Increasingly, enterprises are jumping on the AI "bandwagon" to improve their operational efficiency and overall productivity. The proliferation of AI-powered tools, along with massive data volumes, is driving this level of business transformation. Powered by AI technology, copilots are leveraging data and natural language to perform tasks previously performed by human resources.
That said, to be effective, AI-enabled copilots need access to real-time data across business functions and systems. Fragmented data is responsible for many pitfalls including:
- Functional silos (or islands) of AI-powered capabilities
- Data duplication, leading to duplicated efforts and waste
- Missed opportunities due to a lack of cross-functional insights
Gartner predicts that by 2025, over 50% of enterprise data will be generated outside the traditional data centre. As more enterprises work with distributed data spread across multiple locations, they need a unified data integration plan.
Here’s how siloed copilots can seriously hamper business transformation and optimization.
What are siloed copilots and how can they impact businesses?
Siloed copilots are essentially AI-powered standalone tools designed for a specific use case (for example, customer assistance) in an individual department or business function. For example, a ChatGPT-enabled tool can create an appealing sales pitch or email marketing. However, lack of integration with third-party sales/ marketing tools can largely limit its effectiveness.
Similarly, a siloed copilot can optimize a particular task or activity in sales, product development, or customer service. However, due to their siloed implementation, copilots cannot communicate or share data external to their function. This level of data fragmentation can lead to business inefficiency and loss of productivity.
Here are some areas where siloed copilots can make a negative impact:
- Decision-making
A unified AI approach can boost decision-making skills as cross-functional teams now have access to real-time data (or a “single source of truth”). Siloed copilots work on different datasets, thus providing differing insights, which can impact decision-making.
- Time and resource wastage
Depending on the business use case, individual teams invest their time and money in AI tools and models. However, siloed tools that don't communicate with each other can waste both time and resources. Often, this leads to individual teams duplicating the same work, thus causing inefficiencies and wasted resources.
- Unnecessary costs
Without a unified approach, enterprise departments or functions invest in their own AI tool to develop their capabilities. With overlapped AI capabilities, enterprises often spend their IT budget on the same technology – instead of allocating this budget to other tools or resources. Further, enterprises also run the expenses of running and managing multiple AI systems for the same output.
- Missed business opportunities
Siloed AI copilots restrict real-time data sharing, which is critical for cross-functional insights. For instance, the sales team may miss out on a cross-sell or upselling opportunity due to delayed insights. In the long run, enterprises are unable to unlock their AI capabilities for business growth and innovation.
Overcoming challenges with unified data integration
With a unified approach, enterprises can overcome the challenges of siloed and fragmented AI copilots. This approach can resolve issues faced by disparate AI systems and data, as well as the lack of communication.
Through unified data integration for AI copilots, enterprises are essentially aligning their AI and data initiatives with their business objectives. This can naturally boost collaboration and data sharing among business teams and functions. To leverage AI technology, enterprises need to shift their mindset from simply data collection to addressing the core business problem.
With siloed AI solutions, data integrity is probably the biggest concern among enterprises. AI copilots extract data from disparate sources, which can deliver inconsistent insights. The unified approach effectively integrates business functions and AI tools. This enables enterprises to harness the collective value of their AI tools for improved business outcomes and process streamlining.
Here’s a look at some effective data integration and governance strategies for a successful AI implementation:
- Identify the business needs.
An effective data integration and governance strategy for AI starts with defining the business goal or need. This includes answering questions like:
- What does this data achieve?
- Why do I need to collect this data?
- What are my data sources?
- Collect and transform data using real-time ETL.
Short for Extract, Transform, and Load, ETL processes typically involve:
- Extracting data from disparate sources.
- Transforming the data into a standard format.
- Loading the data into a warehouse.
With real-time ETL, AI models now have access to real-time data – even as it is being generated.
- Aggregate data.
The next step in the unified data integration process is to aggregate the data in a single centralized repository. This enables data consolidation, thus eliminating silos and providing consistent data for enterprise-wide AI applications.
- Establish a data governance policy.
Effective data governance requires enterprises to define clear policies and guidelines for data management. This can address questions like who can access this data – and whether they meet regulatory standards.
Conclusion
Due to a lack of data integration, AI-powered copilots often fail to meet their business outcomes and potential. The presence of siloed AI tools can hinder enterprises from identifying market opportunities and increase wasteful spending on AI services.
At Wissen, we can help you fully maximize your AI potential with our expertise in big data & analytics services. This includes data-related services like
- Data engineering
- Data management
- Data analytics and visualization
Are you looking for professional help in data management and integration for your AI use case? We can help. Contact us now.
