Artificial intelligence (AI)—and its subset generative AI (genAI)—are here to stay. Many organizations are experimenting with AI for sales teams and seeing improved productivity, research, and personalization. But a lack of clear plans and objectives is holding some teams back.
Meanwhile, the gulf widens between B2B sellers’ and buyers’ use of genAI. Nine in 10 business buyers now use genAI at every stage of the buying process, from researching vendors to validating decisions, according to Forrester.
Sales leaders in the age of AI must address this imbalance by developing a strategy for driving impact. Otherwise, they risk falling behind. This post defines AI-related terms and offers a practical approach for building an AI strategy while avoiding potential pitfalls.
AI for Sales Management
Even sales organizations that have seen early success using AI might struggle to fit it into their long-term business strategy. Concerns with data privacy and security, employee readiness, and governance hold some companies back from advancing their AI initiatives.
To reap the benefits of using AI in the short and long term, sales leaders need a clear vision for how it will advance the goals of sales and the company. You must ensure your teams receive the most up-to-date sales training. Sales professionals who are comfortable using AI have a clear advantage over those who are not.
It’s also important to partner with security and IT leaders to protect intellectual property, preserve customer privacy, and safeguard brand reputation.
Some within your teams may already be using genAI tools. Having a coordinated approach can help make certain everyone is using it responsibly, in line with policies, and with tools approved by your company.
5 Steps for Rolling Out AI to Your Sales Team
These guidelines can help you reduce the risks that can come with AI adoption and help you get the most from this exciting tool.
1. Communicate a Clear Vision
Rolling out AI needs to be based on a realistic definition of your sales objectives and how it will advance your long-term strategy. For example, a core objective for most sales leaders is to improve account management. GenAI can be used to create more personalized experiences that build loyalty. Use your goals and KPIs to help develop a clear vision for how your team will use AI.
2. Build Guardrails
Having a process in place for AI adoption and use is critical to avoid privacy compliance risks and brand damage. Work closely with your legal and security teams to understand intellectual property issues, the regulatory space, copyright protections, and how to safeguard internal data.
3. Understand Tech Requirements
AI is deceptively easy to use but decidedly complex in its implementation. Work with your CIO or CTO to understand what the technology can do, what its limitations are, and what
additional investments you will need. Build knowledge of large language models and what’s required to train and prompt these systems.
4. Get Your Data in Order
AI is only as good as the data it learns from. Yet many organizations are plagued by inconsistent, incomplete, or scattered data. Work with your sales operations team to build a plan for cleaning, structuring, and organizing data, including unstructured data such as customer feedback within surveys and social posts.
5. Upskill Your Team for a New Era
Elevate your sales team’s understanding of AI and its capabilities and limitations. Explore practical applications of AI tools for sales coaching and reinforcement. Encourage persistence in using tools until it becomes a natural way of working, and be sure your sellers are educated on evaluating the output.
Know Your AI: A Glossary of Terms
AI is a broad field encompassing many types of systems. Here are some of the terms you may hear.
Artificial Intelligence (AI): A set of technologies that enables machines to mimic human intelligence and perform advanced functions. Traditional AI is used to analyze data and make recommendations and predictions; solve complex problems; and automate tasks.
Generative AI (genAI): A type of artificial intelligence that uses large language models and massive amounts of data to create new content such as text, images, and code.
Large Language Model (LLM): A type of AI model that is trained on vast quantities of data, allowing it to generate human-like text and perform tasks such as translating, summarizing, and answering questions.
Machine Learning (ML): A field of AI that focuses on enabling systems to learn from data without programming. LLMs are a subset of machine learning that excels in natural language processing.
Prompt Engineering: Creating and refining input (questions, requests, etc.) to guide the genAI to generate accurate and relevant responses.
Private GenAI: The use of AI within an organization’s internal systems, which allows companies to maintain control over their data, protect sensitive information, and ensure the AI is tailored to their specific needs.
Public GenAI: LLMs and other genAI technologies that are accessible to the public—often through online platforms such as ChatGPT, Claude, Perplexity, Gemini, as well as DALL-E 2, an image generation model that creates images based on text descriptions.
Build Your AI Practice
AI will shape the future of B2B sales. CROs and other sales leaders who move quickly and thoughtfully will gain a decisive edge.
As you navigate this new territory, The Brooks Group can help. For more than four decades, we have helped companies across industries train and develop high-performing sales teams in every business climate.
Get in touch to learn how we can help you meet changing buyer needs, advance business goals, strengthen ROI, and empower your team.
