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Leadership Challenge

Yesterday our Founder & CEO posted about seeing more “skill cards” from leaders. Today he wanted to speak more to that. We agree – and we’re not just saying that because he pays us!

Skill accomplishment posting are fantastic – and those getting those achievements should be proud of the hard work & dedication they’ve demonstrated – their accomplishments will go a long way in their careers and professional journeys. We like seeing those pop up in our feed on LinkedIn.

But what about our leaders? Granted it’s a tougher to measure in the same way as technical skills, which is interesting because it’s obvious, to all of us, what the difference is between a great leader, a good leader and a leader who needs some more time baking the oven. No, not literally, gosh, more coaching or mentorship.

We’re convinced that classes (example from a MBA) and books (some listed below) are all great ways to start. We’re also equally convinced that for leaders to be successful, coaching & mentorship are equally, if not more, critical – and often missing in the professional work.

We’ve heard “that person was great at their task work, but gosh, they’re a horrible leader, how did they ever get promoted?” so often. Because they became great at their task work and got thrown into the deep end of the leadership pool with no support. Expecting them to do more than wade in place is a Greek tragedy waiting to happen.

So – how about a leadership challenge. Brag about your leaders, call out what they’re doing to help, what they could do to help. Leaders, listen. It may be uncomfortable, but it will go a long way to creating an empowered culture of trust, empathy & resilience.

Any takers?

Falling, Failing & Flailing Forward

We’ve all heard of falling forward when it comes to entrepreneurship, and innovation.

Recently Simon Sinek mentioned choosing different language -falling vs failing – because it matters to shifting our mindset. No matter how much we talk about not fearing failing, that concern remains, especially in larger organizations that continue to evolve what it means to them to innovate – to build centers of entrepreneurship within. They are still operating under cultures that don’t reward, or encourage failure!

When one falls, intentionally or otherwise, our reaction, especially as leaders, is to help them up and continue to encourage them. Makes sense, right?

Look, as innovators & entrepreneurs we have to expect that we’re going to fall. None of us are prescient or perfect – but, and we’ve said this repeatedly – mindset matters. So the simple change form failing to falling makes a lot of sense, removes fear and drives engagement and encouragement to take those risks.

Now – and we always coach this regardless of the size of the client we work with – it’s still about intentionally taking smart risks. So to this we’ll add – don’t FLAIL forward. Don’t just do & do & do to see what sticks, and then think it’ll just be a pivot.

You know what we call something that keeps pivoting – in place? A top. It wobbles to & fro and doesn’t really make much forward progress.

By all means, try, and try, and try – entrepreneurship for us is about intentionally taking risks until we achieve success. So now to this we’ll add – don’t fail, don’t flail, but fall forward.

You’ll be helped up, you’ll be encouraged, you’ll learn and you’ll always make forward progress.

BELYONS OF ROWS!

Let’s talk about selection criteria for that AI\ML platform you’re considering. (ed: yes, I know, I said let’s stop calling it AI\ML, ships turn slow)

We’ve been seeing a lot more ads about different AI platforms out there and they’re typically along the lines of “…crunch through billions of rows of data in 9 seconds flat!” – or some such. While those are great metrics to consider, they really need to be second, or even third, on the list of what you’re thinking of.

Here’s how we approach ML platform selection with our customers.

First – you really want to consider how the platform ingests all the data you have. It used to be there was a lot (a LOT) of data normalization – and that shouldn’t be the case anymore. Make sure you understand what data formats you can export (or which and how you will allow connections into your sources) and the capabilities of the platform to ingest, understand and draw inferences from that data.

Second – what questions are you trying to answer? It’s great to just look forward to being delightfully surprised by unexpected pleasant surprise correlations – or causations – and be able to tell between the two. It’s even more important you set out with a list of questions you want optimal answers to and find those pertinent patterns in your data.

Third – accuracy! Even after training the model, is accuracy being actively, reiteratively measured and are you ensuring you keep training your model as you discover new, pertinent, patterns? This is critically important to ensure you’re tracking to reality and new emerging and changing old patterns. Data, business – the world – is never a static snapshot in time. This, by the way, is where a lot of ML projects come up short – the process of ensuring reiterative training doesn’t occur. ML is not a one shot.

