AI Discovery Is Redefining Brand Identity: BCG

The Boston Consulting Group (BCG) is a global strategic consulting firm that advises companies and governments on transformation, growth, and competitiveness, integrating strategy, technology, and data to drive business impact.

Q: What role does Boston Consulting Group (BCG) play in redefining business competitiveness in Mexico and Latin America?

A: We help organizations translate strategic clarity into execution powered by applied AI. In Latin America, competitiveness is increasingly defined by the ability to navigate volatility, digitize operations, and unlock growth through technology. 

Our core value lies in the intersection of trust, measurable impact, and transformation capability. Clients rely on us because we have demonstrated over time that we can generate quantifiable business results, not just strategic recommendations. In an environment where AI adoption is complex and fragmented, our role is to guide organizations through prioritization, use-case selection, and scaling. 

Q: What kind of services are the most demanded of your clients in Latin America today, and how has BCG responded to expectations?

A: Demand varies significantly by sector, reflecting different macroeconomic pressures such as tariffs, inflation, and supply chain volatility. For example, some industries require support in building resilience against trade uncertainty, while consumer-facing businesses focus on pricing, promotions, and cost optimization. However, across all sectors, AI has become the dominant theme. Whether it is manufacturing optimization, customer targeting, or procurement efficiency, clients are asking how AI can redefine their operating models. 

Q: How is BCG X structured and what tangible benefits do your clients see by working with it?

A: BCG X brings together a distinct set of capabilities that complement traditional consulting. It includes more than 3,000 data scientists, along with data engineers, software engineers, product managers, and experience designers. The model integrates these profiles with strategic consultants to build and deploy digital and AI-native solutions at scale. The key value lies in this combination: strategic insight ensures relevance and prioritization, while technical talent ensures execution and scalability. In practice, clients benefit from faster development of AI products that are grounded in business value from the outset.

Q: The use of GenAI in purchasing decisions grew by 35% in less than a year and is already positioned as one of the most influential touchpoints. How does this change the traditional architecture of the customer journey?

A: About 60% of consumers now use AI during shopping journeys, and those influenced by GenAI recommendations tend to show higher conversion rates and higher average spend once they reach a brand’s website. However, this introduces a structural shift: brands are increasingly mediated by large language models (LLMs), which determine visibility and ranking. Our research shows that different LLMs provide different recommendations, agreeing on the “best brand” only 23% of the time, which highlights fragmentation in the discovery ecosystem.

The primary risk for brands is invisibility. If a company is not consistently recommended by these systems, it risks losing relevance in consumer decision-making flows. Over time, this can lead to gradual erosion of market presence. A secondary risk is commoditization: as consumers rely on intermediaries, brand differentiation may weaken unless companies actively shape how they are represented in these systems. 

Q: Consumers perceive GenAI as more objective, personalized, and efficient than traditional channels. How should companies rethink their marketing and positioning strategies?

A: Companies need to evolve marketing from a channel-centric model to an experimentation and orchestration system. One important capability is the use of synthetic panels, which allow organizations to simulate consumer responses and test creative or product concepts before market launch. This significantly improves speed and decision quality in marketing execution.

In parallel, organizations must develop capabilities in Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), meaning they must understand how LLMs source and prioritize information, and then strategically influence those inputs. At the same time, marketing must become fully omnichannel and dynamically optimized across traditional media, digital platforms, and social commerce. Finally, brand-building cannot be neglected; short-term performance optimization must be balanced with long-term brand equity development to avoid erosion of differentiation.

Q: What impact will the evolution toward “conversational commerce” models have on the relationship between brands, retailers, and platforms?

A: Conversational commerce will expand significantly, particularly through voice and guided interaction models. This is especially relevant in complex categories such as automotive, insurance, and financial services, where consumers require advice before making decisions. In these contexts, conversational systems reduce friction by providing structured guidance and personalized recommendations.

We are also seeing early examples in retail, where conversational agents proactively trigger purchases based on historical behavior and CRM data. When combined with recommendation engines and customer analytics, this creates a powerful end-to-end purchasing system. The open question is whether brands will maintain their own conversational interfaces or whether consumer-owned agents, such as those embedded in general-purpose AI platforms, will become the primary interface for commerce.

Q: In a context where consumers increasingly rely on automated systems to make decisions, what regulatory, ethical, and governance implications should companies prioritize?

A: Organizations must adopt responsible AI frameworks that define how decisions are delegated to automated systems. There is a spectrum between human-led workflows supported by AI and AI-led workflows supervised by humans, and each stage carries different levels of risk. Companies must carefully determine where automation is appropriate and ensure that critical decisions remain under human oversight.

This requires strong governance structures, including guardrails, monitoring systems, and clear decision rights for AI systems. It is also essential to implement safeguards against bias, discrimination, and unintended outcomes. Ultimately, responsible deployment is not optional; it is a prerequisite for scaling AI safely across the organization.

Q: Which verticals or industries in the region are best positioned to capture value from these transformations, and which face the greatest structural gaps?

A: In principle, all industries can capture value from AI-driven transformation, but outcomes depend heavily on their starting technological maturity. Organizations with modern, flexible, and API-driven architectures are better positioned to scale use cases quickly and realize faster time to value.

By contrast, companies operating with legacy systems or fragmented data infrastructures face longer transformation cycles. The main constraint is not strategic intent but technological readiness, particularly the ability to access real-time data and deploy new capabilities without major structural overhauls.

Q: How can companies overcome these challenges, and how does BCG help them?

A: The key is to define a clear target architecture that integrates data, analytics, and applications into a unified platform. At BCG, we use structured frameworks to help organizations design this future-state architecture and plan the transition from legacy systems.

Companies must also make deliberate decisions regarding build versus buy strategies for core components. Once the target architecture is defined, disciplined execution becomes critical, ensuring that systems evolve progressively toward a more modular and scalable digital foundation.

Q: What are BCG’s expansion or investment priorities in Latin America, particularly in capabilities related to AI and customer experience?

A: We are significantly expanding our capabilities in Mexico and across Latin America, particularly in data science, AI, and advanced customer experience transformation. This includes strengthening our local talent base and scaling specialized capabilities such as platform engineering and advanced analytics. Within the region, we are also developing dedicated teams focused on technology architecture transformation to support end-to-end modernization journeys.

Our priority is sustained growth in these capabilities, supported by continuous hiring and capability development.