Customers love personalized hand-holding, yet rapid tech advancements have left many sales teams struggling to keep up. While account executives are familiar with their CRM systems and excel at relationship building, they often rely heavily on their sales engineer partners for more complex, technical tasks.

The question is, how can artificial intelligence (AI) collaborate with customer-facing revenue teams without negatively impacting customer relationships?

Sales teams generate extensive amounts of data, and presales engineers handle lots of transactional tasks. This is seemingly the perfect context for AI assistance. Identifying opportunities for AI to interpret, learn, and act allows for a balanced integration without losing the essential human touch in sales.

In this comprehensive guide to AI in sales, we will discuss the current state of AI for revenue teams, practical use cases for sales representatives and sales engineers, and how we expect AI to transform customer-facing teams.

What is AI in Sales?

AI in sales is the use of technology to improve sales and presales processes. It relies on machine learning, natural language processing, knowledge graphs, and predictive analytics tools. Working together, this tech makes tasks more manageable and helps sales close more deals, more efficiently.

But is AI necessary for customer-facing account executive and presales roles? According to analyst research:

“By 2026, 65% of B2B sales organizations will transition from intuition-based to data-driven decision making, using technology that unites workflow, data, and analytics.”

Gartner

Research has also shown that sales teams spend only about 23% of their time directly selling. The rest of the time is spent on repetitive administrative tasks, including meetings, data entry, pipeline management, and technology issues. 

As Chief Revenue Officers (CROs) and sales managers, you aim to boost sales team productivity. AI can reduce administrative tasks, letting your team focus more on building customer relationships and closing deals. 

That’s the power of AI in sales.

Benefits of AI in Sales

AI is a game-changer for most professional roles, bringing many benefits that streamline sales processes and boost efficiency. Here’s how it can transform your sales and presales operations:

• Enhance customer engagement: Based on potential customers’ data, recommend personalized content, product offers, and communication strategies. For example, AI can analyze customer feedback, behavior, and gaps to suggest the most relevant products during sales interactions.
• Improve sales team focus: Optimize resource allocation to concentrate on high-value sales activities. AI can analyze data from various sources to identify which leads are most likely to convert, allowing sales teams to prioritize their efforts on what matters most.
• Boost sales forecast accuracy: AI uses real-time pipeline analysis to predict future sales trends with precision.
• Increase productivity: AI automates routine tasks, freeing up time so that account executives and sales engineers can focus on closing deals. For instance, AI can quickly answer technical product questions, schedule meetings, and update CRM entries by analyzing unstructured data from emails, telephone calls, chats, and calendars. This eliminates the need for the sales engineer to conduct extensive research or perform repetitive, time-consuming data entry tasks. According to McKinsey, generative AI has the potential to automate up to a fifth of sales team functions. 
• Increase conversion rates: AI can target high-potential leads with predictive analytics and lead scoring. It can score leads by analyzing behavior and engagement data, enabling more focused and successful sales efforts.

How are Sales Professionals Using AI Right Now?

Sales professionals use AI tools for many tasks, including lead generation, personalized guidance, and supporting various sales strategies. They also automate mundane tasks, which greatly improves output and buyer interactions.

In presales, AI is primarily used for technical support and preparing detailed proposals. Presales engineers leverage AI to answer technical questions, generate tailored technical content, and provide valuable insights for crafting accurate, compelling proposals. AI tools also help analyze complex customer requirements and historical data to recommend the best solutions, ensuring a smooth transition from lead generation to deal closure.

The Role of Generative AI in Sales and PreSales

Gen AI is poised to transform sales and presales by continually improving capabilities to enhance customer engagement and boost productivity. 

Top use cases include:

Sales Forecasting

AI provides succinct forecast summaries by leveraging predictive machine learning models for statistical inference and offering real-time visibility into customer sentiment and areas for improvement. This includes anticipating demand, allocating resources, and strategizing futures.

Meeting Notes and Action Items

Forget about manually taking notes during meetings. AI transcribes meetings, summarizes points, and generates action items automatically.

Optimize Sales Pitches

Generative AI helps optimize sales pitches by analyzing what resonates most with your target audience. By tailoring presentations to highlight selling points and addressing customer needs, AI ensures your pitches are compelling to each individual prospect.

Analyze Sales and PreSales Calls

AI eliminates the guesswork involved in analyzing important areas of sales calls. It provides data-driven, real-time insights into customer sentiment, concerns, and areas for improvement. Instead of requiring sales reps and managers to review entire 30—to 60-minute calls looking for soundbites or problem areas, AI proactively surfaces insights and highlights so they can make proactive, informed decisions. 

Answer Technical Questions

Generative AI can assist presales engineers by providing accurate and detailed answers to technical questions. By leveraging vast amounts of technical data, AI ensures potential customers receive timely and precise information.

Recommend Next Steps 

AI suggests the best next steps for selling teams based on lead and customer data. Whether scheduling a follow-up call or sending additional information, AI ensures that each action is strategically aligned to move the deal forward.

Generate Emails and Subject Lines

AI uses past interactions and customer preferences to craft personalized, compelling emails and subject lines to boost engagement.

