AI Gold Rush – From Pilots and Experiments to Enterprise Size and Strategy
When it comes to AI, Moore’s law is really good. There is a huge demand for AI, and every enterprise adopts AI. Innovation can also help this need with new AI models, AI agents and new technologies. This creates a fundamental transformation for businesses, which are the stages of cool experimentation and presentation of pilots and AI, especially the generated AI is fading to a large extent. Businesses realize that AI needs to be embedded as part of their enterprise strategy to scale and create real business differences. In most boards, AI is a theme that leads to strategic innovation and budgeting.
Data: The first domino of AI strategy
The key consideration in any AI strategy should be data. Data is critical to AI models’ context, intelligence and domain and enterprise-specific importance. The AI model predicts the results based on the way the model is adjusted and the input presented to it. Both depend on the quality, diversity, proximity and structure of the data.
According to recent IDC forecasts, AI is expected to drive global economic growth by nearly $20 trillion by 2030, driven not only by models, but also by substantial investment in underlying data and infrastructure.
Training data with a narrow subset leads to biased models, outdated data leads to results that are irrelevant, while poor data leads to only poor AI results. Therefore, data is the first domino in the enterprise data strategy. Even with the best people and cutting-edge technology, if the data domino drops, the entire AI strategy will drop rapidly.
As Gartner’s 2024 report on top data and analytics trends, as the scale of AI expands, the successful leaders will be those who are Build trust in data and lead strategically.
Key strategic data decisions for your AI strategy
Here are 5 key things you and your business need to do when preparing data for your AI strategy:
1. Repeat your data pattern – Several companies will not reuse AI’s data management, data governance, and data storage and analysis landscape. Many data provide critical reports and analytics data is also crucial to AI. Therefore, it is important to start with data assets that already exist in the enterprise. Of course, this needs to be added with the correct data quality metrics.
Key questions to ask – What data do we have in the enterprise and what are its conditions?
2. Metadata and data lineage – For data that is already in place, metadata, i.e. data about data, may be equally critical to AI (if not more). For example, business terms marked as data can help determine the relevant context of the rag model. When a user requests to claim a claim status in an insurance company, all data attributes with claim status can be used as the context of the AI model response. Data lineages also help understand the process of data, thereby helping AI models identify trusted data sources.
Based on the recent ISASA blog, AI governance is crucial and requires the right metadata and data lineage to be extended.
Key questions to ask – Is our data correctly tagged with business and technical metadata? Do we collect data lineages to understand how the data process ends up?
3. Data Governance and Compliance – Ensure that your data is well managed and managed and applies any compliance and privacy regulations to your data. AI strategies should then inherit and extend these governance and regulations rather than starting from scratch. For example, if a customer wants to anonymize their data according to GDPR regulations, the AI model should be trained and operated on an anonymous dataset.
Key questions to ask – Do we have a data governance and compliance plan? If not, what key aspects do my AI strategy need to have?
4. Treat the master data as your AI quarterback – Key master data contains data about key entities in the enterprise and is applied as the basis for your AI strategy. For example, if there is a 360-degree view of the customer, then AI policies on any customer domain (such as churn forecasting) should be leveraged to avoid any missing or incomplete data. Of course, this can be used in conjunction with more information from a specific data source.
Key questions to ask – Can my key primary data domain be complete and connected to the rest of my data landscape?
5. Data and its values - Data should not be considered a cost center, but should be measured by its value to AI and business. In addition to AI, this requires data and CXO themes.
Key questions to ask – Do my board and CXO understand the value of data to the organization? If not, how do we make sure that this is understood, especially in the context of AI strategies in enterprises?
The model comes and goes, but the data continues.
As your AI strategy develops, new models and AI innovations will emerge. The pace of innovation in this area is incredible. But over time, AI models will be commoditized; the real difference in an enterprise is not the model you use, but how it trains, fine-tunes and processes it through data.
If you are developing an AI strategy, don’t start with the model. Start with the question: Do we have data support?
