Force Multipliers and Points-of-Next

Two phrases have been top of mind for me lately:  force multiplier and point-of-next.

Force multiplier is a term most often associated with the US military and refers to any capability added to a combat force that enhances their tactical superiority and increases the likelihood mission success.

This is how I think of AI.  Intense computing power, trained models, deep wells of information and knowledge fed from the data explosion of the past two decades.  My LinkedIn feed is filled with polarizing content regarding the value and risk potential of this omnipresent concept.

But I look at it the same way I look at any of technology.  It's a tool that enables productivity at a scale that I could never achieve as a single human contributor to any endeavor - especially in my chosen field of data management.

I've spent over three decades being a very productive resource for many different types of companies, but I've always felt that my work has been incomplete and usually taken much longer than I anticipated. 

And I'm right.

When I'm set to a task, given a charter or a mandate, I'm moving at the speed of thought.  Models appear, client conversations uncover vast amounts of functional knowledge and desired outcomes, and prototype queries flow from my fingers like water channeled through a mountain ravine. 

But I can't keep up with everything I've taken in, and I've failed to absorb everything that was covered in these critical conversations.  Moreover, I lack the capacity to effectively share my newly acquired knowledge and insights to others in a way that allows me to organize, delegate, and distribute the effort.  Worse, I've found that bringing too many people into the conversation tends to slow things down because everyone hears and processes things differently - and of course consensus mentality further degrades forward velocity.

Enter AI.

One of my thought partners in my fledgling little business is an AI expert who has spent the requisite hours figuring out how to make AI work as a force multiplier for a number of small things in his personal life and a certain amount in the professional work.  While we were talking yesterday, he had nine agents running tasks that would accomplish significantly more than a day's worth of work by the time we got off the phone.

With that time we discussed what AI could do for this business.  His key point when advising me was that a key strength of AI/LLM platforms is that they remember.  Forget about learning for now.  Think about the power of having a damn near flawless memory of every client conversation.

Force multiplier.

Point of next.  This is a phrase I first heard from George Strickland, and it's been a part of my engagement model ever since. Simply put, you're never done. You've only arrived at the next point - of reflection, inspection, adaption, decision. It's a paradigm that allows you to get started not knowing everything, fail fast, and finish first.

Start training your LLM and AI agents how to recognize the articulation of a business rule or target outcome - in the language of the client.  Feed it source data and teach it how to profile and build robust, scalable, and incredibly reliable models and transformations in dbt or Snowflake pipeline notebooks.

Imagine what two people - one product manager / data leader and one AI engineer - could accomplish with just those few added capabilities.  Force multiplier.

Point of next.  Data visualization.  Conversational BI.  Built on the point of previous.  Force multiplier.

Get where I'm going here?  Models, lineage, a semantic layer - all enabled and made accessible  for review and refinement in a fraction of the time it takes a typical data team or consultancy to deliver the first draft solution proposal.  Points of next.

We're not there yet.  Not even close, but it's long past time to take those first, small steps.

Point of next leads to real force multiplication over time.  When will this industry stop chasing dashboards and easy buttons and embrace a sensible, measured path forward - taking advantage of what AI already does well?