A quick shout out to ClosedLoop.ai. No they didn’t sponsor this post, we just really like their current direction for healthcare!

Finally, keep in mind that most ML platforms today are focused on post-encounter or post-service transactions. If you have pre-encounter or pre-service questions or patterns you are looking for, consider something that’s more of a heuristic platform. We’re still researching and sorting through those now, and tinkering with one of our own making. We’ll make sure to update when we have something tangible.

Thanks for tuning in!

Cultures & Software Engineering!?

Let’s talk about cultures & software engineering!

Uh … What?

Hear us out. Every company has its unique culture, even if there’s cultural similarities between companies in the same domain or vertical. They’ve developed and evolve over time.

Just like most mergers or acquisitions fail because culture differences are not tended to, most software engineering projects carry that higher risk, or cost, or longer time-to-value, because software engineering has its own culture and if it’s not tended to – or if it is expected to operate under the same culture as the organization – you’re going to run into significant challenges.

Almost every time we’re called into a software project at risk, we see cultural clashes that aren’t being tended to which are leading to process, communication, productivity and efficiency breakdowns. The antidote – work harder, work more – is counterproductive and harmful.

We address those issues and we also address the cultural differentiation at the root of those challenges. If we don’t, those issues are going to keep coming up and future software projects will be at risk. We’ve shown, first hand, it’s necessary and indeed possible to do so.

If your company is struggling with their own software development projects – please! – call us. We’re happy to spend an hour on the phone with you, getting to know you, your project and sharing pertinent tips to get you started in the right direction.

Reminder: Take Breaks During Your Work Day!

We’re talking to your schedules today. They’re back to back with meetings – where’s the you time in there? We don’t take breaks at work, especially when we’re working at home. Those long hours working a task staring at your screen slinging code or laboring away at a project are actually gradually reducing your effective productivity – and sapping your energy.

The solution is simple. Create a new habit of taking frequent breaks. Once an hour if you can, but no less than every two hours. Get your blood moving, move your energy, set your brain on simmer and breathe with intention! You’ll find yourself a little bit invigorated and sometimes you’ll get that “Aha!” moment you were looking for.

Try it today, wrangle that schedule, create some break times for yourself. We’re eager to hear what difference you see!

Artificial Intelligence is Neither

Artificial Intelligence is neither.

Let me explain. There’s been growing interest and commentary in the last several years on Artificial Intelligence (AI) and as a wannabe writer, relapsed polyglot, software engineer in remission and AI enthusiast, I would like to share some insights, stop budding AI luddites in their tracks and start a movement to reduce popular use of terms such as AI and Machine Learning (ML) – because, well, they’re neither. For the sake of clarity I will continue to use those terms in this article.

Will we create true Artificial Intelligence (or Sentient) systems? The answer to that question, as to any other is, yes, with time, energy and resources. If we can imagine it, we can make it happen. But we’re definitely not there and don’t have the hardware or software for it. It’s zeroes and ones, and even if it’s fuzzy logic or quantum compute, it’s still simulated via zeroes and ones. Intelligence isn’t bound to binary.

Ex Machina - Film4/DNA Films

Today’s AI systems are typically (but not always) statistical model number crunchers taking advantage of utility compute power and freely available oceans of data to find patterns and trends that meet some desired outcome, in the hopes of being able to predict those desired outcomes. One great example of this is ThyssenKrupp’s MAX Predictive Elevator Maintenance Program. It’s a bunch of some wicked statical math and some really cool modelling. There are, of course, other types of AI systems – as mentioned below.

In fact, nearly every example you can think of today isn’t intelligence – it’s really not. Even when it looks like it. For example, autocomplete – isn’t intelligent or smart (but it is cool!). Something like autocomplete is number crunching through immense amount of data (thanks Cloud Compute and everyone who accepted EULA’s without reading them) to generate likely outcomes.

