Demystifying AI strategy: Xomnia’s AI value proposition method

This blog is the first of a series on AI business challenges. It is written by?our Lead Analytics Translator Jasper Küller

Figuring out how to identify AI opportunities suited to a company’s situation can be quite the puzzle. A puzzle with lots of pieces and many different configurations depending on the context. Luckily, even the toughest puzzles can be solved with the right tools and a well-structured, analytical approach.

Let’s begin with some definitions, as the AI elements in a business strategy are sometimes separately referred to as “AI strategy”. The concept remains the same: an AI strategy, like a business strategy, is a plan to achieve a desired state, usually centered around adding value to a company.?A strategy is necessary, because employees leave; teams are disbanded; new people join teams with different perspectives; business goals, products and focuses change over time too. Change is the only constant, which also explains why you need to evaluate your AI strategy frequently.

While this might sound logical, it doesn’t yet explain the “so what” of AI. Understanding and answering this “so what” is important because it ensures that data science & engineering teams have clear business goals (prevent hobby projects), and business stakeholders understand the possibilities of AI (prevent unrealistic expectations or missed opportunities). This alignment is needed to make sure data projects produce real, timely, and human-centered benefits — something that’s?not as straightforward as it sounds?(especially in large, complex organizations).

Three questions need to be clearly addressed when defining an AI strategy:

1. WHAT are the AI opportunities for our company?
2. WHY should our company chase these AI opportunities?
3. HOW should our company chase these AI opportunities?

To answer questions 1 and 2, Xomnia developed an AI value proposition method (further discussed in this blog). To answer question 3, we developed an organizational assessment and roadmap method (to be discussed in a future blog). Collectively, the answers to these three questions form a natural starting point for a company’s AI journey towards business value; a shared story that’s essential for inspiring, guiding and connecting (self-managing) data product teams.

Back to step 1: Xomnia’s AI value proposition method involves three comprehensive activities for translating business challenges into AI solutions in a matter of weeks:

1. Discovery workshop: Like?Bernard Marr?wrote, to find AI opportunities in your business, you should (for now) forget the technology behind it. Start with the identification and ranking of pains and gains, and map these to business processes and company strategy (Vision, Mission, measurable goals / OKRs) using preparatory interviews, predefined exercises and canvases.
2. Use case definition: With the prioritized pains and gains, create problem statements for the ones deemed most urgent and?important. Ideate a long-list of applicable data solutions (AI/ML, data science, data engineering or otherwise), and match these with the problem statements. Review with stakeholders.
3. Analysis & selection: Gather relevant data sources and explore them; model the business cases with available volume/ financial/ product/ customer data; and make a selection of the most promising use cases based on estimated business benefits (increased revenue, reduced cost, prevented churn, improved compliance).

Download Xomnia's Way of Working whitepaper

The AI value proposition method in practice: two client cases

Wereldhave, a Dutch commercial real estate company, partnered with Digital Sundai and Xomnia to determine which analytics projects best matched their?LifeCentral strategy?(“full service centers for better everyday life”) and digital journey. During the creation of Wereldhave’s analytics strategy, it was found that the ability to select the right tenants, optimize mall layouts, and properly determine rents greatly impacts revenues. To this end, tenant performance prediction, NPS driver analysis, tenant mix optimization, consumer spending pattern recognition, and new property valuation modelling use cases were selected for development.

Another client, a non-profit organization battling pollution, approached Xomnia to see if they could streamline their operations using data. It was discovered that camera footage from different locations was manually processed to determine where to focus pollution-cleaning efforts, which is prone to human error and inaccuracy. It was concluded that an object detection model could be used to automatically monitor footage at a scale, and thus make object classification and localization more accurate and consistent with limited human effort.

Download Xomnia's Way of Working whitepaper

(This article originally appeared on Xomnia's website, under the link https://www.xomnia.com/post/demystifying-ai-strategy-xomnias-ai-value-proposition-method/)