Hi Jared, tell us about your role/team at sparks & honey. What inspired you to join the company?

Thanks for the chance to shout about how much I love my team! They’re diverse, brilliant and a great deal of fun. I lead a bifurcated team – with half focused on data science and half building out the product frontend. Q™ is in a sense a major AI play, so we needed a team that was top-notch both in what’s going on in the space now as well as where it was headed. We have data modelers, Python gurus and of course big-picture AI architects.

On the development side, we have some end-to-end developers, an amazing design team and of course QA and dev-ops geniuses. We are over 50% people of color and female, mostly based in NYC. My role is often architectural – looking out for new tech that might fit into the bigger story and helping make sure the design – both from the microservice and experience perspective – is coherent and scalable. As a tech leader, I spend a lot of my time fighting against the big three – technical debt, stagnation and the changing security landscape.

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When I joined sparks & honey almost two years ago, I was excited about the diversity and curiosity that was built into the company’s DNA. That continues to be a key driver of what gets me up in the morning. That done, and our daily mission to quantify and predict culture. The passion and energy of the leadership team sealed the deal.

As someone that has been in the tech industry for the last two decades, what are the biggest shifts you’ve seen?

Now you’re making me feel old. First, there’s some obvious stuff I want to acknowledge and move past – the rise of social, big data, mobile, the questionable peak of digital currency, artificial intelligence. All these things have led to major changes in our industry, but for me, there are three shifts that have had the biggest impact.

First, startup culture emerged at a new scale and we started to embrace a new way of realizing new ideas. We saw this in everything from incubators and boot camps to the very nature of investment and the rise of co-working spaces. Second, we’ve fundamentally rethought delivery. From SaaS to cloud hosting to virtual ad markets, without knowing it we’ve tripped into a world where what companies produce no longer takes up space in our offices or even hard drives. Value is virtual. Finally, and everyone’s talking about it now but really it has been coming into focus for years – our workforces are becoming almost entirely virtualized. Until automation picks up, we won’t see this in the service industry or other more brick-and-mortar sectors, but in the tech space and many other industries, geographical boundaries are simply disappearing. Before all this, I was working with a recruiter who questioned my “NYC Area” requirement by simply pointing out that the best developers are already realizing there’s no need for them to work where they live.

But, the biggest shift I’ve seen in this industry over twenty years is that it has become completely abstracted… and therefore requires more humanity, more creativity, and more trust than ever before. The winners aren’t the ones who own the cloud – though they won’t lose – the biggest winners will be the biggest ideas. And because they can come from anywhere and adapt in real time, scale in real time – they’ll rise almost without warning. Now anyone can play, and though the jackpots are smaller, they’re everywhere all the time. In 2000 if you were to tell a potential employer that you wanted to “Fail Fast” they’d kick you out the door. Today, they’d kick you out if you didn’t.

Please tell us about the recent shifts in Product Engineering and Automation techniques related to software development?

I think we’re witnessing a realignment, a shift into a world of low-code delivery and this is going to have profound effects on how we design new products. The complexities have become homogenized and instead it’s all about the underlying data, APIs, and IP that brings value to a product. This means that product engineering is far more compositional, and connective now than it ever was before. There is still a place for custom, deep-dive code, but it is contained into and connected to pre-built tools.

Add this to UI libraries and bootstraps that give you out-of-the-box experiences that are already mobile-first, already accessible to all users and you start to see a development universe that is both far less risk-averse and far more reliant on the power of experience to create differentiation. This isn’t automation – that’s a different space. A great developer now is a great and dynamic user of tools. Automation – true automation not composition – is rising in the QA/Testing spaces, in the UX modeling spaces and certainly in the security world. Products are being designed for a world of discrete components and being optimized and tested with an increasingly automated set of tools.

How are you coping with remote workplace challenges? What motivates you in these times of uncertainty?

I always smile before I answer the phone and I always translate the word challenge to opportunity. The opportunities we uncovered working remote started with being open and exceptionally transparent with the team from day one.

Even as our company has expanded throughout all this, we had to really inspire confidence about where they were and what we expected of them before we could ask them to do something as mundane as development work in such a trying time. Because of this new almost jarring transparency, our team was able to reflect that transparency back to us.

