The Role of Enterprise AI in Data Driven Analytics By Balaji Krishnamoorthy, EVP Findability Sciences

The ability of AI to convert Data into actionable insights can help organizations from Better Efficiency, Customer Service, alignment on company-wide goals, transparency to generating new revenue models. The Power and Scale that AI has for Business and Governments has been proven, and the excitement of Generative AI has taken it to a different level. As organizations use different data sources and datasets to Artificial Intelligence, it’s important to have the right guardrails in place to ensure data quality, governance compliance, and transparency within your AI systems.

One of the primary functions of Enterprise AI in data-driven decision making is the ability to handle massive and disparate datasets. Traditional analytics tools may struggle to process the sheer volume of data generated by modern businesses. The complication or challenge that organizations still face is which tools and methodologies they should use to create a successful AI driven Enterprise. This along with challenges in areas of Data Quality, ensuring fairness and no Bias, Regulatory and Ethical concerns, scalability, and lack of skilled talent typically top the list. Finding the right Enterprise AI partner and collaborating with them to build a successful AI Enterprise becomes crucial.

Organizations, before choosing the tools, methodologies, and process for Enterprise AI in Data-Driven Decision, need to ask themselves:

Do you have specific areas of databases that you have challenges in driving business value, how are you using or what Data do you have for your AI Initiatives?

What is the sponsorship and plan to promote data access and drive organizational shift in becoming data-driven? How do you work with your compliance, risk, and Data audit team ?  

Answering these Question’s will help determine:  

• What tools and methodologies should one implement?
• How do you choose the right AI Partner
• How can one continuously learn and adapt in an ever-changing AI landscape?

As more organizations will take a data-driven approach to create business impact, they are also transforming the approach to data management. Let’s discuss how we can address some of the key components to operate, govern, integrate, and consume data assets to drive value in your data, by implementing Enterprise AI.

The Core for any AI Implementation is to have quality Data and a proven Methodology at each stage of the AI Journey. Findability Sciences CUPPTM methodology helps organization at each stage. Collection and Unification helps in generating highly resilient, transactional data, intelligent operational data, and unified analytical data. Here it is important that Organization not just look at Big Data, but the width of Data defined as Wide Data.

The Processing of Structured and Historical Unstructured information can be combined under the Discriminative AI. This brings both Prediction and NLP together and comprehensive tooling with multimodal and a multicloud data ecosystems, to help Organizations DataOps ready for AI engineering along with DevOps and ModelOps.

Finally, Organizations can meet their governance, risk and compliance objectives with AI-powered self-service and data-driven governance and security.

These Tools and Methodologies will help Organizations confidently move from answering What Happened? What Will Happen and What to Do? Combined with Governance, both successfully and with trust.

Technologies from Enterprise AI Organizations like Findability.AI & IBM’s Watson X, or Microsoft Azure can be used to expedite the AI Journey within an Organizations. Organizations can also choose to use Opensource technologies on AI like TensorFlow, Python or accelerators provided by Amazon. Whatever direction organizations wish to choose they should look at partners who have success in implementing the Use Cases and shall collaborate and help the organization build their Center of Excellence.

Finally, there is Generative AI, the new kid on the block that has completely taken the imagination and trajectory of Enterprise AI in data-driven decision to greater height. The evolution of AI technologies, coupled with ongoing advancements in Discriminative and Generative AI, will shape a future where organizations derive even greater value from their data.

This leads to the question How or Where does one start?

Organizations who are mature in leveraging Data for AI like the ones in Banking, Finance & Healthcare space or companies starting to leverage Data in the Manufacturing space, should Identify a use case to implement a foundation that delivers value while laying a foundation. This can then be scaled to other Use Cases, a Data Census or understanding the Fabric of your Data is the first step in the Data to AI Journey. This helps in identifying the right use case, which typically can be answered by asking:

• What is your top priority use cases?
• Is there a particular use case that is considered an executive priority?

There are use cases like Demand & Sales Forecasting, Next Best Product Recommendation or Propensity to Pay, which can directly impact Revenue growth. Collaborating with clients on these Use Cases, we have seen anywhere from 30 to 200% positive impact from prior to the implementation of AI. Unleash data and AI agility to enhance existing product lines, create new products/applications, and optimize customer experiences.

In conclusion, the role of Enterprise AI in data-driven decision making is multifaceted and transformative. From handling massive datasets both internal & external, improving accuracy, and providing actionable insights, Enterprise AI can help organizations thrive in the data-centric landscape. With the recent excitement about Generative AI and the power of Discriminative AI, the time for action and use of AI is now. Organizations and leaders not leveraging AI shall be quickly replaced by one’s who do.

Start your Data & AI Journey by identifying a partner with proven AI technology track record, who not just throws products or services at your organization but is a collaborator and takes equal responsibility to see your initiative succeed.

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