Amplifying sales growth in emerging markets has historically meant expanding a company’s geographic reach. Yet more and more brands and SKUs are competing for limited shelf space today, e-commerce has entered the fray, and, for many brands, the reach of distributed channel models in emerging markets is plateauing. Meanwhile, the availability and quality of talent are under significant pressure. As a result, sales organizations must extract greater value from existing customers through improved effectiveness and efficiency while mandating continued improvement in frontline and mid-management capabilities.
Many businesses have already responded by building strong digital and data foundations, with good results. But as AI capabilities become increasingly advanced and accessible, the upside potential of deploying AI in these complex, distributed channel models is immense. In fact, it’s already allowing first-movers to leapfrog the competition, driving potential sales increases of 15% to 20%. (See the slideshow.)
A Transformational Change
Let’s imagine the life of a salesperson tasked with visiting 20 to 25 different small stores each day on behalf of a large consumer packaged goods (CPG) company. Traditionally, they would manually determine which outlets to prioritize, the sequence of visits, and the assortment to promote. While they’ve probably had access to digital tools and basic analytics, they have likely faced challenges in leveraging them due to a lack of the necessary skills and the quality of the tools’ recommendations—given that the latter are based on relatively limited, static datasets.
Now let’s reimagine their day with an AI-powered sales companion accessible on a smartphone. The companion uses text and voice interfaces in the local vernacular to provide dynamic route planning. It offers real-time synthesis and analysis of conversations with retailers, creating highly customized recommendations for each store. It even provides performance management and training. The companion does this by leveraging both structured data, such as billing and loyalty data, and unstructured data, such as store images and planograms, handwritten notes, and sales conversations.
At the same time, GenAI-enabled virtual assistants can manage routine back-end activities, and a 24/7 digital agent can serve as the first point of contact for retailers—resolving queries, capturing orders, and issuing proactive payment reminders. This redistribution of tasks allows the salesperson to concentrate on engagement with higher-value channel partners.
The salesperson’s effectiveness and efficiency can climb dramatically as a result. For example, one mid-size homecare brand in an emerging market realized an increase in sales of 11% within a month by deploying an AI companion that makes order recommendations and sends recovery reminders for lost sales. And a multinational CPG player experienced a 25% increase in customer-facing time and 8% higher product lines sold per sales call after deploying a companion with dynamic route optimization and the ability to nudge its salespeople with personalized incentives and reminders.
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AI-Powered Use Cases
AI could support distributed channel models in many ways. While most businesses are not yet implementing AI in their go-to-market approach for emerging markets, tools such as AI companions, digital sales agents, and many others hold tremendous promise.
For example, AI can enhance performance management by creating personalized targets, gamified incentives, and customized training journeys for individual salespeople. It can act as a personal sales analyst, bringing up potential issues and breaking down performance drivers. And it can deliver nudges and alerts to salespeople to highlight gaps in execution, changes in price, and possible performance issues. If there seems to be a slowdown in daily sales, for example, AI can send a nudge offering an incentive if a specific target is reached.
AI also has the potential to rapidly hyper-customize sales plans for individual stores based on factors such as their square footage, location, and finances. And when sales don’t go as planned, it can perform a focused root-cause analysis to support smarter, faster responses.
How to Get the Most from AI
While recent advances have reduced the complexity of AI, making it far more accessible, companies can face challenges in realizing the technology’s full potential. We offer five success factors for getting the greatest value from AI.
- Create a bold vision that fully reimagines outcomes and drives workflow redesign.
- Choose the right high-value use cases to prioritize—and then measure the outcomes and scale the winners.
- Build a strong integrated technology stack of data, predictive AI, GenAI, and AI agents.
- Focus on effective change management, allowing AI to become a part of processes, rather than an add-on.
- Manage AI risks by putting up strong guardrails around accuracy, privacy, and security, while preparing for the future through adaptive governance for agentic AI.
First Steps
As AI becomes increasingly developed and accessible, companies operating in emerging markets should begin introducing it into their distributed channel models to support channel partners in their day-to-day work.
The results? More direct retail coverage, greater store conversion, higher-value products sold, and an enhanced experience for salespeople—all leading to potential sales increases of up to 20%.
The authors would like to thank the following for their support in the writing of this article: Abhinav Punia, Ankit Malpani, Bharat Mimani, Harshul Jain, Ishang Jawa, Mahima Dighraskar, Namit Puri, Nipun Kalra, Rachit Mathur, Rajat Mathur, Ritesh Ritolia, Sankar Natarajan, and Varun Boppana.
