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It’s time to build your business for an AI world. We help you discover AI’s potential at the intersection of strategy and technology, and embed AI in all you do. Let’s turn AI’s promise into performance.
Across industries, we’re helping clients strategically consider and implement use cases to improve efficiency, customer retention, and time-to-market.
Customer service chatbots, wealth advisor intelligent assistants, and personalized marketing content generation
Contact center automation/chatbots, intelligent assistants for network engineers, and media and gaming content generation
Contact center and chatbots, e-commerce content generation and analytics, and store ops intelligent assistants
Clinical development patient/investigator communication, compliance document generation, and sales rep and clinician intelligent assistants
B2B sales rep intelligent assistants, customer service/support automation, product virtual assistant features, and R&D
A new limited series podcast on how the world's top leaders turn AI ambition into advantage, hosted by experts Andrew Ng and Sarah Elk.
Check out the Podcast
The first company to engage with our OpenAI alliance, The Coca-Cola Company used a buzzworthy campaign to spark a two-way dialogue with consumers. “Create Real Magic” combined ChatGPT and DALL-E into a first-of-its kind platform that offered a canvas for AI-fueled experimentation.
Digital creatives were invited to tap into iconic assets from the The Coca-Cola Company's archives to generate original artwork to be displayed in New York’s Time Square and London’s Piccadilly Circus. By blending customer activation, stakeholder engagement, and public relations, the campaign elevated the company’s world-class brands, marketing, and consumer experiences—and kickstarted its journey with generative AI.
French multinational retailer Carrefour has improved its customer and employee experience with three innovative tech solutions based on ChatGPT technology. Its natural-language, GPT-4-enabled chatbot, Hopla, drives a seamless e-commerce experience on carrefour.fr. Connected to the site’s search engine, the advice robot retrieves customer-specific preferences, offering them smart guidance on food selection based on budget, quantities, dietary restrictions, and more.
In its journey toward becoming a “Digital Retail Company,” Carrefour is also exploring other customer-facing generative AI use cases as well as internal applications. It’s using the technology to enrich more than 2,000 product sheets and to streamline everyday tasks in internal purchasing processes.
When a leading insurance company recognized that consumer needs and buying habits were continually shifting, it began to strategically re-think how analytics could reshape its customer experience. Harnessing the power of artificial intelligence, we helped the company apply machine-learning algorithms against its data to continually refine predictions about which product a customer was likely to buy next. To support that analysis, we created a comprehensive data lake, integrating 20 databases into a system that contained a rich 10-year history of client and external data. After seeing promising results, InsuranceCo locked in the internal capabilities needed to scale its new analytics expertise across the organization.
Results:
The first company to engage with our OpenAI alliance, The Coca-Cola Company used a buzzworthy campaign to spark a two-way dialogue with consumers. “Create Real Magic” combined ChatGPT and DALL-E into a first-of-its kind platform that offered a canvas for AI-fueled experimentation.
Digital creatives were invited to tap into iconic assets from the The Coca-Cola Company's archives to generate original artwork to be displayed in New York’s Time Square and London’s Piccadilly Circus. By blending customer activation, stakeholder engagement, and public relations, the campaign elevated the company’s world-class brands, marketing, and consumer experiences—and kickstarted its journey with generative AI.
French multinational retailer Carrefour has improved its customer and employee experience with three innovative tech solutions based on ChatGPT technology. Its natural-language, GPT-4-enabled chatbot, Hopla, drives a seamless e-commerce experience on carrefour.fr. Connected to the site’s search engine, the advice robot retrieves customer-specific preferences, offering them smart guidance on food selection based on budget, quantities, dietary restrictions, and more.
In its journey toward becoming a “Digital Retail Company,” Carrefour is also exploring other customer-facing generative AI use cases as well as internal applications. It’s using the technology to enrich more than 2,000 product sheets and to streamline everyday tasks in internal purchasing processes.