Schedule fluidity is now the norm.

We strove to hold people more accountable to their time while hearing that fear and stir-craziness often meant that they had less of it.

We worked hard to enforce more work-life balance while also capturing some subtle efficiencies as people found the exact sweet-spot of their work-from-home lives. This was also a time when people were open to experimentation and fun, quirky solutions. So, we funded home-office spruce-ups while at the same time perfecting remote game time and trivia.

We played with our scrum models and agile delivery schedules and created some more effective work routines.

We also trialed new tools and became a great deal better with the ones we already had.

We will go back – likely in a different capacity than before – but right now we’re more effective than we’ve ever been and I’m inspired and humbled by how much my team has grown in this time.

As COVID-19 continues to have an unprecedented impact on business, what gaps have been exposed as it relates to existing platforms and technology? As a result, what are the biggest challenges facing the technology industry now?

So far, COVID hasn’t had the kind of impact that the dot-com bubble or the 2008 crash had, and I don’t think it will. But COVID also hasn’t impacted all sectors evenly.

While my friends in retail and other spaces faced an earthquake, the technology industry only felt a small tremor. There have been some cracks, though, even with that. We’re seeing HR and emotional support infrastructures that were never very powerful being stretched to their limit. This is – above all else – a stressful time.

We don’t have great answers for that. This may not seem like a tech problem, but it really is – developers and designers and data scientists are at a fundamental disadvantage if their heads aren’t in it. The same is true for childcare – the added weight of these things matters.

sparks & honey launched a new SaaS platform last year called Q – what was the impetus behind creating this platform? What gap has it helped to fill/problem is it helping to solve?

Q™ is the answer to the question, how do I measure the rest? With big data and large experience and social platforms, we’ve handed so much of our information to computers. It’s easy to forget that major industries had to go through “digital transformations” to make this possible.

Think logistics before and after SAP, or B2B sales before and after Salesforce. All of these solves measured what was measurable within a company or specific market space.

But what about… the rest? sparks & honey was founded with an ambitious task – how do we measure the stuff that doesn’t live in anyone’s spreadsheet?

How do we understand culture itself? We talk about launching Q™ last year, but in reality, we’ve been incubating this take on culture for almost a decade.

Q™ is just a frontend solution to a mature taxonomy of culture that we’ve used with some of the biggest companies to answer small and tall questions through the lens of cultural forces. The difference at sparks & honey is that what may have looked like rabbit-out-of-hat strategy genius has always been based on quantifiable cultural analysis. Other folks can measure market sizing and user opinions and social media mentions, but we found a way to measure the rest. You’ll see other players in the field who might zoom into a topic – say an ingredient in your soup or the word “sustainable.” That’s what many people think measuring culture looks like – but it’s not – it’s a dictionary’s answer to why a Basquiat is stunning. It’s the what, not the why. Q™ took something huge and fantastic – the world of culture – and applied some of the most brilliant minds I’ve ever known and spared no expense on data and AI at scale to create a symbiotic pair – human and machine – to understand where culture is now and predict where it is headed. The market has always needed a tool to understand culture – it just never knew it was possible.

What industry/type of brand has the most to gain from a platform like Q? Why?

The beauty of Q™ is that every brand or industry can find value in the cultural intelligence it delivers. Here’s an analogy I often reference. Say you have a car brand – every day the top executives look straight ahead at sales of their small SUVs and their practical sedans pleased as punch. Their channels are working. Their sales are solid. Their reputation is stellar.

Q™ is what happens when you stop that executive and say, Hey, look up! There’s a whole universe of culture above and around you. There is so much happening – from circular economies replacing plain old recycling to material innovation. It’s enterprise myopia. That’s where Q™ shines – we give you the tools to look around, to see the rest. And this might come off as a bit starry, a bit squishy – but many large enterprises have innovation labs, incubators and teams dedicated to finding the next big thing. Many even treat these like startups giving them real distance from their corporate home. Q™ isn’t just about recognizing big ideas and preempting disruption – if you look at the interface, you’ll see it looks suspiciously like presentation software. This is no accident – it’s meant to help you scream and shout about change. The enterprise that needs it is the one that cares about change but doesn’t have the muscle memory necessary to react when it’s happening.

