Conventional organizations need to first analyze where they need AI applications and if they do need them in the first place. Companies need to understand in what capacity to leverage AI and what the short and long-term goals for AI implementation need to be. The first step to moving toward a data-driven, AI-powered organization is to take stock of the internal data capabilities and talent and start with building a ‘Data and Analytics’ team.

To do so, companies need to create an ownership function, starting with the Chief Analytics/Data Officer, who will report to the CEO (this is very important because otherwise activities can be steamrolled either by the CFO or COO). As a second step, a small team needs to be hired, with the following skillsets:

- Senior Analyst (to collect and analyze company use cases and create internal POCs)

- Data Engineer (to put all data sources in one place - internal, eg. Point of Sale data, competitive data, etc)

- Data Scientist (to start using external services like Google Cloud etc. and build some models to complete the POCs)

Depending on the POC outcomes and the expected ROI, the company may hire more of the above, eg., if more custom models are required, hire more data scientists.

However, finding the right talent and training them can be expensive, let alone building an internal AI infrastructure from scratch. Hence, if you are not a ‘heavy data-handling company’, using external APIs to achieve your goals is key. Partnering with vendors that specialize in AI solutions that you are seeking can be faster (through API access), less risky, and more economic. But if the cost of the external APIs is too high and there is no ROI, the probability for you to succeed by internalizing the project is very low as you won't have the right talent. It is therefore important to strike a balance between partnering with the right AI vendors that have a rapid ROI potential and building your own internal capabilities.

The next step is to identify which business area or function to target first. Rather than trying to introduce AI across organizational processes in one go, it is much more judicious to analyze the simple challenges first and identify the low-hanging fruits where AI can be easily integrated and create significant value. For example, an AI-driven pricing solution will always start with a simple regression model tested on a small set of SKUs. Once this process is complete, review the ROI and analyze the business impact it can create at scale. It is important to understand that it is an iterative process that requires frequent reviews.

It is important to bear in mind that transitioning to an AI-driven organization takes time. Building AI applications is a continuous process and works better if you follow a test-and-learn approach to identify and resolve problems early on. Setting up and implementing AI processes cannot happen overnight.

Finding the right talent to build a focused, in-house team with domain knowledge and then forging relationships with vendors to partner with, can easily take up to a year or even more in some cases.
AI and big data analytics are bringing a paradigm shift in how we do business - helping improve revenue, productivity, market share, and processes across departments and making organizations future-ready. To take full advantage of data analytics and AI, companies need to incorporate these technologies into their vision and core business processes. Companies need to be adaptable, and flexible; they need to realign their culture and conventional processes to make place for AI-driven decision-making.