This is a draft excerpt from a book I’m working on, tentatively titled The Business Bottleneck. If you’re interested in the footnotes, leaving a comment, and the further evolution of the book, check out the Google Doc for it. Also, the previous excerpt, “The Finance Bottleneck.”

Digital transformation is a fancy term for customer innovation and operational excellence that drive financial results. John Rymer & Jeffrey Hammond, Forrester, Feb 2019.

The traditional approach to corporate strategy is a poor fit for this new type of digital-driven business and software development. Having worked in corporate strategy I find that fitting its function to an innovation-led business is difficult. If strategy is done on annual cycles, predicting and proscribing what the business should be doing over the next 12 months, it seems a poor match for the weekly learning you get from a small batch process. Traditionally, strategy defines where a company focuses: which market, which part of the market, what types of products, how products are sold, and therefore, how money should be allocated. The strategy group also suggests mergers and acquisitions, M&A, that can make their plans and the existing business better. If you think of a company as a portfolio of businesses, the strategy group is constantly asses that each business in that portfolio to figure out if it should buy, sell, or hold.

The dominant strategy we care about here goes under the name “digital transformation.” Sort of. The idea that you should use software as a way of doing business isn’t new. A strategy group might define new markets and channels where software can be used: all those retail omnichannel combinations, new partnerships in open banking APIs, and new products. They also might suggest businesses to shut down, or, more likely divest to other companies and private equity firms, but that’s one of the less spoken about parts of strategy: no one likes the hand that pulls the guillotine cord.

A moment of pedantry

First, pardon a bit of strategy-splaining. Having a model of what strategy is, however, is a helpful baseline to discuss how strategy needs to change to realize all these “digital transformation” dreams. Also, I find that few people have a good grasp of what strategy is, nor, what I think it should be.

I like to think of all “markets” as flows of cash, big tubes full of money going from point A to point B. For the most part, this is money from a buyer’s my wallet flowing to a merchant. A good strategy figures out how to grab as much of that cash as possible, either by being the end-point (the merchant), reducing costs (the buyer), or doing a person-in-the-middle attack to grab some of that cash. That cash grabbing is often called “participating in the market.”

When it comes to defining new directions companies can take, “payments” is a good example. We all participate in that market. Payments is one of the more precise names for a market: tools people use to, well, pay for things.

First, you need to wrap your head around the payments industry, this largely means looking at cashless transactions because using cash requires no payment tool. “Most transactions around the world are still conducted in cash,” The Economist explains, “However, its share is falling rapidly, from 89% in 2013 to 77% [in 2019].” There’s still a lot of cash used, oddly in the US, but that’s changing quickly, especially in Asia, for example in China, The Economist  goes on, “digital payments rose from 4% of all payments in 2012 to 34% in 2017.” That’s a lot of cash shifting and now shooting through the payments tube. So, let’s agree that “payments” is a growing, important market that we’d like to “participate” in.

There are two basic participants here:

1. New companies enter the market by creating new ways of paying for things that compete with existing ways to pay for things. For example, new entrants are services like Alipay, Bunq, Apple Pay, and GrabPay. While this is the domain of startups in most people’s minds, large companies play this role often.
2. Existing companies both defend their existing businesses and create new ways of paying for things. For example, Dutch banks launched iDEAL several years ago. Existing companies often partner with new entrants, for example: Goldman Sachs provides the backend for Apple Pay and Maybank partnered with GrabPay. Incumbents can also accomplish the second goal by just acquiring the new companies: in general banking, Goldman Sachs acquired Honest Dollar to help it get into consumer banking.

“Strategy,” then, is (1.) deciding to participate in these markets, and, (2.) the exact way these companies should participate, how they grab money from those tubes of cash. Defining and nailing strategy, then, is key to success and survival. For example, an estimated 3.3 trillion dollars flowed through the credit card tube of money in 2016. As new ways of processing payments gain share, they grab more and more from that huge tube of cash. Clearly, this threatens the existing credit card companies, all of whom are coming up with new ways to defend their existing businesses and new payment methods.

As an example of a general strategy for incumbents, a recent McKinsey report on payments concludes:

The pace of digital disruption is accelerating across all components of the GTB value chain, placing traditional business models at risk. If they fail to pursue these disruptive technologies, banks could become laggards servicing less lucrative portions of the value chain as digital attackers address the friction points. To avoid this fate, banks must embrace digitized transaction banking with a goal of eliminating discrepancies, simplifying payments reconciliation, and streamlining infrastructure to operate profitably at lower price points. They must take proactive strategic steps to leverage their current favorable market position, or watch new market entrants pass them by.

