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    AI IN BUSINESS

    Use cases, integration, risks, and value creation

    AI in business has moved from the lab to the field. The real question is no longer “should we get started?” It is: how do we industrialize what works?
     
    Since 2022, companies have launched hundreds of POCs. Some have demonstrated value. Most have remained in limbo: too costly to move into production, too risky to deploy without governance, too disconnected from real business processes. The result: consumed budgets, frustrated teams, and leadership starting to question the return on investment.
     

    This guide starts from a simple observation: AI creates value when it is integrated into a process, when its impact is measured, and when it is controlled. Not before. Technological enthusiasm is not enough. What matters is the ability to turn a prototype into a production tool that is used, adopted, and monitored over time.

    Artificial intelligence does not transform a company by itself. It is the industrialization of its use cases, governed, measured, and embedded in processes, that creates sustainable performance. 

    Enterprise AI now covers three very different realities. 

    • Predictive AI for forecasting, scoring, anomaly detection, is mature and widely deployed in operations and finance.
    • Generative AI for conversational assistants, RAG on document repositories, business copilots — has exploded since the emergence of large language models.
    • And AI agents, capable of chaining actions across multiple systems, are opening a third wave: more powerful, but also more demanding in terms of security and traceability.

    These three building blocks combine with one another. The companies moving fastest do not choose between them: they build a solid data foundation, first deploy use cases with rapid ROI, then progressively expand their scope as governance stabilizes.

    In practical terms, four value levers stand out.

    • Operational efficiency: reducing processing times, automating repetitive cognitive tasks, and lowering unit costs. 

    • Enhanced experience: better-served customers and better-equipped employees, 24/7.

    • Decision quality, through prediction and simulation.

    • New products and services made possible by personalization and content generation at scale.

    These levers are not theoretical. Among the projects supported by SBI: a food company eliminated its information silos through operational AI and data streaming. A major financial group unified its analytics governance and built a five-year roadmap. An industrial company reduced its MTTR (Mean Time To Repair) and automated visual quality control through deep learning. A multi-sector organization framed and prioritized more than 60 use cases to finally move from POCs to production.

    But industrializing AI cannot be improvised. It requires managing risks that are often underestimated: data quality and confidentiality, compliance with the European AI Act, cybersecurity (prompt injection, shadow AI, exfiltration via agents) and cost management. Because the usage-based pricing model of LLMs can quickly become unpredictable at scale. 

    AI FinOps is not optional; it is a prerequisite. 

    Finally, there can be no sustainable AI without controlled data. Platforms such as Snowflake, Microsoft Fabric, and Databricks now include native and governed AI capabilities. But they do not perform miracles on a poorly structured data foundation. The data platform remains the foundation on which everything else depends.

      What you will find in this guide:
    • The use cases that create value, process by process, and how to measure their ROI
    • The 4 models for integrating AI into the information system
    •  The risks to anticipate around data, compliance, security, and costs 
    • Examples of production projects and SBI’s method for industrializing AI

    AI IN BUSINESS: WHAT ARE WE TALKING ABOUT?

    Who is really orchestrating AI projects in companies in 2026?

    Artificial intelligence refers to systems capable of performing tasks that usually require human capabilities as perceiving, reasoning, deciding, generating. Long confined to specialized use cases, it became more widespread with machine learning, then deep learning, and since 2022, with generative AI and agents. Today, the challenge is no longer to “do AI,” but to transform business processes end to end, with impact measurement and control across data, security, compliance, and costs.

     

    “CLASSIC” PREDICTIVE AI VS GENERATIVE AI

    • Predictive AI: forecasting, scoring, detecting anomalies, optimizing (e.g. inventory, fraud, maintenance).
    • Generative AI: producing and interacting (text, code, summaries) and “conversing” with company knowledge through RAG.

     

    AGENTS: WHAT AGENTIC AI CHANGES

    (AND WHY IT IS MORE DEMANDING)

    Agents are systems capable of planning, calling tools, and chaining actions (e.g. creating a ticket, querying a CRM, triggering a workflow). They increase value, but also the requirements for security, traceability, access control, and observability.

