A recent State of RevOps 2024 report found that 82% of revenue professionals see the long-term benefits of AI but only half have deployed it for more than one use.
Artificial Intelligence (AI) continues to have its moment in 2024 in the sales and revenue landscape, with countless new platforms and providers shouting about its superhuman potential.
But with so few revenue professionals claiming they regularly use it in their roles, what’s holding us back?
And can it truly change the game?
AI in sales and revenue can refer to any number of applications, including the integration of intelligent technologies, such as machine learning, natural language processing, and predictive analytics, into various sales and revenue operations processes.
These AI-powered solutions are designed to streamline workflows, enhance decision-making, and ultimately boost revenue generation.
With so many areas AI could be applied in the sales and revenue function, it’s actually no wonder so many revenue pro’s are sticking their heads in the sand. These roles are all about problem solving and making revenue generation as smooth and efficient as possible, after all - so why would we add in something new when we’re not confident it’ll be a success?
AI tools in sales and revenue processes offers countless benefits, including:
Increased efficiency: AI can automate repetitive tasks, like data entry and updating the CRM, lead and opportunity scoring, and follow-up comms. As any revenue or sales person knows, freeing up sales teams to focus on high-value activities makes selling time more efficient - and keeps your reps happier too.
Improved insights and forecasting: AI-powered analytics can analyze vast amounts of data, uncovering hidden patterns and providing valuable insights into customer behavior, market trends, and sales forecasting. While this might be a process you enjoy in your revenue role, if your competitors are harnessing AI to do the same thing, chances are they are achieving deeper insights and at a bigger scale than you can alone.
Personalized customer experiences: AI can help sales teams deliver personalized and relevant communication, recommendations, and offers based on customer data and preferences, enhancing customer satisfaction and loyalty. As buyers grow accustomed to personalization in both B2C and B2B settings, the bar is raised. So you can’t afford to slip behind.
Better lead qualification and prioritization: AI algorithms can quickly assess and prioritize leads based on various factors, ensuring that sales teams focus their efforts on the most promising opportunities. Sales does require nuance and human instinct, but prioritization at scale can have significant benefits as you grow your operations.
AI in sales and revenue processes benefits various roles across the revenue function, including:
Sales reps, as AI provides sales reps with real-time insights, personalized content recommendations, and automated follow-up tasks, enabling them to be more productive and effective without the hands-on support of a manager. Great for remote or hybrid teams.
Sales managers benefit as AI-powered analytics can provide them with comprehensive performance data, enabling them to identify areas for improvement, coach their teams more effectively, and make data-driven decisions. Again, incredibly useful if you don’t have one-on-one or face-to-face time with your team every day.
Marketing teams use AI to help them better understand customer behavior, preferences, and journeys, allowing for more targeted and effective campaigns, and improved lead generation and nurturing.
RevOps teams use AI to streamline and optimize various RevOps processes, such as forecasting, territory planning, and commission calculations, improving overall efficiency and accuracy.
Here’s the thing.
You’re already using it.
Even if you’re part of that initial statistic that believe you’re only using it in one area of your sales process.
The reality is that platforms you have used for years have already baked AI - generic and custom models - into your workflow. It’s in your lead routing, your analytics and your forecasting. It’s in the automations, nudges or dashboards you look at every day. So don’t fear it, you’ve already made it part of the team.
The next step is to think about it and consider how best you can iterate your AI tools and workflows.
When using AI in sales, sales enablement, and revenue processes, there are several key considerations:
Data quality and integration: AI systems rely on high-quality, accurate, and well-integrated data sources to deliver meaningful insights and recommendations. So anything you’re not doing manually needs truthful, reliable data at its source.
Change management and user adoption: Implementing AI solutions requires effective change management strategies and user training to ensure seamless adoption and maximize the benefits. Check in with your team and find out how comfortable they are with your chosen tools and AI as a whole. Likely there will be things everyone can learn from each other.
Privacy and ethical considerations: As AI systems handle sensitive customer and sales data, organizations must prioritize data privacy, security, and ethical AI practices. Check-in with your CTO or DPO to make sure you’re on top of data privacy regs.
Continuous improvement and optimization: AI systems should be regularly monitored, evaluated, and optimized to ensure they remain effective and aligned with evolving business needs. Make this a part of your annual reviewing and planning sessions.
Cross-functional collaboration: Successful AI implementation requires collaboration between sales, marketing, IT, and other relevant teams to align goals, processes, and data sources. It only works when it all works!
There’s lots to think about here, while trying not to get overwhelmed. Particularly for RevOps and sales enablement leaders, you need to remember - your job is about problem-solving and easing friction.
So take some time to understand the potential of AI in your sales and revenue processes, and its benefits for various roles within your revenue function.
Identify specific use cases and opportunities to harness AI solutions that align with your team’s specific goals and pain points.
Develop a strategic roadmap for AI adoption, considering factors such as data integration, change management, and ethical considerations. And be sure to collaborate with cross-functional teams to ensure successful AI implementation, user adoption, and continuous optimization