Revolutionizing sales in distribution: Harnessing the power of AI

July 24, 2024Generative artificial intelligence (gen AI) promises to be the transformative technology of our time, catalyzing the benefits from decades of progress in advanced analytics and AI that preceded the debut of ChatGPT. By using sophisticated machine-learning-algorithms known as large language models (LLMs), gen AI can simulate human cognitive functions to produce innovative and original content. It is rapidly becoming a game changer in the distribution industry, with potential in areas including marketing and sales, supply chain, operations, procurement, customer service and support, back office, legal, and human resources.

Of these functional areas, the broader commercial space—sales, marketing, and customer service—is arguably the largest area of impact and disruption for gen AI, offering companies a source of competitive advantage that can drive profitable growth.

AI-powered sales analytics enable distributors to efficiently manage and remain current with their extensive customer base, a task challenging for humans and basic tech. Serving the long-tail of customers is resource-heavy; gen AI enhances customer service without increasing head count.

Improving customer life cycle management with AI

In today’s hypercompetitive environment, distributors are increasingly turning to AI to enhance their growth capabilities. These technologies can provide deep insights into customer behaviors and preferences, automate customer interactions, and improve the efficiency of processes and workflows.

In B2B sales, AI can be applied in multiple use cases (Exhibit 1 illustrates the first three):

1. Next best opportunity. An AI-powered analytics engine can generate leads and recommend products to specific accounts. Actionable opportunities can be identified and prioritized based on factors such as opportunity size, company information, and relationship strength. While this is possible with current capabilities, it is not scalable given how many small and medium-size businesses distributors manage. One industrial distributor deployed gen AI to identify more than $2 billion in white space leads. The technology prioritized specific end customers, created customer-specific value propositions, and generated materials for sales outreach.
2. Next best action. An AI engine can recommend the type and timing of customer contacts, such as when to follow up or how best to reengage. These systems can generate account plans with recommended outreach channels and leverage gen AI to draft emails and texts for sales teams. Currently, figuring out the best action requires knowledge sharing of best practices between reps, which often does not happen. Gen AI can store, prioritize, and match best practices based on the situation. A global B2B software provider saw a 30 percent increase in bookings by prioritizing products and empowering salespeople with talking points and account insights from next-best-action technology.
3. Smart research assistants. Gen AI “copilot” systems can research complicated customer queries and provide sales reps with materials and answers. Copilots can also answer questions as customers navigate self-service options, adapting their responses based on the customers’ prior research and historical interactions. Without gen AI, reps are usually responsible for their own customer research, which is time intensive and takes away from other activities, such as interacting with current customers and finding new ones.
4. RFP and RFQ responders. Gen AI programs can automatically produce humanlike responses to common request for proposal (RFP) questions. Proposals and quotes can be generated as slide decks or written documentation. Responders can also analyze historical RFPs to identify common “winning themes.” Without gen AI, most RFPs and requests for quotes (RFQs) are done manually and take significant time to complete. A water technologies company used a gen-AI-powered virtual sales and RFP/RFQ assistant and RFP/RFQ responder to generate personalized emails to customers. The program, which took only six weeks to develop, generated $1.8 million in quotes for 45,000 customers within four weeks of deployment.
5. Smart pricing. Dynamic smart pricing can provide personalized discount guidance and deal scoring. A gen AI program can assist with negotiation and value proposition support. Distributors often utilize static pricing software and dedicated teams to determine pricing, solutions that are rigid and resource-intensive. A global B2B petrochemical company captured around $100 million in additional earnings across six business units with a machine-learning-enabled dynamic pricing model. To drive dynamic pricing recommendations, the technology clustered customers into microsegments based on more than 100 characteristics.
6. Meeting support. Gen AI can help sales and customer service reps prepare for meetings by synthesizing notes from previous meetings and compiling articles and other relevant information. After the meeting, gen AI can summarize notes and next steps for reps. Reps can move on to other calls or prioritize other work (prospecting, for example, or serving customers), rather than spend time recapping.
7. Smart coaching. Gen AI systems can deliver personalized training for sales reps, including simulated interactions with performance scoring. Automated sales performance analytics and nudges can improve reps’ performances. Current training programs are done in person or via video conferencing software (Zoom, for example) or prerecorded videos. Leveraging gen AI to personalize and scale training benefits all stakeholders. A global agrochemicals player transformed its commercial processes and trained more than 650 sales and marketing professionals with AI-enabled coaching.