But it learned what you really mean to type when it autocompletes duck!

Not really.

What it did was notice that you kept changing duck to puck when texting with your hockey buddies. Then duck & puck to cluck with your 4H alumni. Then duck & cluck to puck back again. So now, sure, it seems like it’s learning and sure it seems smart because now it knows to send puck to Wayne and cluck to Jimmy instead of the ever confusing duck to exactly no one ever. But was that a process of learning? Pedantically, yes, but it’s not really learning when you’re applying it to a narrow problem in a single domain. What you’re doing is (math & models!) changing desired outcomes based on who you’re interacting with. There is a discrete, bound box it operates in. There is no knowledge or skills acquisition.

Wizard of Oz - MGM\Disney

Are there companies that have\are creating AI platforms that you can use? Absolutely! After you train them (lots of data!) and specify (bias) towards your ideal outcomes, they’re a cost effective on-ramp to leveraging the power of these tools. They’re still not AI, and it’s a stretch to call them ML. Look behind the curtain and you’ll find … Oscar Diggs! Same thing. But what a fantastic business model. Now, if I can take one of those systems trained for predictive maintenance for Sunbelt’s construction equipment (made that up) and drop it into Pinch-a-Penny’s pool proactive summer maintenance program (made that up too) and it remembers both … ? Well it’s not going to – and really, nor should it because these are tools and platforms we use to solve problems in a specific domain. And because these systems cannot inherently gather knowledge and skills, it’s not quite right to call them AI or ML systems. Those platforms are great for democratizing those tools, but they’re still neither AI nor ML.

A.I. Artificial Intelligence - Warner Bros

But they’ve created Art! Music! Faces! Again, let’s think about this. They had to have a massive set of data to ingest, real world approximations and a model (there’s that word again) to work within to create something that sounds or looks better than what a room full of monkeys smashing away at keyboards, easels and instruments will create.

Which, if you really think about it, is sort of like the real creative process only with less discordance and poo flinging. I’m pretty sure that’s not how Da Vinci, Prince or Austen created.

So what should we call them? Well, Enormous Cloud Compute Assets Statiscally Calculating and Modelling Things is a bit of a mouthful. And there’s a collection of nicely descriptive names for simulacra “AI” systems such as Expert Systems, Neural Networks, Genetic Algorithms, Blackboards, etc. But they’re still, generally, the same thing at the end of the day. Massive gobs of data, statistical or simulated modeling or meta/heuristics to achieve some desired (and therefore previously known) optimal solution.

I propose we just abbreviate it all down to Simulated Modeling (SM).

Because, Artificial Intelligence, is neither.

(… with apologies to our future sentient machine overlords, please don’t skynet us)

Why Aren’t You On the Cloud?

Why aren’t you on the cloud?

We’re getting tactical with you today. There are a few, and only a few, reasons for your enterprise not to get to the cloud. Stop hosting your own virtual machines (VMs) or hosting them on someone else’s infrastructure, pick a cloud provider and get “your mess, for less” on a cloud provider’s Infrastructure as a Service (IaaS).

Then have a plan to incrementally move from IaaS to Software or Platform as a Service (SaaS/PaaS). The recent ransomware events area a clear indicator that even security vendors are having challenges, and will continue to do so.

Carve out those systems that cannot be moved – then pressure those vendors to get on the ball, connect the rest of it up to a cloud with connections back to those legacy systems as needed. The work involved is well worth the payoff both in short and medium term benefits. You’re getting a layer of unmatchable protection, resiliency and elasticity.

If you think you’re a carve out exception, or you want to know where to start, first three folks that contact us get an hour on us to discuss and sketch out viable ideas.

So …Why aren’t you on the cloud?

(EDIT: This posted right before a widespread Internet outage that same day. Irony perhaps, but the point stands!)

Entrepreneurship in Trying Times

We’ve been reading several articles (and we’ll post one in the comments) about the economic recovery, entrepreneurship and employment and we’re seeing some alignment with anecdotal evidence both first hand and here on LinkedIn. One of the things we do at 1Focus as a way of giving back is give back to young entrepreneurs with prescriptive guidance and direction, especially when it comes to technology.