That is:

1. New methods of payments will destroy your business, those pesky “tech companies.”
2. So you should create some new (not credit card) payment methods 
3. At the same time make your back-end systems more efficient so they can drive down costs for your existing credit card based business, increasing your profit margins despite overall revenue declining as “tech companies” grab more and more money out of your cash-tubes.
4. Also, take advantage of your existing capabilities in security, fraud handling, and governance compliance to differentiate both your new, not credit card payment offerings and defend your existing credit card business.

That’s pretty good strategic direction and it comes, as you can see in the PDF, from a very deep analysis of market conditions, and trends – there’s even a Mekko chart!

Now, how you actually put all that into practice is what strategy is. Each company and industry has its own peccadilloes. The reason McKinsey puts out all those fine charts is to do the pre-sales work of getting to invite them in and ask “yes, but how?” 

Getting over digital transformation fatigue

“Software is eating the world.” Pronouncements like this chestnut are by now obvious thanks to many Casandras have grown hoarse over the years. As one executive put it:

We came to the realization that, ultimately, we are a technology company operating in the financial-services business. So, we asked ourselves where we could learn about being a best-in-class technology company. The answer was not other banks, but real tech firms. 

This type of thinking has gone on for years, but change in large organizations has been glacial. If you search for the phrase “digital transformation” you’ll daily find sponsored posts on tech news sites preaching this, as they so often say, “imperative.” They’re long on blood curdling pronouncements and short on explaining what to actually do.

We’re all tired of this facile, digital genuflection. But maybe it’s still needed. 

If survey and sentiment are any indication, digital strategies are not being rolled out broadly across organizations as one survey, below, suggests. It shows that the part of the businesses that creates the actual thing being sold, product design and development, is being neglected:

As with all averages, this means that half of the firms are doing better…and half of them worse. Curiously, IT is getting most of the attention here: as I say, the IT bottleneck is fixed. My anecdotes-as-data studies match up with the attention customer service is getting: as many of my examples here show, like the Orange one, early digital transformation applications focus on moving people from call centers to apps. And, indeed, “improving customer experience” is one of the top goals of most app work I see.

But, it drops off after there. There’s plenty of room for improvement and much work to be done by strategy groups to direct and decide digital strategy. Let’s look at a two part toolkit for how they might could do it:

1. Sensing your market – how to observe your market to time and plan changes.
2. Validating strategy – a new method to safely and accurately define what your organization does.

Sensing your market

Changing enterprise strategy is costly and risky. Done too early, and you deliver perfectly on a vision but are unable to scale to more customers: the mainstream is not yet “ready.” Done too late, and you’re in a battle to win back customers, often with price cutting death spirals and comically disingenuous brand changes: you don’t have time for actual business innovation, so you put lipstick on discount pigs.

An innovation strategy relies on knowing the right time to enter the market. You need a strategy tool to continually sense and time the market. Like all useful strategy tools, it not only tells you when to change, but also when to stay the same, and how to prioritize funding and action. Based on our experience in the technology industry, we suggest starting with a simple model based on numerous tech market disruptions and market shifts. This model is Horace Dediu’s analysis of the post-2007 PC market. 2007, of course, is the year the iPhone was introduced. I’m not sure what to call it, but the lack of a label doesn’t detract from its utility. Let’s call it The Dediu Cliff:

To detect when a market is shifting, Dediu’s model emphasizes looking beyond your current definition of your market. In the PC market, this meant looking at mobile devices in addition to desktops and laptops. Microsoft Windows and x86 manufacturers had long locked down the definition and structure of the PC market. Analyst firms like IDC tracked the market based on that definition and attempted disruptors like Linux desktop aspirants competed on those terms.

When the iPhone and Android were introduced in 2007, the definition of the PC market changed without much of anyone noticing. In a short 10 years, these “phones” came to dominate the “PC” market by all measures that mattered: time spent staring at the screen, profits, share increases, corporate stability and high growth, and customer joy. Meanwhile, traditional PCs were seen mostly as work horses, as commodities like pens and copy machines bought on refresh cycles with little regard to differentiation.

