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5 Steps to Successful AI Implementation
April 1, 2020 News

 

Artificial Intelligence (AI) is a core component of the digital journey for modern organisations. It moves us from decision tree logic, with applications following a set of predefined instructions, to software being able to learn from data and then make informed decisions.

Achieving success with AI is not just about having a great AI technology; It’s about building the infrastructure to facilitate feeding AI with the data it needs. IBM refers to this as building an “Information Architecture” (IA).

 

Modernise the approach to data by building a data architecture designed for AI.

AI needs access to data, lots of it, often at speed, in real-time and from multiple sources. Hence, organisations need to modernise and cloud-enable their data architecture to feed their AI models. If the information architecture is not in place, then the AI application you develop is likely to fail or underwhelm.

 

These Five Steps Pave the Way to a Successful AI Implementation.

It’s vital that businesses build a platform that can deliver the data that AI needs. IBM’s concept of “the AI data ladder” neatly positions the critical steps that they need to consider.

 

  • Step 1: Modernise – Move from static and hardware-based data architecture to an agile, multi- and hybrid-cloud based information architecture. This is likely to be based on container technology, which allows applications and data across any type of cloud, to be leveraged by your AI technology.
  • Step 2: Collect – To complement a modern information architecture, you need a repository or data store that can collate, store and manage both structured and unstructured data and integrate with AI-driven applications. The data store is likely to be cloud-based and has to meet the level of data governance that your business requires.
  • Step 3: Organise – Your data has to be consistent. If it is not, then neither will the results of your AI-driven analysis. This means cleaning, organising and cataloguing data at scale. It should be governed to ensure only the right people and applications have access to the correct data.
  • Step 4: Analyse – Once you have access to enough trusted data at scale with flexibility, this is the point at which you can use AI to analyse, derive insights and share results. Quality data must continue to feed the AI algorithms to enable ongoing machine learning.
  • Step 5: Infuse – This is where the power can really unfold. Once the underlying architecture is built and proven, the applications for AI can widen. AI can more quickly be rolled out and leveraged across multiple departments and teams. Over time, you can infuse multiple business practices and departments with AI-driven insights and automation.

 

AI is a journey which should be heavily linked to your cloud journey. After all, building the information architecture that supports successful AI will, by definition, involve leveraging cloud.

A very powerful example of a coordinated approach to cloud, data and AI transformation is IBM’s Cloud Pak for Data, which has been created with an implicit understanding of the important principles described in the five steps above.

Every business that is looking to modernise and transform needs to get a grasp on how it will leverage AI. The key to success is understanding that AI is not an isolated journey; Its success depends on the knowledge that it is part of a unified journey towards multi-cloud and ubiquitous data.

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