Creating right foundation for AI adoption

AI is on the agenda of every CEO and is discussed in every Board Meeting. The real details however lie in examining the aspect of competitive advantage that the AI provides to bolster the enterprise and the execution of AI programs. Does AI aid the Company in addressing the gaps in human resource skillset or overcoming technological hurdles or disrupt its routine in the digital world or build enterprise for future?

The perspective changes with the way everyone approaches it when trying to make it to mainstream…

  • For some it starts with math and (or)statistics that drive enterprises into hiring PhD’s and statisticians to create their own magic AI sauce
  • For some, it is investing with technology leaders, be it Amazon, Microsoft or Google, for an AI solution to their various business challenges
  • Others believe in investing with development of their business / data analyst to deliver and drive their AI strategy

Enterprises struggles with data collection, isolation of noise and extracting the relevant information from both structured & unstructured.

Enterprises are constantly challenged to recognize and enhance the ability to learn and identify the data relevant for the AI application.

They also face the challenge of how to learn the tribal knowledge from the users to build an enterprise knowledge model that will build the bridge between user and AI engine.

To be successful in the adoption of AI, an enterprise needs to invest in building right technology capabilities for establishing digital data foundation, drive agile design & discover, and enhance user experience.

The most successful enterprise realized the secret sauce for adoption of AI is by establishing the following foundation blocks:

  • Digital data platform to support design & discover, auto-learn, knowledge model and UX driven business apps.
  • Building strong data foundation and eliminating the noise in the data.
  • Seamless scale of technical infrastructure on demand to the business.
  • Building strong team who are good at data and understands the business and modeling (aka data scientist, data analyst, data engineers, business analyst). This will be a mix of existing SME’s and new talent with data skills.

Tailwyndz helps client through it’s out of box AI apps built keeping the secret sauce for AI and open for build custom apps leveraging the learning from its app building experience.

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