April 22, 2024

Leveraging Early Adopters to Train AI

Artificial intelligence is the enticing, shiny new object everyone’s excited to leverage for their operations, even if they’re not quite sure how it works. But the AI we experience when generating haikus in ChatGPT or planning a summer holiday in Google Gemini isn’t the same thing enterprise end users will eventually encounter within business applications leveraging AI.

That’s because AI itself doesn’t know anything—it needs training. AI is a blank slate of models, rules, and probabilities that only becomes useful once trained on relevant data. You could ask ChatGPT about the Q3 churn rate for your firm’s SaaS offering or which region of your construction business had the highest average cost overruns, but it’s not going to have any answers for you because it doesn’t have access to the data it needs to calculate that.

This might be frustrating for the end user who wants AI to have all the answers instantly, but it’s actually a good thing. If ChatGPT or Gemini could answer those questions, that would mean it’s accessed your sensitive business data and nothing would stop competitors or bad actors from finding those same answers… or worse.

For AI to truly add value for organizations, it must be trained on your organization’s data. Only then can it learn how your organization works and what specific terms mean within that context. And that learning takes time and data… lots of data. Just like you wouldn’t open your refrigerator and declare that what’s inside are the only foods that exist in the world, you wouldn’t want your in-house AI to only train on a couple of sample pieces of data from a few sources.

Outfitting your AI with the ideal set of training wheels

Training AI requires lots of repetitions. It learns through trial and error. That means that when you first take it out of the box to play with it, it’s not going to be that impressive. It will be wrong at times, in seemingly maddening ways that make you question just how “intelligent” it actually is. But just as we don’t write off a 5-year-old child as a terrible speller, we also don’t want to dismiss AI during its early days because it makes mistakes. Those mistakes are part of the learning process and essential to AI eventually living up to its potential. That’s why early adopters can play a critical role in the training process and should be considered vital stakeholders in any internal AI rollouts.

Not everyone has the time, patience, or temperament to be an early adopter, nor is every organization willing to sacrifice a lot of person hours and computing power for a learning exercise with little proven upside. If you’re just trying to get things done as efficiently as possible, dealing with a new technology that hasn’t worked all the kinks out yet is not appealing. But for those craving the cutting edge and eager to participate in the process, AI is intriguing even during its nascent, learning phase.

For the best success, recruit early adopters who display these key traits:

  • Beta tester mentality—They’re not afraid of bugs, they’re actively hunting them. Being super observant and able to document and articulate any issues they encounter is critical.
  • Adventurousness—They’re game for whatever gets thrown at them and are up for any surprises that come their way but patient enough to deal with any challenges or setbacks.
  • Enthusiasm—They see the promise and potential, and they want to be part of the effort that brings it to the masses, but they’re also willing and able to provide constructive criticism.

As an added bonus, it helps if your early adopters are also somewhat connected and respected in the organization. Because once your solution is ready for rollout, they can evolve from early adopters to internal evangelists, spreading the word and encouraging colleagues to give it a go.

Define syntax and taxonomies before launch

Metadata—and the AI that uses it for training—is only valuable when it’s consistent. If one person tags an email with “Europe” for its region, a second person uses “EMEA,” and a third introduces “EU” as a value for that field, your firm now has three values for the same thing. Multiply that out across all the potential metadata tags that could have multiple valid options, and things get muddled and messy quickly.

However, if your team of early adopters all consistently use “EMEA,” when more end users get on the scene they’ll both see by example that “EMEA” is the preferred region, and when AI-powered recommendations kick in they’ll suggest “EMEA” rather than “EU” as well. The same logic applies to the naming and organization of directories and folders on shared repositories such as SharePoint or Microsoft Teams to ensure things are saved in the right spot from the start.

Employing these Information Management best practices gives you a head start on future AI initiatives. Try creating a Metadata Advisory Council to ensure consistency across the organization.

Identify opportunities where AI can improve business performance without disruption

There’s no telling how AI will ultimately impact how we work, but most organizations want to employ it strategically as new paradigms form. So rather than dive into the deep end and hand over mission-critical aspects of the business to AI, try finding some places where it can make end users’ jobs a little easier while also helping the organization achieve goals unrelated to AI itself. One of those domains is Information Management.

At harmon.ie we’re introducing AI-powered recommendations to help end users classify and organize the content they’re sharing to Microsoft Teams or SharePoint from Outlook. With AI suggesting the best location to share an email or attachment, proposing metadata options, or identifying which emails should be shared from your inbox, harmon.ie can help end users save time, be more accurate, and increase their compliance with corporate email disposition policies. And because harmon.ie’s Private AI runs exclusively on your tenant, there’s none of the security risks that other AI solutions introduce when they export your data to their servers for training.

By helping workers do their jobs better—rather than having their jobs replaced altogether—they’ll see the advantages of leveraging this technology rather than fearing it. It’s also an opportunity to demonstrate the real-world potential of AI for your operations without increasing operational risk.

See what else harmon.ie has planned for this year or begin your free trial today to get started.

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