Key considerations for starting your AI journey

Based on the experience of early adopters, the key to AI implementation lies in the combination of strategic strength and tactical flexibility. Successful adopters have a clearly defined strategic approach, focus on maximum value creation, and are prepared to iterate early and often in pursuit of those objectives.

Here are three important things to get right:

1. Build a strategy-led road map. Create a development road map aligned with strategic AI goals by prioritizing business problems such as reducing churn and corresponding use cases (for example, identifying factors in transaction or operational data linked to customer dissatisfaction). These use cases should be scalable to improve enterprise-wide capabilities. Given that AI can have many applications and business implications, a road map that aligns with your organization’s business objectives and goals is crucial. Your AI strategic goals should be based on the value at stake, organizational readiness (Are my sales teams ready to incorporate AI into their daily tasks?), and key design questions (Will we deploy with select sales teams or with everyone?).
2. Build capabilities. Developing essential capabilities for AI is crucial as they can sometimes determine the success of an AI strategy. Capabilities include the right talent, data sources, operating model, and technology ecosystems; and they can often require greater resources than organizations initially expect. Organizations can begin to identify and develop capabilities by launching minimum viable products to prove feasibility of AI and gathering user feedback from, for example, salespeople who could be potential users. Agile testing and iteration can help organizations develop the right capabilities, minimize barriers to adoption, and scale AI effectively.
3. Lead the change. Implement a structured change management plan with clear impact measurement mechanisms in place that encourages adoption and scaling, as well as considers gen AI risk and guardrails. Identify and empower change leaders to drive successful adoption and integration of gen AI technologies within the organization. It is important to have a clear perspective on potential risks and to make design and hosting environment choices that consider the required controls and policies (for example, where customer data resides in the tech stack and how access to the data will be managed and controlled).

Key learnings from implementing gen AI

The first wave of gen AI has already begun to affect organizations with enhanced productivity and a focus on AI as a strategic differentiator. As the second wave of innovation begins, the following are a few key learnings to consider:

1. Gen AI is a catalyst for growth in AI. The past 40-plus years have been ones of advancement and innovation in artificial intelligence. As gen AI becomes more advanced, it will play a transformative role in the broader field of AI by significantly accelerating development, adoption, and innovation within AI technologies.
2. Speed is a strategy. There is a material cost to waiting, so early experimentation is key. Rapid action and early adoption of AI technologies within an organization is crucial to avoid significant missed opportunities caused by delays in developing use cases and capabilities.
3. Top-down value should be the focus. Unlocking transformative enterprise-scale impact will take effort. The solution is to prioritize initiatives that maximize value creation and have the greatest impact on an organization.
4. Gen AI is complex. Significant technology investments (cloud, for example) are crucial to leverage the full capabilities of gen AI; LLM models are only around 15 percent of the value and are just one part of a broader ecosystem of technologies and capabilities that need to be developed to realize the full benefits of gen AI.
5. There will be takers, shapers, and makers. Organizations need to determine their strategic direction by assessing the trade-off between their desire for differentiation and the level of investment they are willing to make.
6. The first wave focused on enhancing productivity; the emerging wave will unlock growth. The first wave of gen AI has driven productivity and efficiency in certain functions. The second wave, and future ones, will unlock growth, which is expected to be even larger in magnitude and impact for early adopters. This will involve leveraging AI to not only improve existing processes but also drive business expansion and innovation.

By deploying AI, distributors can optimize their operations, improve efficiency, and enable data-driven decision making. Companies that have already adopted these technologies are seeing measurable results in terms of improved revenues, faster growth, and stronger customer relationships.