The application of artificial intelligence (AI) to real-world business problems extends across almost all sectors of the economy. Potential value unlocked by AI applications is particularly high in two functions: Sales & Marketing (up to $2.6 trillion); and supply chain management & manufacturing (up to $2.0 trillion), according to McKinsey estimates across 19 global sectors. Global spending on software, hardware, and services directly related to cognitive and AI applications is forecast by IDC to reach $77.6 billion by 2022.
Yet many companies are not sure what steps to take to realize this value.
As a recent CompTIA report states, “artificial intelligence may be closer than ever, but there are still many steps to take before it is fully integrated into everyday business.” With disruptive possibilities for both workflow and workforce, AI must be well understood by the technology team and the business executives making decisions, notes the report. CompTIA lists potential uses for AI as including improving workflow, analyzing large datasets, customer experience, security monitoring/detection, handling routine tasks, automating IT infrastructure and automating IoT systems.
What is the key to capitalizing on the power of AI? Having a good understanding and taking care of the enabling foundation work before diving into AI implementation. Many organizations hire a data scientist to build out AI and ML capabilities, only to find that they have a mountain of foundational work before the benefits of AI can be realized. The data scientist gets bogged down with organizational challenges (data and process ownership) and spends a disproportionate amount of time dealing with legacy systems and dirty data challenges.
First, you must have a solid understanding of what business problem you are looking to solve and how AI can be used as a method of unlocking value for the problem. Once a use case has been identified, the business and technology teams must be in agreement from a leadership perspective to ensure the delivery of the solution and value proposition, which often require an investment in foundational projects before the advanced capabilities and value can be delivered.
Like the construction of a house, there needs to be a strong foundation in place before the walls are built and the siding is placed. AI also needs a strong foundation. This can be achieved by rationalizing the organization data ecosystem. Every organization is unique in terms of its technology landscape. As a result, similar to the construction of a house their needs to be a proper blueprint in place when creating the architecture and infrastructure. Every organization should have a data architecture that contains a holistic set of the data to solve the business problems – or a roadmap to compile this data over time. This data centric mindset is an important cultural shift, which requires discipline, governance, and ownership to refine data before analysis and model productionization.
And of course, the right people and skill sets are essential. While some companies rush to recruit highly qualified data scientists, a more efficient approach can be to work with a skilled outsourcing partner to complete data cleanup and to build a realistic roadmap to unlock these new capabilities. With a full understanding of your data and where it’s located, coupled with sound architecture, the stage is set for the highly-qualified data scientists to apply their talents and drive forward with AI implementations.
In a recent case study, Liberty completed an analysis for a food and beverage manufacturer, enabling the client to quickly understand the disposition of vendor records across a variety of information systems. Liberty used Machine Learning algorithms to identify groups based on non-linear cluster density analysis. The unsupervised machine learning algorithm identifies patterns in the data for data-driven groupings. This analysis helped the client reduce the complexity for accounts payable, and further validate the true disposition of the vendor ecosystem. The client is able to deliver improved spend analytics to the procurement function and streamline master data management. The client reduced the total vendor count by around 8% and delivered on average $500k-$800k in savings across the accounts payable function.
The cluster analysis methodology is repeatable and simple to leverage across the mapping team. With each step, the focus is to improve data quality and reduce the amount of work required to manage vendor data.
As a recent Fortune article points out, “executives who are afraid of long-term commitments should avoid artificial intelligence. Like with romantic relationships, using technology requires an appetite for hard work, planning, and patience.” A sourcing contract with a highly experienced partner can make the difference between success and failure.
For more on this topic contact Liberty Advisor Group, at info@libertyadvisorgroup.com.
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