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How to Maximize the Business Value of Generative AI in Sales

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Generative AI is transforming sales functions, establishing more efficient processes, and enabling deeper customer engagement. Generative AI in Sales can create detailed consumer profiles and recommend targeted actions to improve interaction at each stage of the sales process. Additionally, AI-generated conversation scripts help sales representatives with personalized recommendations, including up-sell and cross-sell opportunities, driving more effective customer interactions.

The estimated impact of Gen AI spending on sales is massive. It is forecasted to account for 4% of global functional spending, compared to 10% of marketing, but with an overall productivity improvement of $486bn, superior to marketing’s $463bn. In the long run, Gen AI is meant to be integrated into the entire customer journey, blurring the line between marketing and sales - thus requiring new skills development to ensure a smooth implementation.

Implementing generative AI could boost sales productivity by up to 5% globally. However, adoption remains relatively low, with only 20% of sales functions fully embracing AI technology. This article will explore how businesses can unlock the value of Generative AI in sales by answering the following questions:

How can AI revolutionize the customer journey?
What are the most impactful use cases for AI in sales?
How can companies prioritize and implement AI use cases to maximize efficiency?
What frameworks can businesses follow to ensure the successful adoption of AI in sales?
How can human oversight complement AI tools to ensure alignment with business goals?

Gen AI Has the Highest Potential in Sales

Generative AI holds significant economic potential, with the capability to contribute an additional $2.6 trillion to $4.4 trillion in annual productivity. This marks a substantial increase over the $11 trillion to $17.7 trillion currently generated by non-generative AI and analytics. This could result in a 15% to 40% increase in value creation, driven by new use cases and enhanced productivity.

Sales and marketing, together with product R&D, product development, and customer operations, are poised to capture up to 75% of the total value generated by GenAI. Sales could achieve over $400 billion in value creation, while marketing follows closely behind. The reason behind this is that sales heavily rely on unstructured text and general reasoning, which aligns with Gen AI’s core capabilities.

Gen AI is experiencing an early momentum, but 90% of commercial leaders still think that AI is underutilized. Over 75% of sales departments have invested in AI and sales analytics, but only 20% have fully adopted the technology. It is thus essential to understand the possible use cases of Gen AI in sales, as well as how organizations can exploit their full potential in the long term.

How to Prioritize Use Cases of Generative AI in Sales

Generative AI Use Cases in Sales and the Need for Prioritization

As underlined in the latest article analyzing the potential of AI for Marketing, generative AI is reshaping the first steps of sales processes like lead identification and personalized outreach. However, its potential in sales is even larger, covering every stage of the customer journey. This is due to the ability of generative AI models to “learn” tasks based on large-scale data, enabling a wide range of applications throughout the sales process.

Generative AI enhances efficiency across the customer and sales journey and has numerous applications, including:

Lead identification and activation: AI segments and targets leads using patterns from customer and market data, creating more effective lead-generation campaigns.
Autonomous lead qualification: AI-powered agents can send automated emails and mine prospect information, streamlining the lead qualification process.
Virtual sales copilots: Sales representatives can use virtual assistants that access internal and competitor product specifications, expanding product expertise and improving communication with potential clients.
Automated RFP creation: Representatives can automate the generation of requests for proposals (RFPs), with AI completing responses based on successful past RFPs.
Personalized outreach generation: Automated tools can quickly create tailored collateral, drawing on customer and product data to customize messaging.
Proposal support and deal closure: AI generates hyper-personalized content for follow-ups and provides real-time negotiation guidance based on historical data, leading to higher conversion rates.
Post-sale customer retention: AI improves retention with personalized onboarding, tailored training content, automated customer support, and churn modeling.

With the wide range of potential applications, prioritizing them becomes essential. Generative AI offers about 10 times more opportunities than traditional AI, but this abundance makes it more challenging to identify the most valuable areas to focus on.

Organizations need to carefully assess where AI can have the greatest impact on efficiency, customer experience, or revenue generation, ensuring they invest resources in the right opportunities.

A Two-Step Approach for Prioritizing Generative AI in Sales Use Cases

To extract the most business value from Generative AI in sales, companies must prioritize the use cases that align with their sales strategy. According to Bain & Company, a two-step approach can be followed.

Step 1: Group Use Cases into Solution Packages

A solution package is a bundle of AI-driven use cases designed to automate or augment a specific role within sales. For example, a sales rep copilot package could include Gen AI applications that gather insights from sales interactions, automate routine tasks, offer real-time coaching, and create personalized content for customers.

In practice, grouping use cases into these packages helps focus on a defined set of tasks, allowing for more targeted improvements in specific roles like sales representatives or sales leaders.

The most relevant Sales packages include:

Sales Rep copilot: Generative AI powers the Sales Rep Copilot by automating routine tasks and providing intelligent, real-time support during customer interactions. It uses natural language processing (NLP) to generate automated meeting summaries, eliminating manual note-taking and ensuring critical details like follow-ups and next steps are captured seamlessly. This copilot also uses AI to generate personalized sales collateral, pulling from CRM data and customer insights to create highly tailored materials that resonate with prospects and clients.
Sales leader copilot: The Sales Leader Copilot leverages generative AI to streamline forecasting and team management processes. By analyzing past sales performance, market trends, and customer behaviors, the AI-powered copilot can generate highly accurate forecasts, reducing sales leaders' time on manual data analysis.
Virtual business development agent: Generative AI drives the Virtual Business Development Agent by automating lead generation and prospecting activities. The AI identifies potential leads by mining structured and unstructured data from various sources, such as CRM systems, market databases, and social media platforms. It qualifies these leads autonomously by sending personalized outreach communications, using AI to craft tailored messages that resonate with individual prospects.

