How to Adopt AI in 3 Steps

At the turn of this century (or even ten years ago) if you thought about what artificial intelligence (AI) and machine learning (ML) technology would look like in 2020, you probably envisioned robots, flying cars, and tiny cell phones. And while all of these technologies are possible today, real AI and ML adoption is coming in the form of everyday automated tasks.

It may be helpful to identify first the major difference between AI and ML functions. AI is when computers or other automated technology operates in executive functions — in other words, decision-making. ML is when a system is programmed to learn new things from data or from experience itself.

These technologies are likely already being employed in some capacity in your office. For example, automated email sorting — like when a spam email is sent directly to your ‘Junk’ folder — is machine learning because the system is being told how to sort the data based on your preferences and then making its own assumptions based on that given information.

Once you understand a framework for utilizing the capabilities of these technologies, it is easier to imagine how they could not only transform the tasks you perform, but the overall efficiency of your company’s processes. Implementing new technology can be a major disruption, but disruption innovation can be an impetus for change and major gains in the future.

How to Get Started

The integration of AI and ML into the office and everyday administrative tasks will drive your company forward.

Consulting firm PwC conducted their own survey of what benefits companies are expecting from their investments in AI, with the majority of respondents cited “operate more efficiently” and “increase productivity.” Increased efficiency and productivity don’t sound very sci-fi, but they are foundational processes and operational improvements that are worth getting excited about.

But what if you are a part of a more change-resistant organization?

Implementing AI and ML can seem overwhelming if your organization’s tech (or approach) is a bit behind-the-times. However, there are actions you can take to drive your AI usage and increase not only efficiency gains, but productivity through streamlining otherwise tedious processes.

Read on for a simple three-step plan for catapulting your company’s AI strategy into 2020 where it belongs:

Identify a Problem

First, identify where AI can have the greatest business impact and build the capabilities that are needed to succeed. Identifying your organization’s biggest pain points can lead you to identify where there is a need for automation.

For example, life insurance underwriting could be almost completely automated by AI in the near future. These AI technologies allow for more accuracy and the ability to serve more customers faster.

Another way to develop a strategy is to determine where you are already using digital technology. A McKinsey survey from 2018 found: “At the most digitized firms, respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI.”

Whether you start from where you lack digitization the most or the least, be firm in your commitment to that strategy as you move forward.

Create a Framework

The next step is to get organized and create a framework for implementation. A successful framework requires:

  • The right tech capabilities: Determine what hardware and software you will need to deploy, integrate, and effectively support intelligent automation.
  • Development and deployment: Map out the processes and resources needed to design, develop, and implement your strategy.
  • Organizational readiness and sustainability: You will need not just monetary investment to support AI and ML implementation, but also company buy-in.
  • Governance: Sustainable change requires resources and an operating model after implementation and continued reinvestment after the initial deployment.

Set Criteria for Success

Before implementation of your plan, determine the criteria you see as a measure of success.

The great news about AI and ML technologies is the amount of data and feedback created through their processes. Measurable data will be available for your analysis and assessment as you determine where there is more opportunity for continued support or streamlining of processes, and where there have been measurable gains in terms of workload and revenue from the implementation of your AI and ML strategy.

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