Post by InnoBiz Global Consulting (iBGC)

𝗪𝗵𝘆 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗶𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗲𝘁𝗿𝗶𝗰𝘀, 𝗻𝗼𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘀𝗰𝗼𝗿𝗲𝘀 If your AI success metric is accuracy, you are optimizing for research, not for business. →𝗔𝗰𝗿𝗼𝘀𝘀 𝗴𝗹𝗼𝗯𝗮𝗹 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗮𝗻𝗱 𝗚𝗖𝗖 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀, AI investments are growing at 𝗼𝘃𝗲𝗿 𝟮𝟬 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆, yet only a fraction deliver sustainable ROI. The gap lies in how success is defined. 𝗔𝗜 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗿𝗲𝗳𝗿𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵. • 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀 𝗙𝗶𝗿𝘀𝘁. Whether it is reducing churn, improving supply chain efficiency, or accelerating underwriting, define the financial and operational target upfront. • 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 𝗮𝗻 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿. Every model output must connect to a specific business decision or action loop. Without this, even a highly accurate model becomes strategically irrelevant. • 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀. Data shows that in large enterprises, up to 50 percent of AI tools are underutilized due to poor integration into workflows. Adoption is the real multiplier of AI value. • 𝗧𝗵𝗲𝗻 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗧𝗼𝘁𝗮𝗹 𝗩𝗮𝗹𝘂𝗲 𝗥𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Organizations that align AI with business KPIs have achieved 2 to 3 times higher ROI compared to model driven approaches. →The insight is clear. 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗶𝘀 𝗮 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲. 𝗩𝗮𝗹𝘂𝗲 𝗿𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗮𝗻𝗱𝗮𝘁𝗲. → 𝗜𝗻𝗻𝗼𝗕𝗶𝘇 𝗚𝗹𝗼𝗯𝗮𝗹 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀 with enterprises to design AI value frameworks, KPI linked architectures, and execution models that translate 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗜𝗻𝗱𝗶𝗮, 𝗚𝗖𝗖, 𝗮𝗻𝗱 𝗴𝗹𝗼𝗯𝗮𝗹 𝗺𝗮𝗿𝗸𝗲𝘁𝘀. 𝗢𝘂𝗿 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴𝘀: AI, BI and Analytics Products AI, Product, IT and Business Consulting Follow InnoBiz Global Consulting at InnoBizGlobalConsulting or #iBGCGroup #AITransformation #EnterpriseAI #AIAdoption #EnterpriseTransformation