When a leading insurance company recognized that consumer needs and buying habits were continually shifting, it began to strategically re-think how analytics could reshape its customer experience. Harnessing the power of artificial intelligence, we helped the company apply machine-learning algorithms against its data to continually refine predictions about which product a customer was likely to buy next. To support that analysis, we created a comprehensive data lake, integrating 20 databases into a system that contained a rich 10-year history of client and external data. After seeing promising results, InsuranceCo locked in the internal capabilities needed to scale its new analytics expertise across the organization.
Results:
Harness the power of generative AI to transform your business
Build and deploy AI applications faster, more effectively, and at scale
Most companies are adopting AI, but few see real financial returns because AI doesn’t create value through technology deployment alone—real impact requires process redesign.
Bain’s third quarter 2025 Generative AI Survey found that of the 59% of companies meaningfully adopting generative AI, the technology met or exceeded expectations in about 80% of cases. However, only 23% could tie their initiatives to new revenue or lower costs. In other words, the technology works, but the value is falling short.
Emerging AI leaders are focusing on fewer, high-value domains and redesigning processes with AI at the core. Lasting transformation requires top-down leadership, smart operating models, and a sustained commitment to change. Companies that take this approach are the ones seeing measurable revenue increases or cost decreases.
The difference between an AI pilot and AI at scale is that a pilot proves a use case works, while scaling changes how the business works—and scaling is where the financial gains appear.
Bain research shows AI leaders have used AI to improve EBITDA by 10% to 25%, while laggards fall further behind. Most pilots don’t pay off because of a specific failure: Teams using AI assistants see 10% to 15% productivity gains but rarely redirect the saved time to higher-value work, so the gains evaporate.
The fix is process change, not more pilots. Leaders are redesigning the surrounding workflow so it captures reclaimed time, and, with autonomous agents, reinventing entire workflows rather than speeding up existing ones. Companies that weave AI into core workflows and scale across use cases capture real returns; those that keep piloting risk falling behind.
Getting a workforce to adopt AI, not just have access to it, requires modernizing workflows and the workforce together. Access isn’t the constraint—companies have spent billions on automation and AI, yet most settle for micro-productivity gains rather than enterprise-wide value.
According to Bain’s third quarter 2025 Generative AI Survey, 74% of companies call AI a top-three strategic priority, yet 39% report a lack of in-house expertise or resources as a concern.
The companies pulling ahead in translating that priority into adoption are linking workflow and workforce deliberately. They redesign the workflows that matter most rather than automating existing “workflow debt.” They team technology, operations, finance, and HR cross-functionally. And they invest in reskilling, redeployment, and clear communication of intent to encourage employee trust and adoption.
Companies that take this human-centric approach to workforce productivity deliver more than two times the total shareholder returns of their peers.
Agentic AI refers to AI systems that pursue goals by planning steps, using tools and systems, and executing multistep workflows with minimal human input. It should change a company’s AI strategy because the opportunity shifts from assisting with discrete tasks to redesigning how work gets done end-to-end.
Two mistakes keep companies stuck in pilot mode: treating agents like souped-up chatbots on standalone platforms that can’t coordinate or hand off work, and layering agents onto broken or legacy workflows. High performers don’t bolt agents on; they redesign how decisions, authority, and execution work at the source.
The path to agentic is phased—build the underlying infrastructure first, then the orchestration layer that lets agents work together, then scale across the enterprise. Companies that re-architect around agents gain a durable engine for intelligence, agility, and growth.
AI adoption raises data security and governance risk because fragmented, siloed systems can lead to inconsistent oversight and higher regulatory exposure. Agentic AI compounds the risk: Without a unified agent registry, centralized token management, and consistent schema and data contract governance, every new agent can add integration debt and erode trust.
A Bain survey shows data security and privacy concerns have grown since 2024, especially among companies that have moved AI from pilots into production. AI can’t scale without the trust of customers, employees, and regulators.
Leaders will build governance into the infrastructure of AI systems rather than bolt it on. They start with centralized policy enforcement and compliance controls, as well as a security baseline that includes runtime guardrails, identity management for nonhuman principals, and prompt-level protections. Companies that treat governance as a prerequisite, rather than a compliance afterthought, will be ready to scale AI responsibly as adoption grows.
