What Leaders Need to Know About AI

AI is ready to be the primary worker in your business. The technology is now at a level of maturity that allows us to get things done with the same level of quality and consistency you would expect from a human. This means that your human team members have the opportunity to step into new roles as curators, reviewers, and managers of the AI workforce, unlocking an unprecedented level of productivity and innovation that we have not seen before. I call this going AI Native.

Companies that recognize this shift and move toward becoming AI Native will win their category. The ones that don’t will fall behind.

The Big Shift

“I really enjoy logging data into Salesforce” — No one. Ever.

If you take a close look at the tasks in any of your business processes, you will notice that they belong in one of three categories: operate, create, or innovate.

Operate tasks are things like compiling reports or doing data entry. Tasks like producing a summary of your conversion stats by channel or logging data into Salesforce.

Create tasks include writing blog posts and drafting personalized emails to prospects. These require a degree of originality but still follow patterns.

Innovate tasks are where the work requires imagination and thinking outside of the box. Things like coming up with feature ideas for your product to test with customers.

Teams spend most of their time in Operate tasks and the least amount of time in Innovate tasks. With AI, you have the opportunity to invert this. Considering that Operate tasks are the ones that create the least value, and create/innovate tasks create the most value, this inversion unlocks a huge amount of value.

The big question is, how do you guide your team to unlock value using AI? There are many steps to this journey. The first step is to develop a foundational understanding of what AI is, what it can do, and its limitations; this is what leaders need to know about AI — I’ll cover these topics in this write-up.

What is AI

The easiest way to think about AI is as a formula. Training a model is the process of asking an algorithm to discover the formula by looking at the provided input data and output data. Compare this to traditional programming, where the developer writes the formula by hand, line by line. With AI, the formula isn’t written explicitly; an algorithm learns it from patterns in data. Inference is the process of applying this learned formula, now stored in the model’s “weights” or “parameters”, to a given input and providing an output. More parameters, arguably, better the formula; and this is the reason model providers tout how many parameters their new model has.

The recent AI boom is powered by two unlocks: word embeddings and the transformer architecture.

Word embeddings, also known as vectors, are how we represent text as numbers. Think x, y coordinates. Now imagine instead of two dimensions (i.e., x and y), having thousands of dimensions that numerically represent information about a word. With this type of representation, we can mathematically discover words and phrases that are semantically closer to each other, which means that with enough data, computers could map which words and phrases go together.

The next big unlock is the transformer architecture. This is the architecture that powers most modern AI systems. At a high level, transformers work by breaking down the provided input into tokens, representing those tokens as numbers using the word embeddings we discussed earlier, and then learning how those tokens relate to one another. The breakthrough was the attention mechanism, which allows the model to weigh which words are most important in relation to each other, even across long passages. This means the model can track context over many sentences and make connections in a way previous approaches could not.

For you as an executive, what matters is that transformers scale. By training on massive amounts of data, they can learn not just vocabulary, but structure, reasoning patterns, and workflows. They don’t think like humans, but they are able to mimic the outputs of human reasoning in useful ways. They are very effective probabilistic next-word predictors. That is the foundation of everything else AI can do.

What Can AI Do

In my workshops, I like to compare AI to a power drill. Its true power isn’t in the drill itself; it’s in the wide variety of "drill bits" or core capabilities you can attach to it. While most people are focused on the most obvious bit, Generate, the real potential is in knowing which bit to use for which job, and especially, how to combine them.

Here are the essential "drill bits" every leader should have in their toolbox:

Generate: The ability to create new content, from text to images.

Synthesize: The power to distill complex information, like turning a 50-page report into a one-page executive brief.

Transform: The often-underused ability to "shape-shift" data, like converting a spreadsheet into a JSON file for your product's API.

Organize: The skill to classify and tag information, from identifying sensitive data to automatically labeling marketing content.

Reason: The capacity to act as a thought partner, validating a new plan or suggesting alternative approaches.

Retrieve: The ability to find the most relevant information from your own internal knowledge base to answer a specific question.

Interact: The power to drive proactive engagement with users.

Orchestrate: The ultimate capability to connect all of these steps into a single, automated workflow.

These are not isolated tricks; they are a powerful toolbox. But the real magic, the part that unlocks massive leverage, happens when you start combining the bits.

This is where you graduate from simple tasks to building a true operating system.

Let me give you a concrete example from our own work: killing the "Jira Tax." We used to spend 30 minutes every single day on the administrative chore of creating tickets after our team huddle. Here's how we combined the "drill bits" to automate it:

First, the AI uses Synthesize to listen to the meeting transcript and identify the core action items. Then, it uses Reason to determine the appropriate owner for each task. Finally, it uses Orchestrate to connect to Jira's API and create all the tickets automatically.

What used to be a manual chore is now done in the background, reliably, every single day. This isn't just a time-saver. It removes a source of dropped tasks, improves morale, and increases the velocity of our entire engineering team.

That is the difference between using a single AI capability and strategically wiring them together to create real enterprise value.

AI Automation Levels

I use the framework of automation levels to make it easy to plan, communicate, and manage the automations. There are three levels. I call them L1, L2, and L3.

L1: These are direct task executions that you can implement using prompt templates. Imagine tasks like drafting a blog post from an outline. What I mean by direct task execution is when a human directly provides input to AI and receives an output in return — this is an important distinction. This means you are using AI to assist you with something you are already doing. You are in control of input and output at all times.

