This short essay is focused on helping legacy systems adopt the incoming wave of productivity through the adoption of AI-workflows. I've gone from a 6-month development cycle, to a 3-month cycle, and now I can move through an entire product vision, roadmap, and simple MVP within 1 month. With promising results. I think many others could do the same, and should.

Over the last 6-months, closer to a year, I've had the pleasure of building, benefiting, and discussing the advancements of AI-assisted workflows. It's likely not a surprise to many keeping an eye on the market, AI is either touching and/or disrupting practically every industry and role. If this catches you by surprise, it's likely because you're safe from this technology or will be replaced by its shear utility.

AI-augmented workflows change the entire production cycle of product development. Even the sales cycle. Yet, I don't think the change is happening fast enough. The reason, human-biased systems are the predominate bottleneck, for understandable reasons. However, I don't think AI-augmented workflows displace human work, they accelerate them.

Allow me to explain.

If you desire to develop a product idea, depending on the size of your team and organization, you'd apply some sort of timeline placing the viability of testing or launching this idea somewhere in the future.

For example, a simple feature to include a certain type of data point, from a new data source, in an existing product may take 1-6months depending how much your engineering team has on their backlog.

Other variables impacting time:

• idea origin eg. client-conversation, business-team, product-team, etc.

• product scope eg. why are we adding the feature, who is it impacting, what benefits is it providing, etc.

• data scope eg. what are the data structures, how is data privacy and security impacted, does the data provide generous benefits, etc.

• engineer scope eg. are there new technical complexities introduced, does it fit into current systems, the typical performance, security, and scalability concerns, etc. And many more domains which can impact the speed, cost, and production of a simple idea.

In a traditional team structure, these domains are outsourced to individuals, and teams, with "specialized" knowledge of the scope to be measured. At least, we hope they are specialized as many writings have shown this may not be the case. In this setting the hierarchy of information is very noticeable. Development many times happens in a somewhat linear process. Many times, a bottleneck in one domain holds back the testing and launch of the next great product.

These silos of domain is what AI is attacking and breaking down. Flattening the illusion of "specialization" and allowing organizations to operate in a more flat-structure, something closer to NVIDIA. More easily allowing people to gather around the development of ideas and not the development of specialization or domains.

This leads us back into why AI-workflows don't replace human work, instead, accelerates them.

The new product development cycle allows for cross-domain integration to happen more seamlessly as the capabilities of all individuals become generalized by AI. Flattening the product development cycle.

These capabilities in acceleration should allow for companies to launch more products, with greater quality, with less time and cost.

For example, a simple feature to include a certain type of data point, from a new data source, in an existing product may now take 2-4weeks depending how much generalization any team is able to handle. We are no longer bottlenecked at domain specialization, we are now only bottlenecked around communication. Communication is actually a bigger hurdle when humans feel like their work is under attack or egos get in the way, as I have encountered many times.

The more important outcomes is that product teams can now dig into data, legal, engineer scoping, or any other combination of a traditional team structure. Cross-checking with specializations as needed but not holding up the cycle of idea-launching.

Idea-launching is the life blood of a successful company.

The main factor getting in the way of most teams today is the trust structure that is built within the organization. Something I can dive into more deeply at a future time. The root of the idea of AI-augmented product launch being the new product development cycle, lies with individuals or teams communicating within high-trust environments with the desire to launch ideas, not gate-keep their role, title, domain, or ego.

We need more companies to invest their time and money into the individuals wanting to adopt this AI-augmented workflows. I predict the orgs adopting these strategies will see drastic increase in their top-line, bottom-line, and a growth of a positive-sum culture, internally and externally.

Steps organization can take today:

1. Enforce ChatGPT, Cursor, Claude, or other similar products as integral tooling

2. Have cross-function team meetings allowing generalized ideas to become "fact-checked" with specialized functions, careful not to debate based on seniority

3. Deeply communicate the culture shift towards flat-structure communication and work, to mitigate ego-bruising, and maintain respect

4. Keep meetings extremely short (<20mins), or very-long (45mins+), only communicating idea-specific topics originating from the process of AI-workflows

5. Maintain conversations within an explore function vs. exploit function separate, explore deals with a lot of uncertainty, exploit deals with deep specificity "the details", if you don't know which one you're in, default to explore, pin, and move on.

6. Empower everyone to think through the entire cycle of what they are working on, note-take where they feel stuck, "stuck-ness" is domain-specialization kicking in, use AI-workflows to unstuck

7. Enable an adoption strategy, ladder-adoption of teams or individuals, to being the transition of flat-structure development with AI-workflows

8. Err on the side of 'everyone is doing their best and has the best intentions' till you reach the other side, when you reach the other side, you'll know who needs to go or not.

God-speed to all in the accelerated future.

(This essay was entirely written by my own 2 hands)