Why product-focused IoT innovation is the comfort zone

In IoT, hardware and data are just the means to an end, not the ends by themselves.

Here's what you should know if you're primarily aiming to use IoT to improve product features and specs.

Are you involved in IoT or thinking of doing something about it?

If so, I urge you to watch this video. It's only 1:52 long (one minute and fifty-two seconds).

An app-controlled motorized pen is obviously an absurd idea that nobody needs, and nobody would or should ever buy—except perhaps for its sheer novelty.

And yet, this is still a far more accurate reflection of the current state of most companies' attempts at innovation with IoT than what the white-hot industry hype would lead you to believe.

Increasing the complexity of a mechanical product by adding sensors, electronics, and actuators, and/or adding new electromechanical features, makes what you do neither “digital” nor “IoT”. It simply makes it something that engineers have already mastered across numerous industries: it's called mechatronics, it's ubiquitous, and it's also nothing new.

Sure, you can go ahead, interconnect these mechatronics products, and get them to exchange data and, strictly speaking, that is IoT. But if you are not in the business of selling the actual IoT technology as your product, I have to ask: so what?

Putting the horse before the cart

In IoT, hardware and data are just the means to an end, not the ends by themselves.

The video above is a neat parody of acting on a deliberate understanding of the opposite.

Here's the real opportunity of IoT; here's the end: exploit the data delivered by and to the product(s) through IoT technology to create customer value, and embed that value proposition within a profitable business model.

In another word: innovation.

Now, what kind of innovation that will be, is up to you to choose. If you are a traditionally product-focused company, here’s what you are probably considering. Maybe you use insights from the data to gain clarity on the actual use cases and product requirements. Or, to model boundary conditions of simulation models more accurately. Or, to diagnose issues remotely and proactively, and thus increase product reliability or boost product performance. Or, to create entirely new product lines and dominate the markets of niche applications and use cases. Or, to improve your repair and maintenance services and reduce quality costs over the entire product life-cycle.

Product improvement using data is nothing new

But here's the thing: such innovations that are focused on the product and its performance or quality had already been possible and accessible, to some degree and to a limited scale, long before the advent of IoT. For example, through the analysis of data captured with offline data-loggers in field measurements. Or, through multiple long-term customer observations in actual product-use situations. Or, in other cases, simply because the system you are supplying a solution to was already making use of data-logging to record operating conditions. These effectively reflected requirements or target specifications imposed on your own product as a subsystem.

Have you really been waiting for IoT to innovate using insights from at least good-enough quantitative and qualitative data regarding your products, their use, and performance?

If not, then good for you! Too bad that, as you'll see soon, your proactivity is commendable, yet still makes little difference as IoT becomes widely available to more and more market players. You are then more likely than ever before to be disrupted in terms of product features, performance, reliability, etc.

If yes, then the lack of continuous data capture and device interconnectedness can't possibly have been your largest barrier to product-focused innovation. Not with the massively decreasing cost of data acquisition equipment, data storage infrastructure, and analytics capacity in the last decade!

Insights from IoT for product-focused innovation are fundamentally a "better mousetrap". Good-enough insights from good-enough data have been available or achievable with sensible effort for a long time now. You should have already been doing continuous, "minimum-viable" data-driven product improvements to play in the market to begin with.

A better, and increasingly commoditized mousetrap

Undoubtedly, IoT makes product/service improvement more accessible than ever before. Two technological factors related to IoT make this kind of innovation increasingly cheap for everyone competing in the market: 1) the growing popularity, accessibility, and plummeting cost of prototype boards with rapidly improving specs and quality, and 2) the progressive commoditization of software and IT platforms for IoT.

If your business competes as a supplier in either of those fields, unit sales will predictably keep pace with the exploding volume of IoT devices coming online in the next years. Over time, average profit margins will eventually shrink commensurately. The quest for one-upmanship in terms of features and performance for the price will be relentless. It’s eventually going to be a red-ocean bloodbath, so enjoy it while it lasts.

In any case, these two factors make better engineering design faster, cheaper, and easier; it’s easier when you know well what target specs you need to deliver upon; and it’s faster and cheaper when you don’t have to do rework due to “new” requirements and changes. The increasingly widespread accessibility of the "better mousetrap" of IoT-enabled product innovation means that eventually everyone in an industry will be able to ride up the technological S-curves, faster than ever before, and with less investment.

Rejoice, or panic; or do both

If you are small fry in the market, or a new entrant, that's great news. This now affords you plenty of opportunities to disrupt the incumbents. Their technological moats will slowly be eroded by insights from higher-quality data streaming into competitors' operations from actual product usage. That, of course, assumes that you can start gathering data somewhere in the market, preferably in a neglected, untapped niche—something quite realistic, and a well-trodden path of new ventures.

This erosion of technology-driven competitiveness will be doubly true and much faster for incumbents that have been relying for years or decades on a technological superiority resulting from R&D based on engineering intuition, extrapolation from past products, or even serendipitous discoveries. I.e., security by obscurity.