Making your own charts will often require some art. For example, another way to look at the PC market changing is to look at screen time per device, that is, how much time people spend on each device:

You have to find the type of data that fits your industry and the types of trends you’re looking to base strategy on. Those trends could be core assumptions that drive how your daily business functions. For example, many insurance businesses are still based on talking with an agent. So, in the insurance industry, you might chart online vs. offline browsing and buying:

While more gradual than Deidu’s PC market chart, this slope will still allow you to track trends. Clearly, some companies aren’t paying attention to that cliff: as the Gartner L2 research goes on to say, once people look to go from quote to purchasing, only 38% of insurance companies allow for that purchase online.

Gaining this understanding of shifts in the very definition of your market is key. Ideally, you want to create the shift. If not, you want to enter the market once the shift is validated, as early as possible, even if the new entrant has single digit market share. Deploying your corporate resources (time, attention, and money) often takes multiple years despite the “overnight success” myths of startups. 

Timing is everything. Nailing that, per industry, is fraught, especially in highly regulated industries like banking, insurance, pharmaceuticals, and other markets that can use regulations to, uh, artificially bolster barriers to entry. Don’t think that high barriers to entry will save you though: Netflix managed to wreak havoc in the cable industry, pushing top telcoes even more into being dumb pipes, moving them to massive content acquisitions to compete.

I suggest the following general tactics to keep from falling off The Dediu Cliff:

1. Know your customer – study their Jobs to be Done, maintain a good, “speaking” relationship with them.
2. Consider Cassandras that use footnotes – track trend spotting, especially year over year (over year, over year).
3. Try new things – experiment and incubate new ideas to continually test and participate in the market.

We’ll take a look at each of these, and then expand on how the third is generalized into your core innovation function.

Know your customer

Measuring what your customer things about you is difficult. Metrics like NPS and churn give you trailing indicators of satisfaction, but they won’t tell you when your customer’s expectations are changing, and, thus, the market.

You need to understand how your customer spends their time and money, and what “problems” they’re “solving” each day. For most strategy groups, getting this hands on is too expensive and not in their skill set. Frameworks like Jobs to Be Done and customer journey mapping can systemize this research, as we’ll see below, using a small batch process to implement your application allows you to direct strategy by observing what your customers actually interact with your business do day-to-day. 

Case Study: “The front door of the store is in your pocket,” Home Depot

In the ever challenging retail world, The Home Depot has managed to prosper by knowing their customer in detail. The company’s omnichannel strategy provides an example. Customers expect “omnichannel” options in retail, the ability to order products online, buy them in-store, order online but pick-up in-store, return items from online in-store…you get the idea. Accomplishing all of those tasks seems simple from the outside, but integrating all of those inventory, supply-chain, and payment systems is extremely difficult. Nonetheless, as Forrester has documented, The Home Depot’s concerted, hard fought work to get better at software is delivering on their omnichannel strategy: “[a]s of fiscal year 2018, The Home Depot customers pick up approximately 50% of all online orders in the store” and a 28% growth in online sales.

Advances in this business have been fueled by intimate knowledge of The Home Depot’s customers and in-store staff by actually observing and talking with them. “Every week, my product and design teams are in people’s homes or [at] customer job sites, where we are bringing in a lot of real-time insights from the customers,” Prat Vemana, The Home Depot’s Chief Digital Office said at the time.

The company focuses on customer journeys, the full, end-to-end process of customers thinking to, researching, browsing, acquiring, installing, and then using a product. For example, to hone in on improving the experience of buying appliances, the product team working on this application spent hours in stores studying how customers bought appliances. They also spent time with customers at home to see how they browsed appliance options. The team also traveled with delivery drivers to see how the appliances are installed.

Here, we see a company getting to know their customer and their problems intimately. This leads to new insights and opportunities to improve the buying experience. In the appliances example, the team learned that customers often wanted to see the actual appliance and would waste time trying to figure out how they could see it in person. So, the team added a feature to show which stores had the appliances they were interested in, thus keeping the customer engaged and moving them along the sales process. 

Spanning all these parts of the customer journey gives the team research-driven insights into how to deliver on The Home Depot’s omnichannel strategy. As customers increasingly start research on their phone, in social media, go instore to browse, order online, pick up instore, have items delivered, and so forth, many industries are figuring out their own types of omnichannel strategies. 

