The Midpoint Consulting 10-Step AI Method

Preparing Your Business for AI

By Israel Squires, Devvan Stokes, Pete Dulcamara & Michael Barber

Midpoint’s 10-Step AI Method to integrating AI into Your Business is your blueprint for embedding AI in a way that respects, enhances, and amplifies human potential. Our pioneering approach demystifies the process of AI integration, guiding you through a journey from conceptualization to implementation, with the human element at its core. We believe that technology, when thoughtfully applied, can unlock unprecedented levels of productivity, creativity, and well-being for your workforce.

Stay Informed:

Choose your company AI Superstars and setup an internal AI Task Force. Keep abreast of AI developments in your industry to understand how they might affect your business. Take the time to become familiar with what modern AI can do. Take advantage of the wealth of online information and resources available to familiarize yourself with the basic concepts of AI.

Correlate Your Learnings:

Learning the basics of AI is step 1. Step 2 is applying that learning to your industry and your business. While there will be certain levels of democratization of augmentation tools, the speed and manner in which these tools are implemented will be different business to business.

Identify the Problems You Want AI to Solve:

Assess where AI can add the most value to your business and correlate that assessment to where the greatest needs in your business are. Think about how you can add AI capabilities to your existing products and services.

Define Outcome Metrics:

Augmented intelligence systems can only intentionally solve problems for which you can take a measurement. You have to know when something is working and not working.

Acknowledge the Internal Capability Gap:

There’s a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. Tang said a business should know what it’s capable of and what it’s not from a tech and business process perspective before launching into a full-blown AI implementation.

Prioritize Concrete Value:

Next, you need to assess the potential business and financial value of the various possible AI implementations you’ve identified. It’s easy to get lost in “pie in the sky” AI discussions, but Tang stressed the importance of tying your initiatives directly to business value.

Invest in Skills:

Building or acquiring AI expertise is crucial for leveraging AI technologies effectively. This does not mean JUST technology skills. This means investing in AI education for your entire workforce. Increased understanding of AI will create increased capacity for every person on your team.

Experiment, Learn and Start Small:

Start with pilot projects to explore how AI can benefit your business before scaling successful initiatives. Begin applying AI to a small sample of your data rather than taking on too much too soon.

Create an Infrastructure Plan:

Continued monitoring of the infrastructure capability of your company will be a continuous process. After you ramp up from a small sample of tool integrations, you’ll need to consider the storage requirements to implement AI solutions throughout the business.

Find Balance:

Developing an AI system necessitates a dual-focused approach that caters to both the technological needs and the objectives of the research initiative. A fundamental principle to consider prior to initiating the design of an AI system is the importance of creating a well-balanced architecture. This point may seem self-evident; however, it’s not uncommon for AI systems to be crafted with a narrow focus on specific research outcomes, neglecting the essential hardware and software capabilities required for support. Such an oversight can lead to the construction of a system that operates suboptimally or, in some cases, is entirely ineffective, thereby failing to fulfill the intended research objectives.

To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. Security is an oft-overlooked component as well. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards — encryption, virtual private networks (VPN), and anti-malware — may not be enough.