    WHAT REALLY CHANGES FOR THE COMPANY

    Beyond the buzz, three disruptions explain why AI has become a strategic topic.

    1. Increased productivity: AI makes it possible to automate or accelerate repetitive cognitive tasks such as writing, summarizing, searching, support assistance, categorization, qualification, and more. Teams can focus more on decision-making, relationships, and arbitration.
    2. Access to knowledge in natural language: AI makes it possible to query documents, procedures, and data at scale. Knowledge becomes easier to act on, even for non-technical teams, provided that sources are governed and up to date.
    3. New products and services: Personalization, prediction, content generation, recommendations, conversational experiences… New use cases become economically viable and differentiating.

    AI IN BUSINESS: WHERE IS VALUE REALLY CREATED?

    Four ROI levers

    EXAMPLES OF AI USE CASES IN BUSINESS

    For an executive committee, the most effective starting point is to reason by process and by KPI (time, cost, quality, risk, satisfaction, revenue).

     

    Operations & supply chain

    • Demand forecasting: Reduce stockouts, overstock, and obsolescence.

    • Optimization : planning, routes, allocation: Improve service and costs. 

    • Predictive maintenance: Reduce MTTR, increase availability.

    Typical KPIs: service level, logistics cost, inventory level, downtime, MTTR.

     

    Customer service

    • Agent assistance (summaries, suggested replies, classification): Reduced processing time.

    • Selfcare (dynamic FAQ, RAG chatbot on the knowledge base): Reduced ticket volume.

     Typical KPIs: AHT, FCR, CSAT/NPS, cost per interaction.

     

    Finance

    • Anomaly detection (invoices, payments, entries): Reduced risk.

    • Control automation (summaries, reconciliations, variance justification)

    • Forecasting (cash flow, churn, revenue): Faster decisions .

     Typical KPIs: closing timelines, error rate, incidents, cash accuracy.

     

    Sales & marketing

    • CRM copilots: Meeting summary, contact enrichment, buyer intent, next best action, qualification.

    • Content generation in all formats, variations, translation, with brand control.

    • Conversational analytics: Questions and answers on dashboards. 

     Typical KPIs: sales time, conversion rate, CAC, pipeline velocity.

     

    IT & Knowledge management 

    •  Document assistant (RAG): Procedures, policies, offerings, runbooks. 

    • IT copilots : Diagnostic support, response generation. 

    Typical KPIs: resolution time, reduction in L1 tickets, compliance.

    EXAMPLES OF AI PROJECTS IN PRODUCTION

     

    Food industry - JAPFA: Real-time data and AI serving new food safety challengesAgroalimentaire modernisation data platform (cloud) et reporting décisionnel – SBI

    Real-time strategic decision-making through operational AI 

    Challenge: Reduce information latency in production and accelerate decision-making in response to market fluctuations.
    Impact: Elimination of silos, drastic reduction in latency, and maximum business agility.
    Approach: Operational AI, data streaming.

     

     

    Financial services - Major group: 5-year roadmap and Data & AI modernization

    Challenge: Unify a fragmented analytics function and evolve a heterogeneous infrastructure to scale.
    Impact: Strategic alignment, unified governance, and clear visibility on long-term ROI.
    Approach: Strategy, 5-year roadmap.

    Industry & logistics: Maintenance (deep learning) and predictive planning
    Transformation data logistique accélération time-to-insight et décisions plus rapides

    Challenge: Reduce MTTR, automate visual quality control, and improve the reliability of inventory forecasts.
    Impact: Reduced inventory errors, accelerated field diagnostics, and automation of repetitive tasks.
    Approach: Deep learning, forecasting.

    Multi-sector - AI governance: Framing and industrializing multiple use cases

    Challenge: Lack of process to prioritize, budget, and govern emerging AI projects.
    Impact: Rigorous prioritization of multiple use cases and transition from POCs to truly industrialized AI.
    Approach: AI governance, Target Operating Model (TOM).

    AI TOOLS USED BY BUSINESS TEAMS EVERY DAY

    Conversational assistants

    Integrated copilots (office suite, CRM, ITSM) or business assistants: writing, summarizing, Q&A, support assistance.

