top of page
Search

3 steps to designing a strong data foundation for AI implementation to transform CX


  • The complexity of crafting a viable data infrastructure that is fit-for-purpose for AI and IA adoption cannot be underestimated. Let’s not forget most organizations are constantly leveraging historic data.

  • It is reasonable for any sizable business - enterprise or government institution to have volumes of data being added on a daily basis.


Thus, ensuring the data being collected by organisations is relatable across multiple systems, and not sitting in silos (one system, process, department, etc.) is vital. The correlation of data across functions and processes allows an organization to achieve a holistic view of the customer. Thus data structuring is an important choice organizations need to make when implementing IA and AI applications. However, complete data synthesis and visibility is an ongoing challenge for most organisations.
Ensuring the data being collected by organisations is relatable across departments, and not sitting in silos is important.

Thus, ensuring the data being collected by organisations is relatable across multiple systems, and not sitting in silos (one system, process, department, etc.) is vital. The correlation of data across functions and processes allows an organization to achieve a holistic view of the customer. Thus, data structuring is an important choice organizations need to make when implementing IA and AI applications. However, complete data synthesis and visibility is an ongoing challenge for most organisations.


Another challenge facing organisations is that AI and IA projects require access to data that has been stored and structured differently from what most organizations are accustomed to. The current data composition means organisations need to work with unstructured data which was never planned to be used for in-depth analysis but simply record keeping. As such, companies need to invest in time-consuming and costly processes to prepare the data for analysis, whilst protecting the privacy and assuring security.


According to a survey Customer Experience Live conducted in April 2021, the most popular AI applications that require structured data include Data Analytics, Business Process Management, and Machine Learning.

3 steps in designing a strong data foundation for AI implementation to transform CX


1. Set an attainable business vision

It is essential to understand how AI integration effects and fits into each process. After a solid understanding, it is possible for AI and IA to aid current business models and examine new data-driven business opportunities.


2. Recognize and analyze the data source

It is essential to find a way to collect and structure the data during the preparation period. Many organizations in the Middle east region use a combination of premise and cloud-based solutions.

Cloud-based solutions are taking predominance in supporting AI and IA implementation, with substantial benefits including flexibility, variable costs structures, access to larger datasets, and the ability to scale up and down to accommodate changing demands quickly.


3. Find and use the right tools to structure data infrastructure for optimal AI and IA implementation

It is vital to find the vendors that best fit an organization's vision and objective regardless of the number of available suppliers, especially with the available solutions already wide open and growing. The reason behind this is to have the flexibility to achieve scale beyond the initial phase as AI and IA evolve.


Examining the payoff – 4 benefits of achieving AI and IA capabilities


Some figures of the potential positive impact of having viable AI and IA capabilities from the survey results suggested the top outcomes were:

  • Improved customer satisfaction

  • Reduced operating costs

  • Increased innovation

Each of these three outcomes has its foundations in comprehensively leveraging data, the same thing necessary to achieve viable AI and IA integration in the first place.


1. Improved data analytics: the complexity of implementing AI and data analytics is beyond most organizations planning and resources, with companies that are able to achieve it gaining competitive advantages in their respective industry sectors. Some commercial organizations are already ahead of their rivals, continuously improving customer experience (CX) by using AI to analyze data sources from every platform such as chatbots, e-commerce platforms, and social media to gain an overview of their customer.


2. Better data management and governance: AI, RPA, IA solutions promote data quality and access to accurately capturing, storing, structuring, labelling, accessing, and governing data. Thus, companies can focus their AI and Automation on pre-existence relevant data. It is only a matter of time when companies will have access to structured data that can be leveraged by IA and AI platforms, with little time and money spent on improving data prior to implementations.


3. Strengthening data security and privacy: AI and ML solutions can spot patterns in data to gain commercial advantage, track fraud and minimize cybersecurity threats, thus ensuring the data is reliable and delivering better ROI.


4. Bringing RPA and BPM together

Both BPM and RPA provide significant and continuous improvements. If implemented correctly, organisations stand to gain long term benefits. While RPA enables high-volume, repeatable tasks, a BPM-led approach allows for extensive improvements in streamlining business processes for maximum efficiency and value.


Next: 4 ways RPA and BPM are relationally valuable - read on.

bottom of page