Unlocking the Potential of Generative AI in Sales

Generative AI transforms sales functions, establishes more efficient processes, and enables deeper customer engagement. Gen AI helps sales teams create detailed consumer profiles and recommend targeted actions, allowing for more effective interactions and new up-sell and cross-sell opportunities.

This technology is projected to boost global sales productivity by up to 5%, with an estimated impact of $486 billion. However, only a minority of sales teams are currently implementing it. Let’s take a closer look.

Why It Matters

Overall, generative AI is projected to add between $2.6 trillion and $4.4 trillion annually to productivity, representing a 15% to 40% increase in value creation. Sales, along with Marketing and Customer Operations could capture up to 75% of this value due to their heavy reliance on unstructured data, which generative AI is well-equipped to handle.

Generative AI is set to transform sales by boosting productivity, improving customer engagement, and automating routine tasks. Though generative AI currently accounts for only 4% of global functional spending in sales (compared to marketing’s 10%), it offers a significant return on investment. 

In the long term, generative AI is expected to impact all customer journey stages, from lead identification and qualification to post-sale retention, by offering a range of advanced capabilities like personalized interaction recommendations, sales forecasting, and virtual sales assistants.

What’s Challenging

Despite its potential, a lot of companies face challenges when it comes to its adoption. Today, only 20% of sales functions are using AI at its full potential, and 90% of Sales leaders believe it remains underutilized. 

This limited adoption is mostly due to challenges with data quality and integration, employee resistance, and the vast array of Gen AI applications in sales operations. To address this, organizations can follow a structured, two-step approach for prioritizing AI use cases.

Step 1: Group Use Cases into Solution Packages

Creating specialized Packages composed of AI tools for different sales roles ensures the tools are used where they have the most impact. For instance:

• Sales Rep Copilot: Automates tasks like meeting notes, RFP generation, and personalized content creation, enabling sales representatives to focus more on client engagement.
• Sales Leader Copilot: Assists leaders with accurate forecasting by analyzing past sales performance, reducing reliance on manual data analysis.
• Virtual Business Development Agent: Automates lead generation by mining CRM, social media, and other databases, identifying potential leads, and crafting personalized messages.

Step 2: Prioritize Use Cases Within Each Package

Once the toolkits and packages are identified, prioritize individual use cases by assessing their alignment with core business objectives, time to value, implementation readiness, and risk:

• Efficiency vs. Effectiveness: Prioritize use cases based on the main objective. Efficiency-focused cases automate repetitive tasks, while effectiveness-focused ones, like real-time deal insights, improve sales impact.
• Time to Value: Start with cases that deliver quick results, such as automated meeting summaries, before moving to more complex solutions that require longer development.
• Implementation Readiness: Assess how prepared each use case is for deployment and how well it aligns with the company’s existing processes and resources.  Simple dashboards may be faster to implement than solutions that require coordination across multiple teams.
• Risk Assessment: In customer-facing roles, consider the risk of AI misalignment with brand messaging or customer expectations.

This approach allows companies to focus resources on high-impact AI initiatives, gradually building data assets and creating value across sales functions.

What’s Next

After prioritizing use cases, companies should establish a long-term strategy to enhance sales productivity with Gen AI. The time required for Gen AI implementation is half that of traditional AI because it requires less data and can produce value more quickly, at a fraction of the cost.

To help organizations structure their adoption, The Three Horizons Framework provides a long-term, gradual path to AI in sales.

Horizon 1: Augment and Automate

The initial phase of the Three-Horizon Framework focuses on automating existing sales tasks, creating immediate efficiency gains. AI-powered tools assist sales reps by automating routine tasks like CRM updates, meeting summaries, and personalized email generation. These tools improve productivity without disrupting workflows, freeing up time for high-value activities.

Horizon 2: Reimagine Workflows

The second phase is about using generative AI to transform workflows. This phase goes beyond automation, fundamentally rethinking processes and redefining roles. AI becomes more integral to complex tasks, such as Configure-Price-Quote (CPQ) processes and end-to-end customer outreach, reducing friction between sales and other departments. Additionally, AI blurs traditional boundaries between sales and marketing, as it initiates customer interactions that sales teams can later develop into full opportunities.

Horizon 3: Drive Transformational Change

The final phase aims for full integration of AI into the sales strategy, creating a major shift in customer engagement and market outreach. By allowing AI tools to manage a growing share of customer interactions, sales teams can scale their reach and engagement strategies. To succeed in this phase, companies will need to restructure roles, workflows, and customer engagement strategies, ensuring that AI adoption supports overall business goals.

Wrapping Up: The Role of Human Oversight in AI Adoption

Human oversight is essential to AI’s successful adoption in sales. According to 61% of executives, while AI significantly boosts sales productivity, human expertise ensures that AI outputs align with business goals and brand values. 

Hiring freelance AI consultants can be a cost-effective way to manage AI implementation, helping with smooth integration and reducing risks. These experts can also provide necessary training and support, addressing challenges like employee resistance and workflow disruptions.

🔹 For further reading:

🔗 How to Maximize the Business Value of Generative AI in Sales