Over the past several years, there has been much discussion around the “consumerization of technology.” This term applies mostly to the usability of technology. Plummeting costs and advanced user interfaces allow new applications to be used more broadly than ever before. Usability isn't the only thing changing, though; the bar is also lower for the creation of new applications, and this democratization has potential to disrupt entire industries and social behaviors. The toolset for building technology has been steadily growing more accessible; for proof, look at the vast array of applications available through mobile device app stores compared to the relatively limited selection of packaged software from previous decades. In 2018, though, a tipping point will be reached. As software becomes the driving force of many solutions, open source concepts allow far more people to build applications around blockchain, natural language processing, or context-aware computing. Hardware is also more of an open toolbox, as makers explore novel use cases using drones, robotics, and 3D printing. Technical literacy has risen steeply thanks to mass availability, and that literacy can now tap into a diverse set of options in order to create groundbreaking new paradigms.

CompTIA’s research in the cloud space includes a typical adoption progression for businesses. Starting with exploration, companies move into non-critical use, where day-to-day applications (though not the most important systems) are placed into a cloud model. Up to this point, most companies have been in these first two stages, learning lessons about system migration, integration with existing architecture, and cloud security. Many firms are now ready to take the next steps, moving into full production mode where there are few if any restrictions on the type of application that can live in the cloud. From there, the final stage is transformed IT, where the architecture has been rebuilt to maximize cloud characteristics rather than simply being forklifted. Unlike the first two stages, later-stage challenges are not primarily technical. Instead, companies must build or reconfigure the appropriate policies and workflow for a cloud-based approach. Those companies on the cutting edge of cloud adoption report that these challenges are even more significant than the hurdles in earlier stages, so the cloud discussions will continue evolving.

Building on some of the concepts that allow for democratization of technology, IoT devices are rapidly making their way into corporate spaces. From gathering new data to automation of infrastructure, companies are finding many benefits from adding connectivity and intelligence to physical infrastructure. Unfortunately, the relatively low cost of IoT devices is not reflected in the cost of system maintenance and optimization. Adding digital capabilities to everyday components drastically increases the scope of IT responsibilities. Additionally, new skills are needed for the different types of data streams being generated and the advanced analysis that companies want to perform. Automation will certainly help ease these burdens, but IoT strategies will still further complicate the already-difficult redefinition of the IT function.

Amid the wave of emerging technologies, artificial intelligence stands out as the one most likely to drive changes to the IT ecosystem. Technology solutions are growing more complex and technology demands are growing more specific. The answer to these issues is not found in constantly adding resources but in leveraging benefits of modern technology. AI requires significant compute resources (which can be procured in the cloud), various algorithms that allow learning (which can be baked into products or provided as a service), and contextual awareness (which can come from IoT devices or massive collections of data). By adding a layer of intelligence to the technical solutions they are building, companies can both manage a more extensive IT architecture and solve a broader range of problems. Not every company will necessarily need the skills to build new AI functions, but they will at least need the skills to manage AI components so that they are not just dealing with mysterious black boxes producing unexplained output.

With security incidents continuing to rise, it is clear that attackers are finding new sources of profit even as companies prioritize the protection of their digital assets. At a high level, the situation is not likely to change dramatically in 2018. However, there will be subtler shifts in methodology as companies consider their approach to security. Many businesses have increased investments in security, but these investments often follow an approach that utilizes technology to build a defense. Increasingly, firms will realize that they must also detect the inevitable breaches and respond quickly. Beyond the technical aspects, organizations will also begin building business processes that enhance security, and they will implement end user training to mitigate human error. In short, companies will shift their security mindset from technology-based defenses to proactive steps that include technology, process, and education. There is no doubt that companies are taking security more seriously, but now they must realize that modern security demands a different mentality rather than just more of the same.

It has come to be expected that technology gets better, faster, more capable, and of course, less expensive. Over the past two decades, technology has become so pervasive that it can be difficult to truly grasp its impact. Because of the scale and scope of benefits provided, the tech industry generally gets the benefit of the doubt, overlooking the occasional product flops, the folly of the dot.com era, or the usual frustrations that come with early stage products. However, signs point to changing expectations and a different environment unfolding. Questions surrounding security, privacy, and screen time continue to intensify. Concerns over market concentration and the power imbalances of gatekeepers loom. Reports of toxic corporate cultures and a lack of inclusiveness in certain quarters of the industry bring much needed attention to structural problems. Further advances in artificial intelligence and automation will likely ratchet up the level of scrutiny of their impact on work and society. To be clear, technology will continue to overwhelmingly be viewed as a force for good, but the industry will need to spend more time looking at itself in the mirror to ensure this sentiment continues.