L2: These are instruction-following automations. Imagine compiling a competitor research report to share with the team at the end of each month. If you were to do this yourself, there is a distinct set of steps you would follow: take each competitor, search the web for product announcements, synthesize the product announcement based on the impact on your product or services, compile the notes from all competitors, draft a report outlining the findings, and share the report with the team via email. You can automate each of these steps in sequence with a mixture of AI invocations and tool invocations. For instance, you would use tool calls to extract your competitors' lists from sources like your CRM, browse relevant websites, collect data, and then leverage AI to synthesize product announcements, translating them into insights that matter to your team. Here we have a clear set of steps and instructions that we follow — this is the defining characteristic of an L2 automation.

L3: Finally, we have the goal-pursuing automations. Imagine providing a goal as your input, along with access to a series of tools and resources, and letting the AI figure out which tool to use when, to optimally reach the goal. That’s what L3 automations do. Let's take the example of getting 500 trial sign-ups for your new product. Imagine an agent that has access to many sub-agents — email copywriter, landing page deployer, ad copywriter, ad campaign manager, and so on. An L3 agent would craft a plan involving one or more of these sub-agents, orchestrate it, and then monitor and optimize the plan to achieve the goal of 500 trial sign-ups.

Each automation level has its place inside an organization. I recommend using L1 automations for Create and Innovate tasks. L2 and L3 for Operate tasks. Consider L3 for low-risk workflows and L2 for the others.

Practice applying this lens of automation levels to every workflow that you come across. You will start noticing how abundant AI automation opportunities are inside your organization.

Risks

AI offers great promise and stands to change the way we work forever. However, there are considerable risks. Any organization deploying AI should implement a plan to mitigate these risks through training, processes, and systems.

Misuse: AI is good at providing an output, no matter what you throw at it. In many instances, a surface-level scan won’t tell you whether this output is good or bad. Almost always, AI outputs sound “truthy”. This is a tolerable aspect of working with AI when AI is used properly. By properly, I mean you trust your process over tools. You use AI in a scaling motion of a validated process with the appropriate checks and controls in place. AI misuse is when you throw things at AI and accept the outputs without a proper process to responsibly integrate AI. I have seen teams that are in a hurry to deploy AI over-rely and misuse AI. This type of misuse could mean a hundred new blog posts that all read hollow and will eventually damage your brand reputation, or worse.

Accuracy & Consistency: The two aspects of accuracy and consistency go hand-in-hand. An important thing leaders should understand is that the accuracy metrics from the training and evaluation do not truly reflect the actual real-world accuracy. Based on the inputs and context, you may see inconsistent behavior from AI. So, it’s important to have frameworks in place to monitor accuracy in production and make these frameworks capable of ongoing monitoring to make sure the AI is consistently performing at a minimal acceptable level.

Data security: When you use commercial LLMs from the likes of OpenAI and Google, you are transmitting your business information to another company. The more data you connect to your AI systems, the harder it gets to control what leaks and what doesn’t. There is little comfort in terms of use, since we have seen how quickly companies make 180-degree turns on their terms. If you are in a regulated industry or handle sensitive information, it’s important to think through how you would use third-party LLMs and the associated risks. There are mitigation strategies like private deployments, permission mapping, and governance systems to handle these data security challenges.

Bias: When you use an LLM, you take it for all that’s good and all that’s bad. Bias is baked in. Models reflect the data they are trained on. If that data has blind spots or prejudices, the model will too. Especially in areas like hiring, finance, or compliance, bias is a serious business risk. You can address bias through model selection, alignment, and guardrails. If you select a model from a company that closely aligns with your own ethos, you are more likely to get a model that introduces a lower risk — but this is not a given. Other approaches you could consider are fine-tuning a model to align it with the use case and introducing guardrails where you have “monitor models” watching out and flagging responses that show bias.

Hallucination: As I described before, AI is good at predicting the most probable next token. And, sometimes this means providing things that are probable but not true. This makes it important to cross-check and validate any factual information you get from an AI system and have the right processes in place, like grounding in search and effective context management, to reduce the risk of hallucination.

AI comes with its own risks, just like any other technology. But the benefits of adoption far outweigh these risks — especially when you implement risk mitigation and the right guardrails.

Conclusion

AI is no longer a research experiment. It is ready to step into the role of primary worker in your business. By understanding what AI is, what it can do, and where it falls short, you as a leader can guide your team to unlock new levels of productivity and innovation while protecting your business from risks.

The path to becoming AI Native takes more than just dabbling in AI pilots. It takes a structured process. You need to catalog and document your workflows, identify the optimal automation methods, estimate the value, prioritize by forecasted outcomes, create risk mitigation plans, align your team, create an experimentation framework to rapidly iterate, and quantify and communicate the ROI.

I’ve spent two decades helping companies modernize their operations using technology and the last ten years working exclusively with AI. That experience has shown me what works and what doesn’t. It’s distilled into the workshops and playbooks we deliver at DeepModel.

If you want to move beyond experiments and set your company on the path to becoming AI Native, the fastest way to start is through our Launchpad program—a three-month engagement where we use the DeepModel platform to build your workflow catalog, design your AI operating framework, and implement AI automation for a selected scope to demonstrate quantifiable ROI.

Get in touch to learn more about how to lead your company into the AI Native era.