What is Production Level AI?

Production Level AI requires prepared data, as a single source of truth.

Production Level AI is defined as AI in production throughout your organization.  It is quite different than the generalized AI toolkits we have all seen espoused in the last 18 months.  Many of those tools qualify as “helpers” or productivity enhancers for individuals.  The business case is often exciting because of what they offer to individual employees.  Quick wins using AI in business-to-business applications are more the focus of this post.  If you want to profitably apply business rules throughout your organization, eg. Enterprise Grade AI, you first need a single source of truth that structures and governs your organization’s data.  In Business Intelligence terms, data structured for this purpose is called a Modern Data Estate.

Example of Modern Data Estate required for Production Level AI

A Modern Data Estate for your business typically begins with your ERP data structured in a way that can be accessed by an analytics platform, such as Microsoft’s Power Platform.  Data Architects can then easily add your data from a variety of sources, expanding the model.   Additionally, the data is secured, and governed.  Finally, you can account for the fact that your data is constantly changing in real time.

How is AI applied at a production level?

As stated in Microsoft’s new ebook, Six Strategies for AI Implementation, “Unified integration of AI and applications with a comprehensive data estate is pivotal for the successful adoption of AI capabilities at scale.” Our interest here is that last piece, “at scale”.  In the past year, much of the imagination and mindshare around AI has been captured by a range of wow-factor demonstrations, from exciting proof of concepts that show potential for profitability, all the way down to flat out parlor tricks leading to magical thinking.  We’ll exclude further mention of the latter and focus on profitability.  First, an organization must test AI business rules on a sample of its data. Once effectiveness of algorithms is demonstrated, how does that same organization cross the chasm to putting these AI business rules in production?

Avoid Magical Thinking

According to the Harvard Business Review white paper “Rethinking Cloud Strategies for Advanced AI”, “Companies need to be specific about what they are trying to accomplish with their AI initiatives and determine if they have enough of the correct kind of data. They then must choose how to prepare the data for use by AI applications, move it across the network, and store it. ‘The biggest challenges are not technical but strategic,’ Intersect360 Research’s Snell says. ‘People have magical thinking that they can throw infinite computing power at a limited amount of imperfect data and learn something they didn’t already know.’

AI Applied to a Modern Data Estate Yields Rapid ROI

The following approach is the fastest path to competitive advantage using AI for most organizations:

Don’t make the mistake of misdirecting your company’s forays into AI.  Much of traditional application of business intelligence forms the basis for applying AI.  Ask these questions.  Does my organization have a concrete idea that will prove to be a profitable use of AI?  Examples range from using AI to explore your supply chain all the way to using AI to identify succesful upsell opportunites for your salesforce.  Does my organization have a scalable single source of truth composed of your massive amount of historical data?  If not, then you may not be ready to deply AI at a production level.  At OmniData™ we can help you assess your readiness for the AI revolution.  In addition, we can help you scope what you’ll need to deply at a production level.

OmniAnalytics™ – Your Foundation for Production Level AI

At OmniData™, our mission is to provide Modern Data Estates that are entirely composed of all Azure based services.  This article makes the case for the superiority of the Azure Platform in general.  In this regard, OmniData’s competencies are Cloud Modernization, Data Architecture, and Analytics.  Our product OmniAnalytics™ will carve as much as 18 months off a DIY implementation.  And we remove your implementation risk.  Connect with us today.