Practical AI: Changing the way we do business

Explore the power of AI in transforming business processes. This article delves into the shift from improvisation to diligent experimentation in start-ups and how AI can elevate operations, from automating simple tasks to orchestrating complex processes.

Practical AI: Changing the way we do business

also titled "Elevating business processes with AI and surviving to see the rewards".

The start-up's cardinal sin is confusing improvisation with experimentation. It's simply too hard for teams of people to learn collectively and move fast while they improvise; there are too many variables and outputs. The beauty of diligent experimentation is that you can iterate your parameters until you get the output you're looking for. For that to happen, you need to: 1) Record your parameters, 2) Record the conditions of the experiment. I've seen too many companies do neither of these things and get frustrated when they realize the curve of their operational costs grows faster than their revenue.

This extends to much smaller endeavors, from product design decisions to making changes to GTM strategies and motions. Next time someone in your team argues for a change "for shits and giggles", shiver with fear.

How is AI related to this? I firmly believe AI, and LLMs more specifically, can and will change the way we do business. However, the value you get from AI is proportional to your level of sophistication. It's hard improving a business process you don't have thoroughly mapped out, whose stakeholders shift, with volatile outputs. Under pressure, I've seen companies quickly and very publicly boast about the benefits of AI in their business, but the benefits are so shallow and the costs so poorly calculated.

Lowest value out of AI: automating SDR emails

I've seen a few dozen companies do this, and another few creating startups around this notion.

Sure, ChatGPT writes decent emails and text – it is a language model after all —, but it's such a low-value effort, with such a lack of defensibility. Anyone with access to OpenAI's web interface can do it; SDRs can do it themselves. Someone with a few weeks of coding experience can even enhance this with better workflows, Linkedin scraping, semantic analysis of the conversation so far, etc., and create a Gmail plugin.

Think about cost savings/revenue opportunities. You're taking a job that is largely already automated and templatized and adding another lazy layer of automation on top. You may be crazy, or greedy, enough to increase your SDR's quotas because you gave them access to a glorified letter writer.

Highest value out of AI: structuring fuzzy inputs, navigating, and executing business processes

Hot take: most of a company's time is spent structuring data, translating semantics, communicating those semantics, re-translating them, and transforming structured data into other types of structured data. In other words, most teams are just an old-school inbox and an outbox with some process in between.

Chances are you have a talented, intelligent person in your team who's doing menial, tedious processing work all day. That's a shame. The goal of AI should not be to get that person fired. It should be to transform that person into the architect and supervisor of that process, letting AI do the tedious work, and allocating their time to higher-value tasks and decision-making.

The next generation of business process management (or BPM)

Every business process in your company, from small tasks like approving expenses to larger ones like designing a new software feature and doing user research on it, should be properly designed, modeled, and mapped out. It's not hard. There are many ways to do it: from informally expressing the process as a series of bullet points, with stakeholders, inputs, and outputs for each step, to using established techniques such as BPM or Six Sigma.

I can feel a few readers scrunching their noses and thinking: well, you're just not going to move fast. This isn't agile. This is how IBM did business in the 40s. My counter-argument is that you're going to move faster by lightly modeling your processes than by improvising them every time you hit an obstacle. Trust me, I'm a startup cowboy; I've felt the pain. My instinct is always going to be to improvise, write quick spaghetti code, iterate very quickly. One day I stopped improvising and my experiments moved faster and better. Especially those of you in the early stages: if your goal right now is to research and learn, your business processes are most likely completely unscalable and unsustainable. That's a good thing, but you'll need to scale them at some point. It's going to be really hard if you didn't document them.

OK, so what is BPM? For those who are not familiar, BPM is one of many disciplines that focuses on modeling your company's business processes. After modeling them, it helps you analyze and measure them, optimize them, and ultimately automate them, which is where AI enters the chat.

A sample BPMN diagram for an Employee requesting leave. Source: Visual Paradigm

The example above is a typical BPM diagram for an HR-related process. The inputs and outputs are self-explanatory.

This is simple enough. How quickly and painlessly stakeholders go through this process today depends on how easy it is to fill out the form, submit it, and evaluate it.

I'm arguing anything in a diagram of this type that involves the words "fill", "submit", or "evaluate", should be automated by LLMs. However, the only way to do it is to first map out very well your processes, inputs, and outputs as a company.

Example of simple data structuring - filling in a form.

This is obviously an extremely simple example (that saved the user 15 min of their time). More complex processes will require proper orchestration, validation of inputs and outputs, database lookups, and others.

Orchestration here is the key: LLM usage needs to be narrow in scope and modularizing and connecting different functionalities is the key to a successful process. The idea is that the resulting structures are transparent to the human users, so proper supervision can be done.

Using AI as an abstract thinker to model

If you're in the situation of most companies, who don't have properly modeled processes, fret not. The beauty of an LLM is its ability to extract semantics out of abstract thoughts and structure them.

Example of a meta-request modeling business process after natural language

As mentioned before, a simple example for illustration purposes, but an interesting starting point. You can use an LLM to structure a business process from a simple explanation.

Putting the horse before the cart

If the two examples above seem simple, it's because they are. Companies are a complex web of requirements, expectations, parameters, and most importantly, stakeholders. However, from first principles, proper orchestration will allow us to build progressively more complex automation if processes are properly modeled and documented.

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