Right now, I think the automotive industry, retail and old media are in that sweet spot – ripe for disruption.

How are you and your team using Q and the powers of AI to help clients navigate this global pandemic and the cultural shifts that are taking place?

Before Q™ or any of our tools were going to be of use to anyone else, sparks & honey had to understand the impact of COVID, and racial injustice, within our own organization. We saw the impacts of COVID sooner than anyone else we knew. We were actually “practicing” for our work-from-home scenario while other organizations were still wondering about whether it would ever come to that. That’s because we pointed Q™ and our daily culture briefings to these problems very early on. Not only did we see the health ramification of the virus early on, we saw the mental ramifications. As the calls for racial justice were seeping into the national conversation, we’d already held our first briefing on the idea of Allyship. So, while sparks & honey was adapting proactively, the word just started to get out.

For example, we knew early on that the folks on our team with families would be greatly impacted, so we launched sparks & nanny – a series of virtual experiences for children at home. This kind of thought leadership then seeped into our stable of existing clients as we used Q™ to explore how behavior was shifting – from snacking habits to binge-watching – and we were able to create powerful analysis for many major brands.

The next phase, though, was new clients – some who saw our briefings or heard from our existing partners and wanted guidance. Using Q™ to zero in on specific communities, trends, and of course social conversations, we were able to move quickly to answer questions and talk not just about the now and the next, but peer around corners into possible futures.

Sixth sense: Future of AI: Are the advancements in AI over-hyped? What does the future of this space look like?

AI is not over-hyped, but it’s misunderstood. VR is over-hyped. Until we lose the hardware, there are no applications where the experience is worth more than the foolishness. Bitcoin is over-hyped. The creators were anti-bank fanatics who were solving a problem that most people didn’t see as a problem.

Artificial intelligence solves an actual problem, and the lift for the end-consumer is nil. The problem with AI is that it’s being overused.

AI isn’t going to make my refrigerator more efficient. It’s not crucial in my self-tuning speakers. And 99% of the time, when you’re talking to Siri or Alexa, she’s not responding using AI – she’s just using branch logic.

AI solves a real problem – scale. Now I’d like to say that AI is not taking jobs away any time soon, but the reality is more complicated and certainly less rosy. AI won’t take your job, but if you don’t learn how to use AI to do your job, you’re going to lose. The future of AI is in the works right now – giant learning farms that are growing better language and decision recognition to replace humans at interaction points.

I said before that Alexa wasn’t using AI to turn on your lights – she isn’t. But she’s using AI to gauge how you respond – to hear you over music, to make a note of whether you’re sounding happy or sad. This has enormous ramifications.

Race matters, socioeconomic standing matters. Gender matters.

Who is more likely to have an iPhone?

Regardless of these concerns – these farms are farming. Amazon, Apple, and other players are getting good at listening to and talking to humans. That’s going to show up in your car. It’s going to show up in customer service. It’s going to show up in spy programs. Facial recognition is similarly happening at a quietly horrifying rate. All these capabilities mean that AI is going to become a distributed service. Have a startup that wants to teach kids reading by monitoring their emotional response? No problem – IBM Watson has you at .035 cents per frame. Want to monitor your middle schoolers’ chatter for offensive language?

Microsoft Azure’s able to distinguish up to ten voices at a time. AI will be a commodity – with large companies vying for leadership in the ownership of giant training corpuses – so likely they’ll end up specializing. All of this will benefit small startups, but real powerhouse advancements in AI will come when 1:1 interaction – customer support, teaching French – shift to systemic interactions.

What role does machine learning play in the organization/business/workforce of the future?

AI is initially going to replace a number of workers who deal directly with customers and it’s going to clear the path northward by turning those interactions into feedback for the systems that make the company run. It’s already happening. In fact, I helped work on some of the AI that does this with automated social media interactions.

Tweet your favorite shoe-brand about your broken laces, add a dash or two of natural language processing and decisioning and poof – that’s magically ending up in the inbox of a material sciences team. This might sound impressive but in the short-term it’s table stakes. There is some space for the other mundane machine learning task – resource optimization. This is the constant running and testing that checks to see how much more or less productive your factory floor is if you up the temperature by two degrees on Tuesdays at 3:30 P.M.