In fact, this scenario might present the best opportunity for startups or smaller players to actually leapfrog large incumbents—notably, if the latter have been “milking the cow” for years with incremental product improvements. After all, business history is rife with examples of incumbents that grew too lazy, risk-averse, or comfortable to endanger an established brand perception with the risks inherent in attempting uncertain innovation with “step-changer” products.

On the flip side: are you an incumbent with lots of market share thanks to a mature product portfolio relying on mature technology for product differentiation? Are your product managers and development engineers overconfident in assuming what the market wants, and what solutions they will provide?

If so, IoT's advent, rapid progress, and increasing availability spells really bad news for you. You are now a sitting duck, ripe for disruption by everyone else; by incumbents, smaller players, as well as entirely new entrants in the industry.

Coasting is over for incumbents

An installed base that is much larger and spread across more markets than that of competitors corresponds to a wider, faster, and more frequent sampling of product usage conditions. This leads to more data that you can sift through to generate even more obscure and previously unthought-of insights. These could help you to identify new market niches, which new product variants could help you dominate, win market share, grow your installed base, gather even more data—and so on, and so forth. Daring to play with IoT potentially kickstarts a virtuous cycle.

If you are an incumbent, as long as your market share is large and intact, it becomes your new moat. At the same time, it presents you with a more urgent and acute challenge of preserving it. After all, it represents a competitive edge in terms of data available that can promote a better customer understanding. It’s truly a case of “use it, or lose it”. Hesitating to play with IoT potentially kickstarts a vicious cycle.

To attempt to sustain or increase your market share and delay the inevitable catching-up of competitors, you could then invest to jump to the next technological S-curve. This is something that other players can very probably also pursue. Even so, one thing is certain: whether you invest or not, coasting on your brand and resting on your technological laurels will become more dangerous than ever before.

Acceleration and disruption

In the era of IoT, data representing the boundary conditions on product usage becomes available granularly, continuously, accurately, and increasingly cheaply. Moreover, it benefits a larger and more diverse set of competitors of all company sizes and ages. Insights from such data provide a more nuanced view on the target requirements and specifications of products and their key differentiating components.

Overall, the question of “what is to be developed?” becomes easier to answer. This makes two new key capabilities crucial to answering this question better than others with similarly easy access to product usage data with IoT.

Firstly, it requires the capability of deriving reliable insights from large volumes of high-dimensional data. Notably, I don't know yet of any traditionally product-focused company that currently embeds business-minded engineers with data science skills within its product development teams, or appoints such people to upgrade or lead their R&D or product-development capabilities. Moreover, such traditional companies are stalwart, and expectedly too proud of their legacy to allow insights from data to interfere with and cast doubt on subject-matter experts’ arcane, tacit knowledge.

Side note: This “prima donna” attitude is best summarized by a memorable quote of an ex-colleague of mine. It was uttered in the exasperated tone of voice of someone who’s been around the block a million times: “Isaak, R&D is art, not science” (I was left speechless).

Secondly, and most importantly, it requires the readiness to act upon such data-driven insights, even if their implications rock the boat. This implies the courage to reshape the product portfolio as needed—even if that means slaughtering “holy cows” such as preexisting market segmentation, once-lucrative but now-declining product categories, or established corporate fiefdoms and functional silos. If history teaches us anything, it’s that a historically-successful organization will tend to undertake such drastic, humbling measures only reactively, shortly before or after things start to go south.

Even if only the first factor is satisfied, the barrier to entry towards good-enough answers to the “what” question is massively reduced. The incumbents have paid dearly for decades in order to achieve their proprietary customer and market understanding. And, they’ve had to endure their share of product flops and tough organizational learning to correct course.

Yet, new entrants focusing on a very narrow niche with IoT solutions have the ability to ramp up their market understanding unprecedentedly faster than the rest of the industry, even avoiding mistakes to a large extent. This makes them more likely to disrupt incumbents—sometimes even unintentionally.

After all, if capital is made available to a startup, and it can know as much as an incumbent (if not more) at a massively lower cost, what’s stopping it from acting upon that knowledge? Talent availability? Patents? Market reach? All of these can be (legally!) circumvented with enough capital, leadership, and creative engineering. And, for sure, it doesn't fear failure or the loss of brand equity as much as the incumbent. Old story.

Product portfolio management

At the same time, as competitors with more complete information of similar quality keep trying to leapfrog each other, maturation of technology and products accelerates. With a better understanding of target requirements and specifications, incremental product upgrades become easier to scope.

Companies who may have gotten used to a “natural constant” of releasing upgrades every so many years now find themselves in the tough spot of executing on scopes every so many months, instead. This brings a whole lot of change pain, as the company acclimatizes itself to a more modular approach to product development, and to making increased use of systems engineering and platform development.

These enablers, by the way, have their own caveats with regards to getting stuck on a specific product architecture, and higher transactional and coordination costs. They are thus not a free lunch, but an expensive source of complexity, also with potentially adverse effects on creativity—as everyone gets relegated to a little box in a system breakdown chart.

In terms of product portfolio management: some may fall into the irresistible trap of a higher product release cadence, aiming to capture customers’ attention more frequently than the competition. This brings with it the risks of stagnating component/product specifications, trivialization of the product category, and increasing customer indifference—much like what already happened with the automotive industry, or during Android smartphones’ initial explosive growth.

