Real Steps to AI Adoption Within Your Business

When a company decides to start using AI, the path forward isn't one-size-fits-all. Where you are on the technology adoption curve will determine the steps you need to take to successfully integrate AI into your operations.

Starting Point: Understanding Your Tech Maturity

If you're a tech-forward company that already has proprietary technology or significant technological sophistication, you can view AI as a new set of tools to incorporate into existing workflows. The integration will feel natural, like adding another capability to your tech stack.

But here's where things get different.

The Fundamental Shift: From Deterministic to Probabilistic

The tools won't behave like other tools you're used to. You'll find that you can get different outputs with the same input, and this is fundamentally different from traditional software. With practice, you'll learn to understand what types of inputs and outputs you're working with, and more importantly, when generative AI will give you inconsistent results.

This is the most important aspect of adopting generative AI within your organization: understanding that you are moving from deterministic software to probabilistic software.

What does that mean?

In deterministic software, a single input will always give a single output. You can run it a thousand times expecting the same result every time. Generative AI is fundamentally different because the same input can give different outputs depending on various factors and settings—and we're not just talking about temperature and prompts.

For example, the larger the prompt, the more variation you should expect to see in your output. Additionally, the larger your output, the more variation you should expect to see.

Stage One: Getting Comfortable with Risk

As your organization starts to experience this variation, you'll need to become comfortable with a new level of risk—the reality that information might be different each time you run something. This immediately highlights the importance of being able to log and audit different interactions with AI. The more important that interaction is to your business, the more vital it is to have traceability to what happened.

Stage Two: Implementing Controls and Safeguards

This brings us to the next stage in adopting AI: understanding what controls your organization needs when you're going to start utilizing generative AI for decisions beyond normal brainstorming, note-taking, or summarization.

When you start to explore formal tools—software that's powered by generative AI—ensure that you know what kind of safeguards and traceability that solution has in place. This isn't optional. It's critical for any AI implementation that touches business decisions.

Stage Three: Full Adoption and Process Transformation

Finally, you'll be in the midst of adoption within your organization. This is where your organization has tools in place and is starting to adapt processes to get the most benefit from using these tools.

Something I often say in our world of generative AI is this: there should be no starting from blank slates anymore. There should be no blank documents that begin a task. Instead, everything should be drafted based on prior examples, and you should be editing a draft instead of starting from scratch.

This gives you tremendous efficiency in being able to edit what's already there, and you still have the final say in what is ultimately produced because you are the one making the decision on what is finally delivered.

Understanding AI's Core Capabilities

At its core, you can break down suitable AI tasks into a few different categories:

1. Responding to human speech. You can see this in support systems, chatbots, and email responses. Generative AI makes it easy to process a natural language sentence from a human, understand the intent behind it, and map that to processes within a system.

2. Extracting data from unstructured information. Unstructured information can mean human language or it can mean a document, and you can extract structured data such as dates, times, amounts, and labels.

3. Evaluating information based on subjective criteria. Before Generative AI, this was something that required a human, and there was no way software would be able to do it. Now, software can not only do it but also provide a justification, which allows you to audit the information and have traceability for the decision.

4. Drafting information. This one is obvious, but Generative AI can create content such as blog posts, articles, research papers, company reports, memos, etc.

The Tipping Point

After you start understanding the basic building blocks of generative AI and what it's good at within your organization, you'll very quickly find adopters within the organization who start to find tremendous efficiencies in using these within their own workflows.

After you have people using it in their own workflows, it's hard for others to ignore because you will very, very quickly find that they can outpace those who aren't using generative AI by significant factors.

That's when real transformation begins.

Ready to explore how AI can transform your business? GenServ AI helps mid-market companies develop and implement comprehensive AI strategies that deliver measurable ROI. Contact us to learn more.