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AI Consulting Services | Bain & Company

Our AI consultants are helping clients strategically consider, build, and implement AI to improve efficiency, retention, and time-to-market.

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Full path: Home, Digital, AI, Insights, and Solutions, Artificial Intelligence Consulting

Home

…

Digital

AI, Insights, and Solutions

Artificial Intelligence Consulting

How we can help

Artificial Intelligence Consulting

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.

Artificial Intelligence Consulting

AI Consulting Services

Experience & Impact

Featured Alliances and Partnerships

Insights

Our AI Experts

For as long as artificial intelligence has helped businesses streamline how work gets done, we’ve been at the forefront—whether it’s
defining AI
frameworks for financial services clients, helping telcos establish
responsible AI strategy
, or embedding AI in our
own
digital tools
.

While fast-moving AI adopters can establish a competitive advantage, they first must overcome inherent challenges—from building the right tech stack and managing the safe use of data, to training employees and ensuring output quality and proper tracking.

We combine
business strategy and technical expertise
to help you rethink your goals with AI in mind, then build the most high-value use cases from proof of concept to production. Our integrated team of industry and AI consultants,
AI product managers and engineers
, and key partners work together to propose strategies based on value, feasibility, risk, and differentiation—then deliver the solution’s tech stack, operating model, and talent strategy. Crucially, we ready your organization for AI adoption by strengthening internal capabilities, training your team, and implementing change management to ensure a smooth transition.

With our fingers on the pulse of the evolving AI landscape, we understand the technology’s untapped potential and its risks—and how to manage both responsibly. Our technical expertise and experience collaborating closely with early movers can help you kickstart your journey and achieve sustained success.

AI Consulting Services

AI Consulting Services

Across industries, we’re helping clients strategically consider and implement use cases to improve efficiency, customer retention, and time-to-market.

Financial services

Customer service chatbots, wealth advisor intelligent assistants, and personalized marketing content generation

Telecom and media

Contact center automation/chatbots, intelligent assistants for network engineers, and media and gaming content generation

Retail

Contact center and chatbots, e-commerce content generation and analytics, and store ops intelligent assistants

Healthcare

Clinical development patient/investigator communication, compliance document generation, and sales rep and clinician intelligent assistants

Industrials and TCS

B2B sales rep intelligent assistants, customer service/support automation, product virtual assistant features, and R&D

Our AI Experience & Impact

The Coca-Cola Company

Carrefour

InsuranceCo

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:

25% potential boost in revenue

10x improvement in marketing performance

The Coca-Cola Company

Carrefour

InsuranceCo

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:

25% potential boost in revenue

10x improvement in marketing performance

Winning with AI: Explore the stories

Featured Alliances and Partnerships

Featured Alliances and Partnerships

Harness the power of generative AI to transform your business

Learn More

Build and deploy AI applications faster, more effectively, and at scale

Learn More

Accelerate your AI ambition. Achieve scalable, transformative results.

Learn more

Our AI Insights

The Executive's Guide to Agentic AI

Explore Bain’s Executive’s Guide to Agentic AI, with insights on how AI agents are transforming leadership, operations, and enterprise strategy.

Report

Digital

Proprietary Intelligence: How to Win with AI

Build advantage no competitor can copy.

Brief

Digital

The Era of Experimentation Is Over with Arvind Krishna, CEO of IBM

CEO Arvind Krishna joins Sarah and Andrew to discuss what the company has learned along the way—and how those lessons are shaping the IBM playbook.

Podcast

Telecommunications

AI in Telecom: The Opex Reckoning

Here's why AI investments often bloat costs—and how to avoid that.

Brief

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Winning with AI Podcast

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.

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SEP 15, 2026

The Era of Experimentation Is Over with Arvind Krishna, CEO of IBM

JUL 28, 2026

The Agentic Travel Agent with Glenn Fogel, CEO of Booking Holdings

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Is the Jobpocalypse Real? with Denis Machuel, CEO of The Adecco Group

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Our AI Experts

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Partner

Munich

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Partner

Denver

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Partner

Sydney

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Partner

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The Artificial Intelligence Questions Leaders Are Facing Today

Why are most companies adopting AI, but few seeing real financial returns?

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.

What is the difference between an AI pilot and AI at scale, and why does it matter?

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.

How should we get our workforce to actually adopt AI, not just have access to it?

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.

What is agentic AI, and should it change our AI strategy?

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.

Why does AI adoption raise our data security and governance risk, and how do we manage it?

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.

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</reference>

<statements>
1. By connecting generative creative assets with enterprise personalization algorithms, Coca-Cola reduced localized marketing production cycles by 40%, lowered creative production costs by roughly tenfold compared to traditional physical design iterations, achieved a 117% increase in click-through rates, and drove a 36% revenue uplift across targeted online channels
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.