We’ve see a significant uptick in solo entrepreneurs, and at least 20% of them have proven to be nimble & adept at pivoting – a greater version of which we incorporate in our global client engagements.

This is where Diffusion of Innovation Theory comes into play (link in comments). With all size of our clients there is often a concern of competition. But this is good – you don’t always want to be first, it’s ok to launch with a small pack of competitors. That competition will drive differentiators and a higher level of service – both of which are opportunities to create distinct advantages.

If you feel the drive to start a business – do it – with intention! And remember, competition is good, for your business and for your consumer and it allows you to compete on more than just price!

We’re big believers in opportunity, so, we’ll give the first three folks who ask an hour of advice – gratis.

What are you waiting for?

Celebrate the Small Wins!

Celebrate the small wins on the way to the larger victory!

Too often we’re quick to self criticize and shrug away celebrating ourselves. This would be a great new habit to build!

Whenever you accomplish something, even a small step, take a moment, take a breath and celebrate it. Stop for a moment and congratulate yourself. It’s an important way to build self motivation – which will always carry you.

And if you falter? Celebrate the lesson that mis-step taught you and avoid making the same error. Relish the fact that you get another shot.

We’re still giddy at yesterday’s featured post, even as we’re focused on the next milestone. So take a moment today, and celebrate one of your accomplishments. Then do it again tomorrow & the next day.

Stop the Insanity of Constant Work!

(This is in response to the excellent Wall Street Journal The Pain of the Never-Ending Work Check-In by Rachel Feintzig. It was a featured post for that article at LinkedIN too!)

I mean where do I even start with this … an apt subtitle for this would be how to do everything the way we’ve always done it and be surprised that it doesn’t work.

With yet another wave resurging globally (and vaccinated people exhibiting less severe symptoms and vastly reduced need for hospitalizations), for society to function, businesses need to function, for business to function, they have to learn new modes of behaviors and interactions. At least until we get to a point where energy is widely available & free and matter converters are ubiquitous (Star Trek reference), these are the facts we are presented with.

The video’s we’ve posted and will continue to post over on 1Focus address exactly this, so let’s do a top 3 do’s & don’t’s really quick.

DON’T keep transacting business the way you always have.
DO expect, accept & embrace the need to change, and make adjustments, so you’re transacting business in an intentional, impactful way.

DON’T let business happen to you – that’s so incredibly risky & dangerous – especially since these factor are all known knowns.
DO come together – stop, drop and roll, create a plan, stick to it, tweak it as you go (I’m looking at you perpetual meetings).

For Leaders:
DON’T constantly inspect your people, if you’re micromanaging and people care is in the rear view, you’re not doing 50% of your job! Ok, the hell with it – here’s a shocker – 80% of your job as a leaders is seeing to the well-being of your people, removing impediments and collaborating on progress. It is NOT to monitor or micromanage them!
DO provide and project that sense of calm and, you know, authentically caring about your people. People can tell.

For Contributors:
As with real life, you have to set limits, we ALL have to set limits. Here’s a shocker – after just ONE 50 hour week your efficiency is reduced, after TWO you’re burning out and you’ll have to work ever more to get the same results.
MORE importantly, you’re not the best you for yourself, your family, your friends, or your job at that point – and the viscous cycle will start.

Look, this is why we talk quite a bit in our books about coming together. We’re not pushing some kumbaya new-again hippie fad. The only way any of this succeeds, the only way we break old toxic patterns and create new healthy ones is by coming together and making a change together.

And we’re sitting here on a MOUNTAIN of experience. This is what we do. It’s the HARDEST part of what we do – not the tech, not the process, but gradually, incrementally, consistently bending work spaces, leaders, individuals and cultures into a better place. More happiness & resiliency leads to more productivity & retention & less crisis.

It’s your choice. You can do what you’ve always done, or you can decide to take this opportunity to make a lasting, permanent change that’s healthier for everyone, now, and in the future.