Others may learn to lean back from the market noise, and refuse to play along in the leapfrogging game. They may opt to use the improved customer understanding to slice the scope of a product category in the longer run, and focus more on increasing the interim value perception of benefits, than on advertising more features and higher specifications. They may then opt to release larger hardware upgrades less frequently, interspersed with smaller, more frequent software updates—much like the trajectory of the iPhone or the iPad.

In both playbooks, capital investment in jumping to the next S-curve of components’ technology presents a more short-lived advantage than before—especially when competitors are ready to follow suit, or when their supplier base overlaps. After all, it’s highly likely that the suppliers too are making use of IoT-derived insights for their own technology and product development, and are also using that to their advantage.

Side note: assume that releases of tangible products become so frequent and predictable that successive generations of a tangible product become nigh indistinguishable from each other, barring progressively-unlocked features enabled through software. Add on top of that a vendor lock-in, e.g. enabled or enforced by insights from IoT. Doesn’t then a periodic per-unit purchase of the same, slowly-evolving product effectively become an almost inescapably compelling subscription to this product line? Isn’t this then implicitly an argument for pursuing IoT-enabled service business model innovation hot on the heels of IoT-enabled product-focused innovation? More on this in the conclusion.

Ephemeral advantages, and paradoxes

Before IoT, having a technological edge on the competition, and having long experience on customers’ product usage provided time-based and knowledge-based competitive advantages, respectively. With IoT, both advantages become more fleeting and short-lived.

When these two advantages erode fast, answering a different question reliably becomes essential to survival. That question is: assuming near-perfect knowledge of customer requirements thanks to IoT insights, how are we going to develop a product to satisfy them effectively and efficiently?

In other words, the importance of data-driven capabilities for R&D and product development becomes paramount.

Such capabilities famously involve much more than technology such as CAD, simulation models, 3D printing, rapid prototyping, or sophisticated algorithms and data science skills. They also include the far-tougher ability of bringing various subject-matter experts and business functions together in orchestrating complex knowledge work and systematic problem-solving.

In fact, the rise of IoT’s technological measures corresponds to a sudden and massive increase in overall system complexity, beyond the strict boundary of the product/service. Paradoxically, this makes the human element even more important than before in navigating this complexity to reach ambitious goals on time.

When “what to develop” is similarly clear to all players, “how to develop” becomes the last bastion of knowledge-based competitiveness, as well as a source of a fleeting but still important time-based competitive advantage.

Has your organization grown allergic or immune to hitherto nice-to-have pie-in-the-sky buzzwords, such as Digital or Lean or Agile product development? If so, in the era of IoT it will have to accept that a pragmatic, productive application of such concepts without hype turns out to be a major, unavoidable deal. These concepts become essential to acting upon the insights from IoT-gathered data in order to gain market share profitably with new or improved products.

Side note: fundamentally, this also spells the need to finally evolve the role of the development engineer beyond e.g. pure mechanical engineering, and towards skills related to computer and data science.

Can you avoid playing the game of continuous product improvement? If you can’t avoid it without IoT, you probably can’t or shouldn't avoid it with IoT, either. Dropping out of the game means that your products reach obsolescence and your market share erodes in record time frames.

When IoT becomes ubiquitous, using IoT-based insights as the driver of continuous product improvement simply becomes the new minimal requirement to pursue product-focused innovation. In yet another paradox, it becomes simultaneously cheaper and more expensive to innovate: cheaper, despite the more frequent product increments in the short and middle term; and more expensive, due to the more frequent and capital-intensive product step-changers in the long term.

Product-focused innovation: been there, done that, sans IoT

So, back to the question of innovation with IoT: you exploit the data delivered by and to the product(s) through IoT technology to create customer value. You then embed that value proposition within a profitable business model.

Based on the exposition above, is a business model upgrade centered around product-focused innovation the “killer app” of IoT?

I would argue that no; it’s simply more of the same; just technologically fancier, geekier, faster, and more hectic for all involved. In particular for traditionally product-focused companies, tech-obsessing product-focused innovation still very much lies in their comfort zone. It's simply turbocharged by a better knowledge of target requirements and specifications. Yet, it imposes new challenges to build new organizational capabilities that were probably long overdue, even before IoT came about.

To the contrary of this comfort zone: in particular for traditionally product-focused companies, innovation with data-driven software and/or service business models is the real opportunity that IoT presents. The IoT hardware, software, and all other technological aspects are simply the means to get there. Don't put the cart before the horse.

As experience shows, this kind of business model innovation implies non-trivial capability-building, mindset-changing, and portfolio-expansion challenges—far more than canvases and playful Design Thinking workshops. Few incumbents so far have managed to tackle such challenges from within their existing, product-focused, cash-cow business models without the ability to lead pragmatic change.

So, are you still geeking out with IoT gadgetry? Or are you already thinking of leading pragmatic change to build capabilities such as data-driven R&D, Lean/Agile product development, and service/software business model innovation?

If it's the latter, let's talk!

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