Identify, Prioritize and Qualify Leads

AI can analyze large amounts of data to find, rank, and qualify leads based on their level of engagement and potential value. Sales teams will have the information to focus on the best opportunities from the start. This will improve the quality of leads in the sales pipeline.

Predict Likeliness to Close

Generative AI predicts deal closures by analyzing customer behavior patterns. Sales teams can ensure they allocate resources to opportunities with the highest potential.

Monitor Competitors

AI-powered competitive analysis will continuously monitor competitor activities, analyst research, and market trends. The sales team will have competitive intelligence when it’s needed most.

Handle Objections

Generative AI helps sales teams handle objections like price, value, and timing. They can overcome hurdles with responses and solutions that are current and accurate. 

Reporting

AI simplifies most of the reporting process by supporting natural language interaction with reporting tools, rather than keystroke combinations and traditional sort/filter functionality. This empowers sellers and sales leaders to analyze vast amounts of data quickly. It provides real-time and forward-looking metrics that help sales leaders track sales performance, identify trends, and make informed, data-driven decisions.

Meeting and Demo Preparation

Gen AI can assemble talking points for meetings and product demonstrations, creating interactive and personalized sales presentations based on customer needs, gaps, and industry requirements. This saves sales teams time and provides a consistent and tailored experience for each prospect.

Future Predictions of AI in B2B Sales

AI is rapidly integrating into all business areas and has had a transformational impact on sales organizations. AI excels at automating repetitive tasks and analyzing data with precision and speed. However, human empathy and intelligence are irreplaceable. 

As AI evolves, the future of sales jobs will depend on the unique human skills that AI cannot replicate. Workers should develop and hone these skills (personality, human empathy, etc.) to remain valuable and relevant in an AI workplace.

Will AI for PreSales Replace Sales Engineers?

AI in presales is more about improving collaboration than replacing roles. AI acts as a digital collaborator or assistant, handling repetitive tasks and data analysis, allowing sales engineers to focus on complex, strategic activities, such as a very customized proof-of-concept implementation. AI can help identify gaps in the customer’s current processes and products and filling them with your tailored solutions. 

AI generates technical proposals, answers common technical questions, and recommends solutions based on customer data. While this technology will take over certain duties, it won’t replace the need for human intelligence and empathy in the sales process. Instead, it will enhance the capabilities of sales engineers, enabling them to provide a more personalized customer experience.

Considerations and Best Practices for AI in Sales and PreSales

Using AI in presales requires a careful approach to enhance, not replace, the human touch, which remains a fundamental sales component. AI can improve personalization by analyzing unstructured data in the background. Its true power is using models tailored to your company’s specific data, use cases, and contexts.

Here are some considerations and best practices:

• Maintain human connections: AI should enhance personalized customer interactions, not replace them.
• Use custom models: The success of AI depends on models customized to your company’s specific data and use cases. Custom models provide more relevant recommendations, leading to better outcomes. To handle complex data, knowledge graphs are essential as they manage interconnected relationships, enhancing AI’s ability to understand these connections. Ensure your AI systems leverage knowledge graphs to improve data integration.
• Address the talent shortage: Implementing AI requires specialized skills. AI experts understand both AI technology and business needs. Many companies turn to regions with high AI expertise, like India, or upskill their current workforce to meet the growing demand.
• Monitor inaccuracy: Nothing can hurt a deal more than bad advice. AI models require regular monitoring, validation, and updates to maintain accuracy.

Integrating AI into PreSales

AI adoption is skyrocketing, and it’s easy to see why. The capabilities are leaving users amazed, and the industry is only in its early stages. Experts suggest starting with pilot programs to test AI applications in presales before going all-in. This helps identify the best use cases and resolve any issues early on.

To make AI integration successful, set a North Star direction and purpose, build a strong performance orientation, create information transparency, accelerate decision-making, and empower your teams through leadership that serves them. 

Here are three key steps to ensure a smooth and effective AI integration:

• Assess current processes and data quality: Conduct a thorough audit to identify areas where AI can automate tasks and improve lead conversion predictions. Be sure your team focuses on high-quality data when capturing activities, product gaps, and documentation. The value of AI is dependent on data accuracy.
• Training and communication: Organize workshops to train sales, IT, and other relevant teams, ensuring everyone is prepared to leverage AI tools. Training should go beyond technical skills and encourage team members to rethink their approach to problem-solving and scheduling.
• Pilot projects: Start with small-scale pilot projects to test AI applications, gather feedback, and refine your approach before a full-scale implementation.

In an upcoming article, we’ll share real-world case studies of presales teams using AI in action, with practical tips, tricks, and advice that will get your team a collaborative partner to augment the work of presales engineers.

Experience the Balance of AI Capabilities with the Human Touch

Despite AI’s impressive capabilities, some aspects of sales will always need a human touch. AI can’t replace the empathy and personal connections that sales professionals build with customers. Human interactions, understanding emotions, and earning trust are crucial for successful sales relationships. A robot cannot do this alone. Strategic thinking and complex problem-solving often need human creativity and intuition, keeping sales professionals indispensable. Focus on sales enablement efforts that perfect soft skills like empathy, personality, and communication, as well as hard skills like creative planning and strategic problem-solving, to stay ahead.

Are you curious how AI can enhance your presales efforts without losing the human touch? Request a demo from Vivun today.