Step 2: Prioritize Use Cases within Each Package

Once the most relevant solution packages are identified for the business, the next step is to prioritize the use cases within each package. To do so, companies should first define their overall business objectives (efficiency or effectiveness in sales operations). Once this is clear, assess which use cases align best with these objectives and prioritize accordingly.

The prioritization process involves evaluating each use case based on its impact, time to value, implementation readiness, and associated risks. Taking for example the sales representative solution package:

Business objectives: Prioritizing efficiency is ideal when the goal is to streamline repetitive, time-consuming tasks like RFP responses or meeting summaries. On the other hand, companies should prioritize effectiveness when the goal is to enhance sales impact through deeper personalization or real-time data insights.
Time to value: Consider how quickly each use case will deliver returns. For example, implementing a meeting assistant that generates summaries and reminders may yield fast efficiency gains, while advanced knowledge management may take longer to deliver improvements.
Implementation readiness: Evaluate the organization’s ability to deploy these solutions, including training, disruptive potential aligned with culture, and so on. Simpler use cases like sales analytics dashboards might be easier to implement than more complex, cross-functional solutions.
Risk assessment: Consider the risk level associated with each use case, especially when deploying AI in customer-facing roles. This includes the possibility of AI-generated content being misaligned with the brand’s messaging or customer expectations, AI “hallucinations” or employee reluctance (e.g, fear of losing their job).

By focusing on the highest-impact use cases and implementing them in small, manageable packages, companies can gradually build data assets that compound value across other sales functions. This allows for a more scalable and strategic adoption of Gen AI in sales.

The Adoption of Generative AI in Sales: the Three Horizons Framework

Once the use cases are prioritized, companies should develop a long-term strategy to implement and augment 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 offers a path to implement Gen AI in sales. Each horizon focuses on building key capabilities while driving incremental transformation in the sales process.

Horizon 1: Augment and Automate

The first phase of the Three Horizons Framework focuses on augmenting and automating existing sales tasks. Generative AI assists sales teams by automating administrative tasks, allowing reps to spend more time on high-value activities.

Gen AI personas act as expert assistants, streamlining tasks such as meeting summaries, RFP generation, and personalized email creation. For example, sales representatives can use AI to automatically update CRM data, generate call summaries, and receive real-time deal insights.

By starting with straightforward automation, companies create immediate efficiency gains, freeing up valuable time for customer engagement. In this phase, the focus is on doing what sales teams already do, but faster and more efficiently, without disrupting existing workflows.

Horizon 2: Reimagine Workflows

Horizon 2 is about reimagining sales organization’s workflows using generative AI. This phase moves beyond automating existing tasks and seeks to fundamentally reshape how work is done.

Gen AI personas take on more complex roles, such as managing configure-price-quote (CPQ) processes, automating customer outreach, and generating comprehensive RFP responses that improve bid quality. By integrating AI more deeply into sales operations, teams can reduce friction between departments like sales and commercial strategy, enabling smoother collaboration.

Generative AI also blurs the lines between sales and marketing. For example, AI-driven campaigns can initiate customer interactions that sales teams later develop into full-fledged opportunities.

To succeed in this stage, organizations must embrace the idea that using GenAI is not just a technological transformation—it’s a business transformation. Sales teams need to be trained to work effectively alongside AI. This phase emphasizes the redesign of workflows to capture the full potential of AI in sales.

Horizon 3: Drive Transformational Change

The final stage of the framework involves driving transformational change by fully embedding AI into the core of the sales process. This stage focuses on scaling AI’s impact to transform sales strategies and market engagement.

By allowing AI personas to handle an increasing share of customer interactions, sales organizations can reach far beyond their traditional capabilities. AI enables companies to replicate the behaviors and tactics of top-performing sales teams, extending their reach and influence across larger market segments.

Achieving this level of transformation requires more than just technological upgrades; it calls for a reimagining of organizational structures, workflows, and even market strategies. Sales, marketing, and customer service functions may need to be integrated to ensure that AI can deliver consistent value throughout the customer lifecycle.

Organizations must also address potential risks, such as brand reputation management, as AI takes on more customer-facing roles. While most companies are still early in this journey, those that succeed in Horizon 3 will have the ability to scale their sales efforts and achieve a competitive advantage.

Wrapping up: the importance of Human oversight in Generative AI

Human oversight remains essential when implementing Generative AI in Sales. 61% of executives believe AI will significantly boost sales efficiency, but they also emphasize the need for human expertise to manage and optimize the process.

Human involvement ensures AI outputs align with business goals and are free from biases that may arise from algorithms alone. For many organizations, the integration of AI in sales is best supported by skilled consultants who can bridge the gap between technology and strategy.

Engaging freelance AI consultants offers a flexible and cost-effective way to implement advanced AI strategies in sales while maintaining the necessary human oversight. These experts can ensure a smooth deployment, provide training, and manage risks arising during the integration process.

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