All of those different combinations and changing options will be a fog to strategy groups unless they start to get to know their customers better. As Allianz’s Firuzan Iscan puts it: “When we think from the customer perspective, most of our customers are hybrid customers. They are starting in online, and they prefer an offline purchasing experience. So that’s why when we consider the journey end to end, we need to always take care of online and offline moments of this journey. We cannot just focus on online or offline.”

Corporate strategy didn’t sign up for this

The level of study done at The Home Depot may seem absurd for the strategy team to do. Getting out of the office may seem like a lot of effort, but the days spent doing it will give you a deep, ongoing understanding of what your customers are doing, how you’re fulfilling their needs, and how you can better their overall journey with you to keep their loyalty and sell more to them. Also, it’s a good excuse to get out of beige cubicle farms and dreary conference rooms. Maybe you can even expense some lunches!

As we’ll see, when the product teams building these applications are put in place, strategy teams will have a rich source of this customer information. In the meantime, if you’re working on strategy, you’d be wise to fill that gap however you can. We’ll discuss one method next, listening to those people yelling and screaming doom and disruption.

Consider Cassandras

In Western mythos, Cassandra was cursed to always have 100% accurate prophecies but never be believed. For those of us in the tech industry, cloud computing birthed many Cassandras. Now, in 2019, the success of public cloud is indisputable. The on-premises market for hard and software is forever changed. Few believed that a “booker seller” would do much here or that Microsoft could reinvent itself as an infrastructure provider, turning around a company that was easily dismissed in the post-iPhone era.

Despite this, as far back as 2007, early Casandras were pointing out that software developers were using AWS in increasing numbers. Early on, RedMonk made the case that developers were the kingmakers of enterprise IT spend. And, if you tracked developer choice, you’d see that developers were choosing cloud. More Cassandras emerged over the years as cloud market share grew. Traditional companies heard these Cassandras, some eventually acting on the promises.

Finally, traditional companies took the threat seriously, but as Charles Fitzgerald wickedly chronicled, it was too late. As his chart above shows, entering the public cloud market at this stage would cost $100’s of billions of dollars, each year, to catch up. The traditional companies in the infrastructure market failed to sense and act on The Cliff early enough – and these were tech companies, those outfits that are supposed to outmaneuver and outsmart the market!

Now, don’t take this to mean that these barriers to entry are insurmountable. Historically, almost every tech leader has been disrupted. That’s what happened in this market. There’s no reason to think that cloud providers are immune. We just don’t know when and how they’ll succumb to new competitors or, like Microsoft, have to reinvent themselves. What’s important, rather is for these companies to properly sense and respond to that threat.

There’s similar, though, rearview mirror oriented, stories in many industries. TK( listing or summarizing one in a non-tech company would sure be cool here ).

To consider Cassandras, you need a disciplined process that looks at year over year trends, primarily how your customers spend their time and money. Mary Meeker’s annual slide buffet is a good example: where are your customers spending their time? RedMonk’s analysis of developers is another example. A single point in time Cassandra is not helpful, but a Cassandra that reports at regular intervals gives you a good read on momentum and when your market shifts.

Finally, putting together your own Dediu Cliff can self-Cassandraize you. Doing this can be tricky as you need to imagine what your market will look like – or several scenarios. You’ll need to combine multiple market share numbers from industry analysts into a Cliff chart, updating it quarterly. Having managed such a chart, I can say it’s exhilarating (especially if someone else does the tedious work!) but can be disheartening when quarter by quarter you’re filed into an email inbox labeled “Cassandras.”

Thus far, our methods for sensing the market have been a research, even “assume no friction” methods. Let’s look at the final method that relies on actually doing work, and then how it expands into the core of the new type of strategy and breaking The Business Bottleneck.

Try new things

The best way to understand and call market shifts is to actually be in the market, both as a customer and a producer. Being a customer might be difficult if you’re, for example, manufacturing tractors, but for many businesses being a customer is possible. It means more than trying your competitor’s products. To the point of tracking market redefinition, you want to focus on the Jobs to Be Done, problems customers are solving, and try new ways of solving those problems. If this sounds like it’s getting close to the end goal of innovation, it’s because it is: but doing it in a smaller, lower cost and lower risk way.

