As the hype around A.I. continues to increase, you may wonder how to get started with an A.I. project. To create real value, you need to apply it as practically as possible. This means finding new paths for reducing costs and discovering operational improvements.
Find and prioritize the best use-cases by working with the subject matter experts and internal stakeholders involved with the use-case. You’ll know you’re on the right track when these people brighten up at the idea of streamlining their work.
Next, identify the availability and viability of the necessary data. Determine if accurate and consistent sources are available, and how they will be accessed.
Finally, build specific people, processes, and tools around the bigger picture of A.I. governance.
3 Steps to Getting Started with an A.I. Project
- Identify the best use-cases
- Create a data supply chain
- Get the right people, processes, and technology in place
1. The A.I. Use-Case: Start with a Clearly Defined Problem and Outcome
Chances are you already have a problem you’d like to solve and you are wondering how A.I. or automation will help.
According to research firm Gartner, Inc., over half of all machine learning projects never make it to production.
The best way to get started with an A.I. project that won’t fail is to clearly define the business problem you need to solve. A.I. is not all about algorithms and fancy machine learning models – it’s all about improving business outcomes with the right use-cases.
The best use-cases will have measurable and predictable outcomes and an expectation of both costs and return on investment. Use this handy graphic as a guide to assess your use-case for maximizing A.I. outcomes:
2. Data: Build a Data Supply Chain
Take time to adequately document and define the problem. Consult the business and process stakeholders to understand exactly what data and information they process and how.
For an A.I. implementation to progress into a production environment, enough of the right data must be consistently and predictably curated and governed. Successful deployment absolutely depends on a high quality fully managed data supply chain.
If your data supply chain consists of unstructured, or document-based data, you will need an intelligent document processing solution (IDP). IDP will consistently convert and integrate accurate data into your line of business applications or robotic process automation (RPA) tool.
You may even discover that combining IDP with RPA will provide the business-changing innovation you are looking for rather than building custom A.I.-based solutions. Regardless of the tools you choose, mastering A.I. requires an ever-expanding strategy for collecting and orchestrating data.
3. People, Process, and Technology: Structure Your A.I. Initiatives
As with all business strategies, a structured and governed plan provides better outcomes. Enterprise A.I. initiatives are no different. People, processes, and technology play a crucial role.
People are the most important consideration. Whether internally-sourced or consultants, three roles are key for success:
- A.I. specialist
- Information Technology / Data Architect
- Most important of all; subject matter experts
Also, increasing overall data literacy in any organization will booster the effectiveness of any new A.I. initiative.
There are proven methodologies that have been created to ensure success with A.I. projects. Two are ModelOps and MLOps:
What is ModelOps?
ModelOps is your organization’s strategy for managing the life cycle and governance of A.I. and machine learning / decision models. The idea of ModelOps is really the centralized management of all A.I. projects, proofs of concept, and pilots. By maintaining an overarching focus on A.I., you will drastically improve success and achieve better business outcomes. The success of your A.I. initiatives hinges on a successful adoption of ModelOps.
What is MLOps?
MLOps is the operationalizing of machine learning models themselves. This is a critical component of ModelOps that is used for standardizing the deployment and management of machine learning models. It includes the people, processes, and technology needed for success.
When it comes to technology in A.I. projects, the lines get blurred. You need to identify what, if any, pre-baked solutions exist, and what specific data sciences techniques are needed for success. Most data science algorithms are not new and may only need to be adapted to your data sets and processes.
More than likely, you’ll use a blend of A.I. techniques that are already integrated with existing line of business software. As mentioned before, intelligent document processing includes many pre-built data science tools, potentially saving time during the data gathering process.