We’ll see it in hiring, then all over. But this will fizzle and, in some cases, already has – a simple rule for whether machine learning is going to take over space is: does it move the needle more than it costs to maintain?

Machine learning is relatively cheap – but it costs to maintain – it costs managers who file reports on productivity, and it costs big data storage and training hours. What it will do is create avenues for human amplification. Connecting machine learning (and eventually AI) to repetitive tasks and reports will shortly be part of standard business school training. Soon, just like when we stopped needing designers and Computer Science majors to update our blogs, we’ll stop hiring teams of data scientists to build our dashboards.

Data science is going to become a commodity skill both because everyone will start to learn it and because the tools of the trade will soon make it easy.

Excel will have machine learning plugins. And once it does, the smart kids will figure out how to use it. The winners in the space will create language and paradigms around it. We’re going to have to have a bell curve moment with machine learning. And when we do, the people who get it will be running the show.

Figuring out how best to use A.I. requires a realistic understanding of what a company is capable of —not just tech chops, but what their customers want, how it fits in with their overall brand and market position. What advice do you have for business leaders and marketers looking to lean in more to A.I. now?

AI isn’t magic. It’s a tool with costs and benefits. AI is scale. If you could hire a smart human to do it cheaply, you wouldn’t need AI. In fact, you’re going to hire a bunch of humans just to get the AI to start approximating some of its work. AI (right now at least) is just human action at scale – so you must train it, just as you would train a person. You must know what value it’s going to bring. AI is not able to bring more intelligence than a team of people – it’s just going to do it more quickly. Here’s a simple way to think about it.

I could hire a driver. A person could deliver a driver’s worth of intelligence and value to my transportation experience. But I can’t afford to. I can, however, afford to drive a car that uses AI to see stop-signs and self-park. That is AI replacing a person’s output at a fraction of the cost. Your company needs to have a job that is replaceable before it can get value out of replacing it. Here’s a counterexample. I can’t afford a person to monitor my spending habits. I can’t help it – I see a collectible Star Trek phaser, I buy a collectible Star Trek phaser. But if I used AI to monitor my spending habits, it would almost certainly veto my phaser-based purchases. The question is if an AI told me not to buy it… would I listen?

Value – AI value – only happens then if it does three things. One, it must do something that’s possible. It’s possible to monitor my spending. Two, it must advise in a way that you’d actually listen to – unlike my joy-killing budget monitor. Finally, it must do all that cheaper than if you’d done without. My example may sound silly but there were in fact multiple banks who have tried or are currently trying to win using just that model. And they aren’t. AI is a practical thing – treat it as such.

Any advice for recent graduates looking to start their career in this space?

I wouldn’t give advice just to recent grads – there are many brilliant and talented second and even third-act career folks out there who might want to hear this as well. For anyone starting – or restarting – in this space, I want to say three things.

One, Hell Yeah. The world is your oyster – there’s so much potential and you are getting in line to shape the world. You are amazing.

Two. Because you are amazing, you have to make some demands!

Demand equity. Demand diversity.

If you look into a room – ANY room in your company and you don’t see it, hear it, feel the voices of different people – you turn around and you run. You may think this isn’t a tech-point, but it is THE tech point. Everything from virtual reality to machine learning and AI are training and learning. And if they aren’t training and learning on all kinds of people, then these tools will someday be part of their oppression. Demand transparency. Demand work-life balance. Being a developer is work. That work had better be respected. Demand kindness. And third, finally, a weird bit of advice I gave to some totally bewildered interns just this year. I said check out the Yamaha jet ski.

The logo on the front.

It’s kind of unclear at first, but it’s three tuning forks in a circle. On a jet ski. Over a century ago they were making little reed organs in Hamamatsu and now they make motorcycles and synthesizers because everything you do is subject to change. Nothing here is sacred. Where you are today is not a map to the future, it’s gas for your trip.

And, that is why technology is so amazing. That is why we’re here. So, don’t waste a second of it being boring.

Thank you, Jared! That was fun and hope to see you back on MarTech Series soon.