For example, if you’re in the utility business, become a customer of in-home IoT devices, and how that technology can be used to steal your customer relationship, further pushing your business into a commodity position. In the PC market, some executives at PC companies made it a point of pride to never have tried, or “understood” the appeal of small screens – that kind of willful, proud ignorance isn’t helpful when you’re trying to be innovative. 

You need to know the benefits of new technologies, but also the suffering your products cause. There’s a story that management at US car manufacturers were typically given a company car and free mechanical service during the day while their car was parked at the company parking lot. As a consequence, they didn’t know first hand how low quality affected the cars. As Nassem Talab would put, they didn’t have any skin in the game…and they lost the game. Regularly put your skin in the game: rent a car, file an insurance claim, fill out your own expenses, travel in coach, and eat at your in-store delis.

Ket to trying new things is to be curious, not only in finding these things, but in thinking up new products to improve and solve the problems you are, now, experiencing first hand.

The goal of trying new things is to experiment with new products, using them to direct your strategy and way of doing business. If you have the capability to test new products, you can systematically sense changes in market definition. Tech companies regularly gloat new ideas as test products to sense customer appetite and, thus, market redefinitions. If you’ve ever used an alpha or beta app, or an invite only app, you’ve played a part in this process. These are experiments, ways the company tries new things. We laud companies like Google for their innovation successes, but we easily forget the long list of failed experiments. The website killedbygoogle.com catalogs 171 products that Google killed. Not all of these are “experiments,” some were long-running products that were killed off. Nonetheless, once Google sensed that an experiment wasn’t viable or a product no longer valid, they killed it, moving on.

When it comes to trying things, we must be very careful about the semantics of “failure.” Usually, “failure” is bad, but when it comes to trying new things, “failure” is better thought of as “learning.” When you fail at something, you’ve learned something that doesn’t work. When you’re feeling your way through foggy, frenetic market shifts requires tireless learning. So, in fact, “failing” is often the fastest way to success. You just need a safe, disciplined system to continually learn.

Validating strategy

Innovation requires failure. There are few guarantees that all that failure will lead to success, but without trying new things, you’ll never succeed at creating new businesses and preventing disruption. Historically, the problems with strategy has been the long feedback cycles required to tell you if your strategy “worked.”

First, budgets are allocated annually, meaning your strategy cycle is annual as well.Worse, to front-load the budget cycle, you need to figure out your strategy even earlier. Most of the time, this means the genesis of your current strategy was two, even three years ago. The innovation and business rollout cycles at most organizations are huge. TK( some long roll out figure). It can be even worse: five years, if not ten years in many military projects. Clearly, in “fast moving markets,” to use the cliché, that kind of idea-to-market timespan is damaging. Competing against companies that have shorter loops is key for organizations now. As one pharmacy executive put it, taking six months to release competitive features isn’t much use if Amazon can release them in two months.

Your first instinct might be the start trying many new things, creating an incubation program as a type of beta-factory of your own. The intention is good, but the risks and costs are too high for most large organizations. Learning-as-failure is expensive and can look downright stupid and irresponsible to share holders. Instead, you need a less costly, lower risk way to fail than throwing a bunch of things at the wall and seeing what sticks.

The small batch cycle

Many organizations using what we’ll call the small batch cycle. This is a feedback loop that relies on four simple steps:

1. Identify a problem to solve.
2. Create a theory of how to solve the problem.
3. Validate this theory by trying it out in real life.
4. Analyze the results to see if the theory is valid or not.

This is, essentially, the scientific method. The lean startup method and, later, lean design has adapted this model to software development. This same loop can be applied “above the code” to strategy. This is how you can use failure-as-learning to create validated strategy and, then, start innovating like a tech company. 

As described above, due to long cycles, most corporate strategy is theoretical, at worse, PowerPoint arts and crafts with cut-and-pasting from a few web searches. The implementation details can become dicey and then there’s seeing if customers will actually buy and use the product. In short, until the first customer buys and uses the “strategy,” you’re carrying the risk of wasting all your budget and time on this strategy, often a year or more.

That risk might pay off, or it might not. Not knowing either way is why it’s a risk. A type of corporate “double up to catch up” mentality adds to the risk as well. Because the timeline is so long, the budget so high, and the risk of failure so large, managers will often seek the biggest bang possible to make the business case’s ROI “work.” Taking on a year’s time and $10m budget must have a significant pay off. But with such high expectations, the risk increases because more must be done, and done well. And yet, the potential downside is even higher as well.

