数据战略一致性促进人工智能的成功

Data StrategyAlignment forGen AI Success A data strategy with robust data governance andalignment with business objectives is essential forstrong ROI from Gen AI projects Table of Contents 346877101110131015Executive SummaryEnthusiasm and Novelty Meet Business RealitySynergy Between Data Readiness and Gen AI ApplicationsThe 3 Pillars of Good Data Governance for Gen AI SuccessCase Study: Industrial Success Through Strategic AlignmentCore Principles of Gen AI IntegrationConclusion: Strong Use Cases and Reliable Data for Gen AI ROIThe Data Balancing ActEnsuring Scalable Gen AIÌË Data Availability×Ë Data QualityäË Data Integration Executive Summary Generative AI has caught the imagination of enterprises as arevolutionary technology capable of reshaping operations,optimizing workflows, and redefining customer experiences.Companies across industries are investing heavily in hopes ofachieving groundbreaking efficiency and unlocking newopportunities for innovation. Yet, as many organizations havelearned, the road to operationalizing Gen AI while realizing ameasurable ROI is often fraught with challenges. Many organizations rush into Gen AI adoption without a clearvision for how it aligns with broader business goals. Projects areundertaken with vague objectives, driven more by fear of missingout than by well-defined use cases. This lack of strategy leavesteams churning through resources only to deliver a proof ofconcept showcasing limited business value. Infatuated with AI’stheoretical potential but unsupported by tangible outcomes,organizations frequently abandon pilots, sowing frustration andskepticism. Crucially, for Gen AI projects to have alignment with businessgoals, they require a data foundation for a proper rollout. Withoutscalability and accuracy, Gen AI applications cannot solve theproblems identified by the business or create value. Business andtech leaders seem to know that. According to a survey conductedby Wakefield Research for SoftServe of 750 business and ITleaders at companies with $1 billion or more in annual revenue,98% of them believe a data-strategy update should be requiredbefore being able to gain the full advantages of initiatives like AI. Accordingly, without a data strategy geared toward providing thenecessary data governance for application expansions and reliableresults, Gen AI projects quickly turn into sunk capital. For organizations to succeed with Gen AI, a robust data foundationaligned with clear strategic objectives is not just helpful — it isessential. Read on to learn more about the core features of thisalignment, and how you can avoid pitfalls while making the mostout of your Gen AI investments. Enthusiasm and NoveltyMeet Business Reality According to a 2024 commissioned global study conducted by Forrester Consulting on behalf ofSoftServe, only 22% of organizations have achieved enterprise-wide Gen AI success. To grasp thedifficulties most companies face with Gen AI, imagine a company excitedly launching AI initiatives,buoyed by promises of automation, enhanced productivity, and groundbreaking customerinteractions. Early pilot projects, designed to test AI capabilities, often generate internal excitement.Sleek demonstrations of machine learning models provide highly controlled and successfuloutcomes, further fueling optimism. Yet, as the novelty fades, businesses often encounter starkrealities that hinder progress. Our research with Wakefield shows that 64% of business leaders admit their companies frequentlydeploy AI solutions without first establishing a viable business use case to justify their efforts. Thetransition from successful prototypes to real-world applications magnifies the challenges. ControlledAI demos crumble under the complexities of inconsistent and fragmented enterprise data. Data quality issues emerge as the AI encounters messy, incomplete, or inconsistent informationfrom various enterprise systems. Integration with legacy systems proves far more complex thananticipated, requiring extensive custom development. What seemed like a straightforwardimplementation transforms into a labyrinth of technical debt, with each solution creating newcomplications. Gen AI, lauded for its ability to aid workflows, thus frequently produces results requiring extensivehuman oversight. Instead of streamlining operations, Gen AI systems often introduce new burdensas staff make significant efforts to verify outputs, fine-tune generated insights and monitor forhallucinations or factual inaccuracies. What can prevent these dismal results? Data readiness — and deep alignment between datastrategy and business objectives. Synergy Between DataReadiness and Gen AIApplications The Data Balancing Act A strong Gen AI rollout depends on two things: A solid data foundation(the supply side) Clear, business-aligned AI use cases(the demand side) An imbalance between these two can lead to costly inefficiencies( ' Over-investing in data without business use cases results in u