     

    RAG (Retrieval-Augmented Generation): Augmented search

    The model responds based on your documents and data, with citations/references.

     

    Content generation

    Text, images, code, presentations: draft production, formatting, translation, ideation.

     

    BI & conversational analytics

    Query indicators and dashboards in natural language, obtain insights and explanations of variations.

    THE TECHNICAL BUILDING BLOCKS “UNDER THE HOOD” (PLATFORM SIDE)

    LLMs & foundation models

    Proprietary models (GPT, Claude, Gemini) or open-weight models (Llama, Mistral).
     

    Agents & orchestration

    LLMs capable of planning, calling tools, and chaining actions.
     

    Vector DB & RAG pipelines

    Embeddings, indexing, semantic search.
     

    LLMOps & observability

    Versioning, testing/evaluation, monitoring quality, security, costs, guardrails.

    4 WAYS TO INTEGRATE AI INTO THE IS (OFTEN COMBINED)

    1. Overlay / Copilot

    2. Dedicated AI platform

    3. Business agents

    4. AI in the data platform

    OPERATIONAL RISKS TO ANTICIPATE

    Risks do not come only from technology: they appear as soon as AI touches sensitive data, critical processes, or decisions. Anticipating them as early as possible helps avoid ending up with POCs that “work” but are impossible to industrialize.

     

    DATA

    • Quality & freshness: Industrialized AI amplifies errors (incomplete data, inconsistencies, duplicates, obsolete documents).
    • Confidentiality: Risk of data leakage (prompts, logs, datasets, model outputs) and uncontrolled reuse.
    • Access rights: Ensure that AI does not give access to more than each person is authorized to see (especially in RAG).
    • GDPR: Legal basis, minimization, retention, traceability, rights management (access/deletion).


    LEGAL & COMPLIANCE 

    • Intellectual property: Rights over content, injected data, and outputs (contracts, clauses, licenses).
    • AI Act (EU) & compliance: Use case classification, documentation requirements, human oversight, risk management.
    • Liability: Decide who bears the risk in case of error (vendor, integrator, user, business team) and define guardrails.
    •  Supplier terms: Data location, subcontractors, auditability, SLAs, contractual security.


    SECURITY

    • Prompt injection: Manipulating the model to bypass rules, extract data, or trigger unwanted actions.
    • Exfiltration via agents: An agent connected to tools (email, CRM, tickets, drive) can become a leakage vector if its permissions are not strict. 
    • Shadow AI: Undeclared uses (external tools, personal accounts) lack of control and traceability. 

     

    ORGANIZATION

    • Adoption: Without a clear use case, training, and support, the tool remains a gadget.
    • Skills: Upskilling business teams, IT, and security (best practices, limitations, validation).
    • Trust: Perceived quality, explainability, error management, and validation procedures.
    • Change management: Governance, communication, usage rules, and integration into existing processes.

    FINANCIAL RISKS: THE COST TRAP (AND HOW TO AVOID IT)

    Financial risks are often underestimated in AI projects, especially with generative AI. The pay-as-you-go model (tokens, API calls, storage, vectorization, inference) can drive costs up very quickly when scaling, multiplying users, or letting agents run continuously.
     
    To avoid this pitfall, it is strongly recommended to implement AI FinOps from the start: define a target cost per use case, measure a baseline (cost per query, per document, per user) set caps through quotas and guardrails (context limits, caching, model selection) and track simple KPIs (monthly cost, cost per outcome, drift).
    Finally, the “build vs buy” decision must be made based on real numbers (TCO, security, maintenance, vendor dependency). The goal is not to have “the most powerful model,” but profitable, predictable, and governed AI.

    NO SUSTAINABLE AI WITHOUT A CONTROLLED DATA PLATFORM

    Governance, quality, unified architecture, cost control.

    AI in production is not “a model + an API”: it is an industrial capability that depends on your data platform. Without a robust foundation, AI projects become fragile: they consume a lot of energy for limited impact, and they expose the company to risks that are difficult to control (quality, compliance, security).

