The Trends to Drive 2026: GenAI, Agents, Personalization

Q: How is Artefact positioned in the global data and AI consulting ecosystem? What is Mexico's role within your strategy?
A: We are a global specialist in data and AI, operating as a pure-player firm with a singular focus on these areas . Our differentiation stems from the depth of our technical and specific industry’s expertise and the breadth of our international presence, with 23 offices and over 1,500 professionals worldwide. We provide full-spectrum services from strategic advisory to AI factory implementation, supported by a research center in France and Artefact School of Data, which promotes data literacy at scale. This global structure allows us to execute complex, multinational initiatives while maintaining technical and ethical rigor.

In Latin America, our strategic growth began with Brazil and has extended to Colombia, Chile, Peru and now Mexico, our highest priority market in the region. The acquisition of BrainFood in Chile added over 60 experts to our regional capabilities, and in Mexico, we focus on building multidisciplinary, locally fluent teams that understand both business imperatives and technical deployment. We are addressing Mexico’s unique challenges in data maturity, infrastructure, and compliance by combining regional depth with a tailored, market-specific approach. This positions us as a key player in Mexico’s data and AI transformation journey.

Q: How does Artefact's "AI is About People" philosophy differentiate you from other data and AI consultants?
A: "AI is About People" is anchored in the belief that data must serve human and organizational advancement. We leverage data not only to improve decision making but also to optimize internal processes in a way that empowers employees and drives measurable business outcomes. Rather than deploying AI for its own sake, we focus on impact, whether in cost reduction, productivity gains, or revenue enhancement. 

Q: What specific challenges do Mexican companies face when adopting data-driven transformation strategies, and how does Artefact address these challenges?
A: Mexican organizations face two primary challenges: strategic alignment and talent scarcity. Many companies still treat data as a technical silo rather than a strategic asset, which leads to insufficient executive sponsorship and limited adoption. This is particularly evident when operational teams, despite having access to robust tools, do not utilize them. To address this, we embed adoption programs in every engagement, including training for business and technical users, and leverage our Artefact School of Data to instill data fluency and accelerate cultural transformation.

The second challenge is the lack of specialized talent capable of operationalizing advanced data products. High-caliber engineers, data scientists, and analysts remain in short supply. We mitigate this gap by deploying expert project teams, helping clients build internal capabilities, and supporting the recruitment and upskilling of technical staff. 

Q: How do you adapt your value proposition according to the specific technical and regulatory needs of each different sector?
A: Each of our projects is fully customized and led by industry-specific experts who understand the unique dynamics, compliance frameworks, and operational challenges of their sectors. We do not offer standardized products; instead, we design tailored solutions aligned with the client's objectives, industry constraints, and regulatory requirements.

We are also technology-agnostic, with teams trained across platforms like AWS, Google Cloud, and Microsoft Azure. We integrate governance and compliance considerations from the outset. In highly regulated environments such as banking or healthcare, this ensures solutions are secure, scalable, and audit-ready by design. This allows us to deliver precision-engineered systems that reflect both technological and sector-specific realities.

Q: How does Artefact articulate the integration between consulting, technology, and operational execution to generate tangible value across the entire business value chain?
A: We approach value creation across three integrated fronts. First, our consulting teams define the strategic roadmap by aligning AI and data initiatives with concrete business priorities. Second, our technical experts design and deploy the appropriate infrastructure, tools, and models based on industry standards and regulatory constraints. Third, in the operational phase, we implement, optimize, and institutionalize these solutions through change management and KPI-driven monitoring. When needed, we offer a hypercare period to ensure post-deployment stability and adoption. 

Q: How do you ensure data quality and governance in complex corporate environments, especially in regulated industries like finance or healthcare in Mexico?
A: Our governance approach is built around end-to-end data integrity. We implement quality control mechanisms that detect outliers, monitor value ranges, and validate historical consistency. This is essential for decision-making and model accuracy, especially in regulated industries where compliance is non-negotiable.

We also define clear ownership through domain-specific governance roles, ensuring data lineage, usage policies, and accountability are well established. Since we work mostly  with client data, we often initiate remediation programs to improve existing data assets, ensuring the inputs into AI models are accurate and reliable. 

Q: What are the three most disruptive trends in the corporate use of AI that business leaders in Mexico should have on their radar?
A: First, Generative AI is automating content creation and customer interactions at scale. It enables intelligent chatbots that can learn and adapt, significantly improving functions like service, internal knowledge management, and marketing efficiency.

Second, hyper-personalization powered by Customer Data Platforms (CDPs) is transforming how companies engage with users. Retail, banking, and telecoms are leveraging AI to deliver dynamic pricing, targeted offers, and real-time risk assessments.

Third, AI agents are extending automation beyond traditional robotic process automation (RPA). These agents can execute entire workflows independently, such as claim processing in banking. Combined with low-code and no-code environments, this democratizes access to AI, allowing smaller firms to innovate with limited technical resources.

Q: How would you evaluate the adoption of Generative AI in Latin America in terms of maturity and strategic use?
A: Generative AI adoption in Latin America is progressing rapidly, especially in automating repetitive tasks and enhancing content creation. However, compared to the European Union or Asia, the region is still evolving in terms of large-scale strategic integration. Regulatory frameworks, digital infrastructure, and enterprise readiness in those regions often accelerate deployment. That said, the enthusiasm in Latin America is significant, and we are seeing meaningful pilot programs that are poised to scale, particularly in customer-facing sectors.

Q: What are Artefact's strategic priorities within the Latin American market for late 2025 and early 2026?
A: Our strategic roadmap for Latin America focuses on regional consolidation, market penetration, and client expansion. We are replicating our successful operational model from Brazil, building a unified regional structure across key business functions. In Mexico, we are targeting growth in priority sectors such as telecommunications, food and beverage, energy, banking, and healthcare.

We are also launching the Data Circle in Mexico, a professional community designed to foster collaboration and knowledge exchange among data leaders. Another key priority is expanding our local client portfolio. While global clients remain important, growing our Mexican client base is essential for long-term regional success.

Q: What do you consider to be the most valuable principle to follow for achieving sustainable and business-aligned change?
A: All data and AI initiatives should be anchored in clearly defined business objectives. Every deployment should be tied to a measurable outcome, whether it is revenue growth, market share, or operational efficiency. If the value is not tangible, adoption will falter.

Equally critical is fostering a data-driven culture. This involves empowering teams, managing change proactively, and ensuring that cross-functional collaboration supports solution adoption. Technical sophistication alone is insufficient, as success depends on cultural alignment and strategic focus.