According to estimates, 90% of all digital data in existence in the world today was created in just the past two years. This is a function of the ongoing digitization of information, and more recently, the mass deployment of sensors. Advances in distributed computing power and storage have enabled the collection of just about every data point under the sun (literally, in some cases, as in the ever-expanding use of satellite imagery data streams). These developments are ushering in what can be characterized as the insights economy. Data is obviously the foundation of an insights economy, but similar to other raw materials, it requires refinement and conversion into something of value. Machine learning and artificial intelligence are the levers that make this happen. Companies at the forefront of pulling these levers, for purposes of pattern recognition, predictive analytics, natural language processing, and computer vision, drive the insights economy. In the aggregate, these data-driven activities already account for a sizable portion of global GDP, a figure that will only grow as an insights orientation takes hold across every industry sector. As with anything, tradeoffs will need to be considered as the insights economy comes into focus. A prime example? Data network effects, whereby more data leads to better insights, which leads to even more data. And the aggregator of all that data holds immense power and influence as a result. This could further accelerate a winner-take-all dynamic and create even more pronounced data divides. Additionally, data privacy and ethical concerns are certainly well founded given the frequent lack of transparency and control over how data is collected and used. The sci-fi TV series Black Mirror does an eerily good job of portraying a point in the near future where a person’s all-encompassing data score dictates how they are permitted to interact with the world around them.

Aligning technology to optimally meet business objectives continues to challenge many organizations. This starts with shortcomings in developing a strategy and vision that covers not only technology, but also the people and process elements. Because most technology initiatives today span multiple functional areas and entail many moving parts – infrastructure, mobile environments, data, and integrations, projects can quickly derail without the necessary levels of commitment and resources. Increasingly, organizations are recognizing the importance of having a tech-savvy C-suite and boardroom. This doesn’t necessarily mean having deep technical knowledge, but rather having a feel for the tech landscape, knowing the types of questions to ask, and being able to push back on pursuits that may be a poor fit for the organization. Additionally, with the consequences of a single digital misstep becoming more severe, board-level engagement with cybersecurity and data governance is no longer optional.

The concept of ‘new collar’ jobs originated with IBM CEO, Ginni Rometty. It was a call to action to recognize the changing nature of middle-skill jobs and the need for new approaches to training and preparing the workforce of tomorrow. This change is largely a function of the intersection of technology and just about everything in the economy, from products and workflows to supply chains and job roles. With the rapidly expanding Internet of Things footprint, this trend will only accelerate, reshaping existing jobs and creating entirely new categories of knowledge workers and technologists. While there is still a case to be made for traditional four-year degrees, it has become clear that alternatives are needed. This may entail a skills-centered education and training approach (covering both hard and soft skills), validated by one or more recognized credentials, resulting in a jobs-ready candidate. The New Collar Jobs Act and CompTIA’s Chance in Tech Act, which promotes IT apprenticeships, points toward momentum in the form of legislative support and funding. Despite the momentum, it will take time and an ongoing demonstration of success in building a new collar jobs’ pipeline to cultivate buy-in from students, parents, and employers.

At ever-increasing scale, vendors from Amazon to Microsoft to Salesforce are leveraging their own online marketplaces to aggregate technology goods and services as a kind of one-stop-shop for customers. These marketplaces include technology solutions ranging from standard products to subscription-based cloud services and applications. A quick scan of Google’s GSuite Marketplace, for example, yields access to hundreds of independent software vendors’ cloud-based applications, spanning categories from ERP/logistics to human resources applications to CRM solutions. And with the click of a button, customers can install these solutions and roll them out into their businesses. In 2018, these IT shopping malls will only grow in influence as line of business workers take charge of more IT decision-making and purchasing. This is potentially bad news for the channel, as these marketplaces pose yet another competitive threat. But do they? The answer is both yes and no. The yes is obvious: with more options for customers to quickly and conveniently buy technology direct online, there’s less need for an intermediary. But savvy solution providers, MSPs and IT consultants are figuring out ways to co-exist and even exploit these marketplaces as an extension of what they offer or recommend to their customers. During a time when services dollars cannot be over-valued, certain channel firms will wisely play the business consultant role by helping customers vet the marketplace solutions that will best support their organization’s objectives. And if the customer ultimately self-procures the solution, so be it. The channel firm’s goal should be to provide accompanying business advice, application customization, integration, security and other services.