     

    GOVERNANCE AND ACCESS RIGHTS: THE CONDITION FOR TRUST

     AI (especially in RAG) must never answer with information that the user is not authorized to see. This requires consistent sharing rules, clear roles, and data/AI governance that traces who accesses what, why, and with what level of sensitivity.

     

    QUALITY AND FRESHNESS: AI AMPLIFIES DEFECTS

    A controlled data platform starts with reliable, documented, and maintained data. If sources are incomplete, contradictory, or obsolete, AI will “industrialize noise”: more outputs… but more errors, therefore less trust and less adoption.

     

    UNIFIED ARCHITECTURE: REDUCING COMPLEXITY (AND COPIES)

    Multiplying silos, extractions, and data copies increases risk (leaks, inconsistencies) and cost (storage, integrations, maintenance). Modern data platforms (Snowflake, Microsoft Fabric, Databricks) make it possible to bring data, governance, and AI closer together to limit duplication and accelerate scaling.

     

    COST CONTROL: AI FINOPS STARTS IN THE DATA PLATFORM

    LLM costs can be managed (tokens, models, quotas) but the overall cost is also decided upstream: ingestion, transformation, vectorization, refresh, storage, observability. A well-designed data platform makes it possible to measure and optimize cost per use case, and to avoid drift when usage explodes.


    In summary, a controlled data platform is the best accelerator for industrialization, and the first safeguard for AI in business.

    AI IS ALREADY IN YOUR DATA PLATFORMS

    Native and governed AI capabilities

    FAQ: AI IN BUSINESS

    WHAT IS AI IN BUSINESS?

     AI in business refers to technologies (predictive AI, generative AI, agents) that automate, assist, or augment business processes based on internal data and knowledge. 

    WHAT IS THE DIFFERENCE BETWEEN PREDICTIVE AI AND GENERATIVE AI?

     Predictive AI anticipates (forecasting, scoring, detection). Generative AI produces (text, code, summaries) and interacts in natural language with your content, often through RAG.

    WHAT ARE THE BEST AI USE CASES TO START WITH?

    The best AI use cases to start with are those with measurable ROI, available data, a stable process, and a business sponsor.

    HOW DO YOU MEASURE THE ROI OF AN AI PROJECT?

    Define 1 to 3 KPIs, measure a baseline, then track the impact in production.

    RAG: WHAT IS IT FOR AND WHEN SHOULD IT BE USED?

    RAG (Retrieval-Augmented Generation)  allows an LLM to respond based on your documents and data, with the context of your company (procedures, offerings, contracts, etc). It is used when the answer depends on specific documents (internal, up to date, numerous) and/or when you want an assistant that “knows” the documentation without retraining the model. RAG helps reduce invented answers (“hallucinations.”).

    AI AGENTS: WHEN ARE THEY RELEVANT?

    AI agents are relevant when actions need to be chained across several systems through a controlled workflow.

    WHAT ARE THE 4 WAYS TO INTEGRATE AI INTO THE IS?
    • Overlay/Copilot,
    • Dedicated AI platform,
    • Business agents,
    • or native AI in the data platform.
    WHAT ARE THE MAIN RISKS OF AI IN BUSINESS?
    • Data quality/leakage,
    • Compliance,
    • Cybersecurity,
    • Adoption,
    • Governance.
    WHY CAN COSTS EXPLODE IN GENERATIVE AI?

    Usage-based payment (tokens) becomes unpredictable at scale.

    WHAT IS AI FINOPS?

    AI FinOps is an AI cost management system: cost per use case, caps, monitoring, optimization.

    WHY IS A CONTROLLED DATA PLATFORM CRITICAL FOR AI?

    Without a controlled foundation, AI industrializes errors instead of creating value.

    HOW LONG DOES IT TAKE TO MOVE FROM POC TO PRODUCTION?

    A few weeks for a POC, then several weeks/months to industrialize.

    HOW DOES SBI SUPPORT AN EXECUTIVE COMMITTEE ON AI?

    We support your AI projects end to end: from identifying high-impact use cases to moving them into production, driving team adoption, and scaling them. Our approach combines strategic advisory, technology choices, risk management (costs, compliance, security) and industrialization through a governed Data + AI foundation, with training.