Harnessing AI in sales: Strategies for smarter selling

Integrating AI in sales: from vision to value

The topic of how, if, and when to implement AI in sales to work alongside existing CRM solutions is top-of-mind with executives around the globe. Yet, according to McKinsey, just 21% of B2B sales teams at enterprise companies report fully integrating AI automations and tools into their processes, while 22% have run limited pilot programs.

These statistics are more impressive, however, when adjusted for adoption timelines. In 2025, large language model (LLMs) chatbots already enjoy a nearly 45% usage level. It's clear that these new AI for B2B sales tools are not just improving workflows and outcomes; they're becoming table stakes for companies to remain competitive.

The good news for CROs and CIOs? Unlike the scale and scope of traditional SaaS migration implementations, rolling out AI B2B sales and marketing solutions can be completed relatively quickly and affordably. And, instead of ROI taking months to be felt throughout an organization, benefits to sales teams are evident almost from day one.

But every organization is different, and so selecting the right approach is imperative. Stakeholders must have a clear vision of which challenges they're trying to solve, who will be using the AI B2B marketing and sales tools, and how they'll measure the success of a new AI software investment.

Using AI-driven sales strategies to solve pain points

A sales team's performance can be impacted by everything from legacy software limitations and company culture to market conditions and management decisions. However, they all tend to share certain challenges. The following AI in sales examples illustrate how its capabilities can support and solve challenges for most B2B teams:

Administrative overload: Agentic chatbots and virtual assistants can handle initial queries from prospects, write e-mail updates and responses, and flag follow-up tasks to help salespeople use their time more efficiently while compressing B2B sales cycles.

Connecting the data dots: Context and history about a customer or deal often spans months and involves many people and moving parts. AI agents can gather all relevant details in an easy-to-read report, so salespeople have everything needed at their fingertips when meeting with or responding to prospects.

Lead scoring in the sales cycle: Sales teams are eager to follow up on leads but often lack insights into which ones are most likely to convert, and in which fiscal quarter. Using machine learning, lead score features help predict each prospect's buying stage and can significantly improve sales results.

Forecasting and prediction: Deep data analysis and pattern matching identify sales trends, anticipate disruptions, flag customers for potential churn, and more. Armed with timely insights, sales teams can pivot on products or address issues that avoid negative impacts while potentially growing sales.

Building business relationships: Offloading administrative tasks frees up time for salespeople to focus on personal contact with prospects and customers, learning about their needs and goals. This allows them to right-size and customize solutions for each client. AI sales tools can also assist with gauging sentiment, flagging issues, forming insights for negotiations, and more.

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How new AI in sales automation can improve outcomes

Unlike other recent automation technologies that primarily improve productivity, the latest AI capabilities for B2B sales can now play an active role in generating revenue alongside your salespeople. Many companies have figured out how to use AI in sales and are now beginning to implement virtual sales agents. Preliminary outcomes from these creative use cases are encouraging:

• Reaching out at the right time
  Teams with well-tuned AI-driven sales strategies and automations benefit from the system acting as their eyes and ears to track current customers and sought-after leads. Agents monitor leadership changes, product launch announcements, location expansions, and more. When relevant, sales teams are nudged at the right time with new data and suggested pitches to pave the way for engagement.
• Activate new segments
  Keeping up with fast-moving industry changes is challenging and can leave business leadership feeling blindsided by events like supply chain disruptions and pricing fluctuations. Automated AI sales agents monitor social media sentiment, track competitor developments, follow specific news topics, and help anticipate product demand in a new market. This enables companies to respond appropriately and proactively.
• Tailored sales coaching
  While every successful salesperson has many strengths, no one strikes gold every time. However, by using AI sales tools to analyze negotiation tactics and identify win/loss patterns, teams can build an internal best practice guide to elevate each rep's performance. Companies can include this in their onboarding process to help new employees hit the ground running.
• AI co-selling support
  While still in development, innovative sales teams are beginning to build out complex agentic workflows, which act as virtual sales assistants. For example, the agent can perform research, gather contextual data, create an initial sales deck, and provide price negotiation considerations before a salesperson ever places their first call.

Giving AI decision makers the details they need

When selecting AI-driven sales and marketing tools, savvy executives get ahead of budgeting pushbacks by making their cost/benefit analysis specific and easy to understand for a non-technical audience. This works well since finance is often in the purview of company leadership who may not immediately see the value of a novel tech investment.

Here are key benefits to include when discussing implementing AI in B2B marketing and sales software for your organization:

• Enhancing, instead of adding
  Unlike large-scale IT projects, AI for B2B sales solutions are designed to integrate with existing software, workflows, and roles in a company. Reps are prompted and guided within existing dashboard environments, so adoption feels intuitive and helpful. In addition, these tools transfer to salespeople's mobile apps, so they're supported wherever the workday takes them.
• Incremental, instead of immediate
  If leadership isn't sure about a department-wide rollout or licensing commitment, start small. Prioritize which team could benefit most from AI sales automations and agents. Next, provide real-world use case examples from your industry, and propose a three to six-month pilot program to test its effectiveness.
• Visible, instead of opaque
  Agree on a specific timeline and the KPIs you'll measure against, then let the B2B AI sales program run. Unlike ERP digital transformations, where the ROI isn't obvious right away, your team will quickly know if they're meeting more prospects, saving more time, and closing more deals.

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Looking ahead: AI for B2B sales and marketing

The age of AI is in full force, and businesses of every size around the world are assessing how to take advantage of its many benefits. Companies shifted from questioning if AI tools were worth the investment to prioritizing AI-driven sales strategies in a short time span. In fact, according to Forbes, "75% of B2B automation decision-makers in Forrester’s Automation Survey, 2024, indicated that they expect their organization to invest in sales automation in the next 18 months."

Some salespeople may have reservations about adopting AI tools into their workflow, as well as concerns about AI taking over their role. However, it’s clear that customers still need and expect a human element in their vendor and partner relationships. Rather than replace anyone, new B2B AI sales tools allow for more in-depth, genuine connections and collaborations between both sellers and buyers; and that's a win for everyone.

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