Insights Think Big, Start Small, Scale Fast

Think Big, Start Small, Scale Fast

As I write this, I want to give all credit to my colleague Tabatha Frozena and Sabrykrishnan Loganathan from RRX on the term “Think Big, Start Small, Scale Fast”.  This phrase as truly been tied to the impact we’ve seen with customers as we’ve engaged them at the executive level through to the business.  I believe this framework has a major contrast with what I’ve seen from simply producing AI-idea-lists and “prompt-kid scenarios”.  Let me explain what I mean:

Think Big

I’ve seen many companies fail to think big enough as they engage AI.  There isn’t anything wrong with incremental ideas, but if all you do is pick the “low hanging fruit”, you’ll likely never make traction toward your real moon-shot.  Every GREAT strategy starts with a great idea and a will to achieve it.  Ask the question, “if I started the company from the ground up, how would I execute on the mission of the business with AI?”  Now, you might not have the opportunity to start over, but you might have the opportunity to enhance your current strategy with AI, vs. just picking the lowest apple.  Now is the time to think BIG and to dream big.

To start thinking big, start with your WHY statement.  Answer “why does my organization exist” and “where is my organization going”?  What can AI enable me to accomplish in the track of that WHY statement that I couldn’t before?  This can be both in commodity AI (tools everyone uses) and mission-driven (uniquely created for your business).  For example, Duolingo exists to spread multi-language capability to every person on earth.  They are already using AI to facilitate translation analysis and preparing content, creating a more efficient scale model if they are truly to serve everyone.  How can your organization think equally big?

The next piece of thinking big is to ask, “what needs to be true” in order to achieve it? Brian Evergreen has done a great job talking about this topic in Autonomous Transformation. So many organizations quickly dismiss ideas because they are unwilling to clear blockers to success. What needs to be true in order for your dreams to be reality? Put aside your feelings positive or negative of Elon Musk for a second.  His initial vision to create affordable electric cars was met with the blocker of (1) there wasn’t an electric car production network and (2) severe lack of charging stations anywhere.  He cleared both blockers by creating the connected network of cars and stations that remains unrivaled today, despite mass production from larger vendors.  This was not his goal however… his end vision was to connect home production of electricity (essentially distributed electricity production) to the same goal of electric vehicles.  All of this has yet to hit full fruition, but incremental steps have been hit along the way.

The second and certainly more ambitious vision has been to create a multi-planetary species, with the affordable spaceflight being a means-to-an-end. For more on this, check out a great article on Wait But Why.  Space X was created for the purpose of enabling commoditized and cost-effective spaceflight, while building toward the long-term goal of hitting the goal of making humans a multi-planetary species on Mars.  Notice how the vision exists, but the incremental goals to get there were more attainable (if you can call landing a spacecraft vertically after launch attainable).  Compare this against the namesake of “moonshot” the Apollo missions, which also incrementally sought a goal (landing on the moon) but took steps bit by bit to get there.

With all this in mind, the most important thing of an AI effort is to create big ideas for the organization, but then start on those ideas incrementally that build toward the big idea.  The focus being ideas that gain ground toward it, not ones that are simply adjacent.

Start Small

The next step is to start small.  The interesting thing about this step is that many companies have mis-interpreted it as “start with just a low hanging fruit”.  Often that low hanging fruit has nothing to do with the big idea, which is a mistake as those initiatives tend to get cancelled.  The second failure I’ve seen when starting small is that the success or failure of the individual effort is perceived positively or negatively by the organization as a whole as a judgement on AI in its entirety.  This goes back to “think big”… don’t let the first effort be a definition of success on your entire strategy.  This is why “fail fast” is necessary in the context of any starting point. The best companies encourage innovation, testing, and adjustment toward long term outcomes.  Determine what will work, what won’t work, and make progress toward your goal with value provided along the way, not just at the endgame.

The ultimate goal of your “start small” first steps is to show progress, learnings, and movement that continues the momentum.  For instance, if I had a dream of participating in an Ironman Triathlon, I’d start with a Sprint to understand the sport and its elements.  I might even do some incrementally larger events on the way there.  The thing you wouldn’t do is treat a failure in one event (like a flat tire, or a cramp, or a slow result) to be a definition of your opportunity to succeed at the entire goal.  The best coaches will see that event result and guide you to push through toward your goal, not quit at the first sign of struggle.

Another similar example to the first is when funding is prematurely cut off of a small effort that shows initial progress before it gets to an operationalized state.  I’ve seen organizations start a good idea, with good results, only to pause because the first iteration wasn’t 100% successful.  Organizations need more persistence toward an end goal than they might show other efforts in the tech organization.

Scale Fast

The last step is to scale fast, but it starts with the big idea and its translation into skills across the organization.  Scaling fast is built on the goal of democratize the access to AI tools in the organization under the context of governance.  This is a balance… because you can’t govern what doesn’t exist.  A company also needs a framework to operate within when technology is democratized.   The best organizations know how to partner with the business to drive excitement and outcomes, while at the same time building toward responsibility and governance across the organization.

The advent of commodity AI has made scaling fast in this space very realistic.  Technologies like Copilot and Copilot Studio enable user and maker communities inside the business to build real and impactful applications of AI that create value for the business.  The skills that come along with AI adoption in Copilot are unlocking capabilities of every person that may not have been used in a long time (such as creativity, delegation, or critical thinking).  Or… the team members might be used to leveraging those skills outside of work (such as in a hobby) and now need to apply them to their day-job. The transition from repetitive work to directive work will be easier for some than others.  It is our responsibility to enable this growth and transition for every person.

The best organizations think of scaling fast as a company challenge, not a technology department or siloed problem, with this being tied to the future of the business.  The best organizations are consistently thinking about the opportunities that abound when AI is leveraged to build incredible outcomes.

Let’s go!!! The future is bright, we have the opportunity and this is our time. There has never been a better time to be involved in the tech community and never a better time to consider the powerful impact tech will have on every person.