AI to create forecasting models that go beyond standard SaaS metrics. These models can factor in seasonal patterns, contract terms, product adoption rates, and external market data.

What Can You Do with Generative AI in B2B?

And if you take a closer look at how generative AI specifically is being used in B2B companies, you might be surprised by how versatile and helpful it really is. 

• Auto-generating support replies: Based on ticket history and knowledge base content, generative models can draft first responses or reply suggestions, reducing time to resolution and improving consistency.

• Creating help docs and product FAQs: Product and support teams can use AI to turn ticket patterns or feature updates into readable, structured documentation that’s easier to maintain and scale.

• Writing personalized outbound emails: Sales and marketing teams are using generative AI to draft personalized cold emails or nurture sequences, pulling in context from CRM data and website behavior.

• Summarizing long threads and meetings: From Slack conversations to sales calls, generative models can condense long interactions into short, actionable summaries that save teams hours each week.

• Drafting internal reports and updates: Operations, product, and finance teams use AI to generate updates, weekly summaries, or slide outlines based on metrics, logs, and notes.

Closing thoughts

We’ve covered how B2B companies are using AI to automate customer support, boost sales efficiency, forecast better, and scale content creation. Whether through off-the-shelf tools or custom development, AI is helping teams do more with less. And let’s be honest—there’s no business on earth that wouldn’t benefit from automating parts of their operations to save time and money.

If you’re looking for an AI-powered B2B support solution, look no further than Thena. It’s built for modern teams that want to unify Slack, email, and chat, auto-triage tickets, and resolve issues where they start.

Book a demo to see Thena in action.

FAQs

What is the best AI-powered B2B customer support solution?

Thena is one of the best AI-powered support platforms built specifically for B2B teams. It connects tools like Slack, email, and chat to track, route, and resolve tickets from a single view. What makes Thena stand out is its ability to work across channels, use AI to triage tickets instantly, and surface relevant context—all without needing a massive team.

Will AI replace humans in B2B companies?

The purpose of using AI isn’t about replacing humans. In fact, AI is all about augmenting teams. Most B2B workflows are complex, collaborative, and relationship-driven. AI helps by handling routine tasks like summarizing conversations, scoring leads, routing tickets, and generating reports. This frees up teams to focus on strategic decisions, creative work, and human-to-human interactions that AI can't replicate.

What are the top ways to use AI in B2B companies?

AI can streamline operations and uncover insights across departments in B2B. Top use cases include:

• Automating customer support

• Scoring leads and optimizing sales outreach

• Forecasting revenue and reducing financial risk

• Summarizing internal data and meetings

• Generating content for onboarding, emails, and FAQs

• Predicting churn and monitoring account health