𝗗𝗮𝘁𝗮, 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜 𝗲𝗳𝗳𝗼𝗿𝘁𝘀 𝗰𝗮𝗻 𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲 𝘁𝗼 𝗿𝗲𝗮𝗹𝗶𝘀𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲 𝗯𝘂𝘁 𝘄𝗵𝘆?

🎯𝗠𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗴𝗼𝗮𝗹𝘀 𝗮𝗻𝗱 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘀𝘂𝗽𝗽𝗼𝗿𝘁. Many projects are initiated without a clear understanding of how they will support the organization’s strategic objectives. Without alignment and leadership support, even technically successful projects may fail to drive meaningful business outcomes.

🖥️𝗢𝘃𝗲𝗿-𝗲𝗺𝗽𝗵𝗮𝘀𝗶𝘀 𝗼𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, rather than delivering value to internal business customers, quickly and iteratively.

⚙️𝗟𝗮𝗰𝗸 𝗼𝗳 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Everything we do is built by people for people. Inevitably these efforts cause and/or respond to change. Change management is necessary to minimise the change friction and ensure adoption. You need to have good understanding of data maturity and ensure that elements of a capability are introduced at the right time.

✅𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲𝘀. Poor data quality, including inaccuracies, inconsistencies, and incomplete datasets, can lead to unreliable insights and predictions, undermining the effectiveness of analytics and AI initiatives. Data quality commitment is very much reliant on change management and having an engaged set of stakeholders.

📈𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀. Transitioning from pilot projects to full-scale deployment can be difficult. If solutions aren’t built with scalability in mind, they may struggle to perform effectively across the entire organization, diminishing their overall impact. Again, change management and managing expectations are important related elements.

What elements do you think are crippling the delivery of business value?

originally posted LinkedIn.