Kristin Simonini
Kristin Simonini is VP of Product at Applause. A 20-year veteran of the product management space, Kristin leads Applause’s product organization. Her team is responsible for defining the strategic roadmap for Applause’s industry-leading crowdsourced testing platform. Prior to Applause, Kristin led the product management efforts at EdAssist, a Bright Horizons Solution at Work where she instituted a product management practice and led the effort to reinvigorate their industry-leading tuition assistance platform including the release of their first mobile app.

Artificial intelligence and machine learning (ML) offer companies massive opportunities to improve operational efficiencies, decrease costs and increase profits. As noted by Gartner, “the successful application of AI can unlock new opportunities and help achieve business goals.”

But the adoption of AI and ML presents some challenges that must be overcome before companies can enjoy the benefits of these technologies. The data problem, for example, caused by AI’s voracious appetite for data. A recent Forbes article observed that organizations “that can capture the most data in the shortest amount of time may be the ones to capitalize on the promise of AI. Those that cannot will likely fall behind and risk investing in AI programs that don’t work.”

Read More:   25 Questions to Ask Before You Hire Web Developers in 2022

Success in the implementation of AI and ML strategies can depend upon the ability of your organization to harvest massive quantities of data from a large and disparate group of sources: your customers. However, the real difficulty lies in ensuring that the data collected is representative of your entire customer base.

The following are four keys to navigating the AI/ML modernization journey in your organization.

#1. Ensure Human Bias Doesn’t Ruin Your Outcome

AI and machine learning offer the opportunity to remove human bias from your data. Care must be exercised, though, to ensure that human bias does not creep into the machine models. Choose your selection criteria wisely, for it will determine how your machine models react to their human counterparts in the real world.

And when possible, consult with pertinent subject matter experts. SMEs can be immensely helpful in separating the wheat from the chaff. They will know best what data is important and what data might introduce unintended biases diminishing the accuracy of the AI’s end results.

#2. Build Feedback into the Process

Communication is vital in obtaining the best results from AI and machine learning. But if your organization is like most, you’ll want to constantly guard against the common tendency for teams to fall into silos. Silo’ed teams rarely incorporate feedback loops into their process, and having a feedback loop in place is essential. Feedback can keep you on course by letting you know what’s going right and what can be improved. Feedback is the fuel that powers your efforts to constantly improve your machine models.

Make certain that your feedback loop evolves with your machine models. Early on, your feedback loops may be guided solely by subject matter experts. Eventually, your machine models should be generating and incorporating some of the feedback in an automated fashion.

Read More:   5 Types of Machine Learning Algorithms You Should Know - 2022

#3. Make Your Data Worthwhile

It’s true that data is the new gold. But unused data is as useless as unmined gold. The more relevant data you’re able to feed your machine models, the more productive and accurate they’ll be. And that’s why data science teams are so crucial to AI projects; they are able to process the data and make sense of it, making it actionable. Data science teams possess the unique skillset necessary for managing vast quantities of data and using it to improve machine models specifically, and business processes as a whole.

#4. Remember: There’s No Time Like the Present

While there’s no such thing as too much data for AI, there’s also a point where enough data has been collected that you should start, or risk becoming paralyzed by a pursuit of perfection. If you delay starting until you are able to make your AI perfect, you’ll never get started. Because your AI will never be perfect.

Similarly, if you over-analyze your machine models, you’ll never get to launch. In part, that’s because AI and ML are, by their very nature, constantly improving processes — the work is never really done.

Yes, the more data you feed your models the better they will be. But you must get your models out the door and into the real world at some point for them to begin delivering results. Beginning with the data you have available is better than not beginning at all.

It’s Easy to Put the Hardest Part Behind You

A 2018 McKinsey survey found that only one in five companies has embedded AI within multiple components of their business. And a mere 3% of large companies have integrated AI across a broad spectrum of their enterprise workflows.

Why have so few organizations begun to tap into the benefits of AI? Why are so many risking the competitive disadvantages of falling behind with AI? Perhaps it’s for a very simple reason: getting started can be the most difficult task in the AI-ML modernization journey. But, paradoxically, the getting-started roadblock can be the easiest to overcome. It just requires the will to move ahead in the journey, along with the establishment of a sound procedural foundation.

Read More:   Re-Evaluating MTTR as Key Metric for Operational Performance – InApps Technology 2022

As your company ventures forward in the AI-ML modernization journey, just be sure to:

  • Constantly gather data
  • Ensure its validity (and remove bias)
  • Continually improve the process through feedback
  • Utilize your data to implement your AI strategies

And remember — prognostications of existential threats notwithstanding — the biggest threat that any company faces from AI/ML is the failure to capitalize upon the unprecedented potential that is there for the taking.

Feature image via Pixabay.