AI Maturity & Adoption Strategy For Businesses Who Aims To Be AI-Powered
While AI is now the most ambitious dream for most companies but its success heavily relies upon your IA. By that, we mean that there cannot be a successful Artificial Intelligence semantic without a great Information Architecture. We recommend you to take a look at our Data Modernization process to help you understand further and better.
Inducing the AI fabric in an organization is a collaborating task were stakeholders come together to work with us on a regular basis for till the journey is achieved, in addition to this here is our pragmatic process to help you leapfrog this paradigm shift.
- Identifying the problems we aim to solve with AI in your company:
Once we are done with basics, we need to think about how we can add AI capabilities existing aspects of your business, it is good to have specific use cases to target with AI and generate demonstrable values.
- Determining concrete values:
Assessing potential and financial value possible implementations identified in the previous step, also prioritize short term visibility and long term benefits that are going to affect your business.
- Reconcile and acknowledge the internal capability gap:
This realistic assessment highlights the cracks that are to be fixed prior to implementing a full-blown AI. It may involve identifying what you need to acquire and any processes that need to be internally evolved before you get going.
- Experimenting with a PoC(Proof of Concept) or a Pilot project:
Depending upon your data readiness business can chose to start small and scale based on the arbitrary success as it will be hard to go from “Zero to 1” but will be easy to move “1 to n”. Pilots(2-3 months) are done on real data with real people as an experiment however PoC(3-5 Weeks) can take place in much smaller surrounding but for both data, authenticity plays a key role.
- Data Maturity before AI maturity:
A lot of success depends upon how mature the data landscape is at the organization, of course, we can have a workaround if needed but the right set of data will derive the right set of values and insights for which we can assess if there is a need of Data Modernization.
- Finding the right technology/platforms, architecture, models:
This is the most important step to chose that technology that is compatible and seamlessly integrates with your data sources, choosing the right AI models targeted to specific problems in the context of domain of the business. And later on to build a scalable and end-to-end training and inferencing pipeline. Launch and iterate while adding new features to the pipeline.
- ML Maturity & Model Growth:
Eventually, with time and more data sets with varied inputs, the AI aspect will continue to mature and evolve into a full-fledged cognitive characteristic of your business.
Would you like to know more about how we can help you as a business? feel free to reach us out we are just a click away!
Consulting Manager - Product & Solutions