This risky mentality has been unavoidable in business for the most part – building factories, laying phone lines, manufacturing, etc. require all sorts of up-front spending and planning. Now, however, when your business relies on software, you can avoid these constraints and better control the risks. Done well, software costs relatively little and is incredibly malleable. It’s, as they say, “agile.” You just need to connect the agile nature of software to strategy. Let’s look at an example.

Case Study: Most viable strategy: Duke Energy validates RFID strategy

As an energy company, Duke Energy has plenty of strategizing to do around issues like: disintermediation from IoT devices, deregulation, power needs for electric vehicles, and improving customer experience and energy conservation. Duke has a couple years of experience being cloud-native, getting far enough along to open up an 83,000-square- foot labs building housing 400 employees working in product teams.

They’re applying the mechanics of small batches and agile software to their strategy creation. “Journey teams” are used to test out strategies before going through the full-blown, annual planning process. “They’re small product-type teams led by design thinkers that help them really map out that new [strategic] journey and then identify [what] are the big assumptions,” Duke’s John Mitchell explained. Once identified, the journey teams test those assumptions, quickly proving or disproving the strategy’s viability.

Mitchell gives a recent example: labor is a huge part of the operating costs for a nuclear power plant, so optimizing how employees spend their time can increase profits and the time it takes to address issues. For safety and compliance reasons, employees work in teams of five on each job in the plant, typically scheduled in hour-long blocks. Often, the teams finish in much less than an hour, creating spare capacity that could be used on another job.

If Duke could more quickly, in near real-time, move those teams to new jobs they could optimize each person’s time. “So the idea was, ‘How can we use technology?’” Mitchell explains. “What if we had an RFID chip on all of our workers? Not to ‘Big Brother’ check in on them,” he quickly clarifies, but to better allocate the spare capacity of thousands of people. Sounds promising, for sure.

Not so fast though, Mitchell says: “You need to validate, will that [approach] work? Will RFID actually work in the plant?” In a traditional strategy cycle, he goes on, “[You’d] order a thousand of these things, assuming the idea was good.” Instead, Duke took a validated strategy approach. As Mitchell says, they instead thought, “let’s order one, let’s take it out there and see if it actually works in plant environment.” And, more importantly, can you actually put in place the networking and software needed: “Can we get the data back in real time? What do we do with data?” The journey team tested out the core strategic theories before the company invested time and money into a longer-term project and set of risks.

Key to all this, of course, is putting these journey teams in place and making sure they have the tools needed to safely and realistically test out these prototypes. “[T]he journey team would have enough, you know, a very small amount of support from a software engineer and designer to do a prototype,” Mitchell explains. “[H]opefully, a lot of the assumptions can be validated by going out and talking to people,” he goes on, “and, in some cases there’s a prototype to be taken out and validated. And, again, it’s not a paper prototype—unless you can get away with it—[it’s] working software.”

Once the strategic assumptions are validated (or invalidated, the entire company has a lot more confidence in the corporate strategy. “Once they … validate [the strategy],” Mitchell explains, “you’ve convinced me—the leader, board, whatever—that you know you’re talking about.”

It’s software

With software, as I laid out in Monolithic Transformation, the key ways to execute the loop are short release cycles, smaller amounts of code in each release, and the infrastructure capabilities to reliably reverse changes and maintain stability if things go wrong. 

These IT changes lead directly to positive business outcomes. Using a small batch cycle increases the design quality and cost savings of application design, directly improving your business. First, the shorter, more empirical, customer-centered cycles mean you better match what your customers actually want to do with your software. Second, because your software’s features are driven by what customers actually do, you avoid overspending on your software by putting in more features than are actually needed. 

For example, The Home Depot kept close to customers and “found that by testing with users early in the first two months, it could save six months of development time on features and functionality that customers wouldn’t use.” That’s 4 months time and money saved, but also functionality in the software that better matches what customers want.

As you mature, these capabilities lead to even wider abilities to experiment with new features like A/B testing, further honing down the best way to match what your software does to how your customers want to use it, and, thus, engage with your business. TK( quick example would be nice here ).

Software is the reason we call tech companies tech companies. They rely on software to run, even define their business. Thus, it’s TK( maybe? ) software strategy where we need to look at next.