As channel firms transform from transactional product sales to recurring revenue models, they often grapple with how to price their services. It’s not an easy task. Subscription-based models are complex and can seem arbitrary compared to placing a specific percentage margin markup on a piece of hardware. Things are going to be tougher in the coming year as the new Accounting Standards Codification 606, which took effect in December 2017, makes its impact on the way companies recognize revenue and ultimately bill. Under the new standard, contract-based software solutions that include other offerings such as maintenance, security, implementation services etc., will have to be broken apart into individual transactions. How to price these various bits and pieces? It’s really anybody’s guess right now. Bottom line: the new rule is going to add complexity and more work for MSPs and cloud services providers in the year ahead. One possible source of help? PSA tools vendors such as Connectwise, Kaseya, et al, that already offer automated customer billing modules in their platforms may be able to address the new accounting requirements in their tools. Unfortunately, this will not help the many MSPs that do not use these types of commercial software packages, so they will need to navigate these new financial dictates on their own.

Today’s tech landscape exists as an ”as-a-service” world. From desktop-as-a-service, software-as-a-service, infrastructure-as-a-service, and beyond, the cloud has ushered in an era where any conceivable type of technology solution can be delivered much like the utility services that power your home. But it’s getting more complicated. A cornucopia of disruptive and emerging technologies is spilling into the as-a-service arena. Drones-as-a-service, for example, may sound like something out of sci-fi, but increasingly drone service providers are cropping up to support customer needs. Customers, for example, like AES Corp., a Midwest-based global energy company that wanted to use drones to conduct inspections of its facilities in the field. Drones not being a core competency, AES had two choices: learn a new skill or outsource. They tried the former, but ended up choosing the latter, partnering with a drone-as-a-service operator called Measure, whose company tagline is “We don’t make drones. We make drones work.” And that’s just the point: as more and more customers grow accustomed to consuming established and emerging technologies as a service, channel firms and other providers will need to make sure they are truly up to speed and not simply paying lip service to the “as a service” moniker. Customers want the flexibility the model allows, fast and reliable service, predictability and skill to back it all up. As 2018 marches on, channel firms have a tremendous opportunity to crack into some of these new markets that include authentication-as-a-service, analytics-as-a-service, artificial-intelligence-as-a-service, drones and more. But they will need to invest in skills training, workforce development, and more to take advantage.

DEFINING THE FACETS OF TECHNOLOGY

As technology extends its reach to companies of all sizes, industries with changing technical demands, and nearly every aspect of modern society, the conversations around technology are also expanding. It has always been the case that technology solutions are built from a stack of different components, and technical professionals often focus on a subset of this stack rather than having deep expertise across the entire array. With interest and proficiency growing across the wide population, though, some classification of the technology landscape gives structure to discussions around strategies and expectations.

At the bottom of the tech pyramid, there are enabling technologies. These building blocks are often off the radar screen for most people, but sometimes a leap forward in capabilities will bring an enabling technology to the forefront. A traditional example of an enabling technology would be the TCP/IP protocol—a standard that powers the entire Internet, but not a piece that is wildly evolving on its own. Some topics in the spotlight today appear to be areas where companies need to build competency, but they are in fact enabling technologies that will eventually live in the background of innovative applications. 5G is the next-generation standard for wireless networking, promising lower latency, higher bandwidth, and stronger security. But beyond any infrastructure upgrades, most businesses will focus on new possibilities in workflow rather than specifics of the 5G standard. Similarly, blockchain became a huge topic when Bitcoin’s value skyrocketed in late 2017, but ultimately this technology will be used to fuel a new wave of transactions rather than being a core skill that every company must adopt.

The bulk of the tech ecosystem is comprised of established technologies. These solutions, built with enabling technologies, have proven their worth in the market and are productive tools used by a wide range of businesses. The most well-established examples are so commonplace that they generate few headlines and little hype. Servers, firewalls, virtualization, PCs, and SQL are all widely adopted, and IT pros continue to build their skills in these areas, but they rarely make any top 10 lists or keynote addresses. Cloud computing, smartphones, and social media are newcomers to this space, so interest is still high, but those have clearly become accepted as standard pieces of a modern architecture.

The final category is the one that receives the most attention as consumers and businesses try to stay ahead of the curve and build competitive advantage. Emerging technologies are on the horizon, holding the potential for disruption but still fighting hurdles to adoption before their full potential can be realized. Given the growing importance of technology and the general belief that the rate of tech progress is accelerating, there is a recent tendency to approach emerging technology as a single topic. In reality, this space can be broken into three separate components, each of which are useful to businesses going through digital transformation.

The most common perception of emerging technology is a collection of different trends, any of which could eventually migrate into the bucket of established technologies. Some of these trends (like blockchain) may actually be enabling technologies, driving some confusion around how broadly skills will be needed or where the technology will appear in a solution stack. Other trends—like virtual reality, drones, or autonomous vehicles—clearly represent the type of endpoint tool that would be purchased by a consumer or business. The challenge in dealing with such a wide range of topics lies in developing the expertise to understand the pros and cons of each one. Limited resources further complicate this task.

A second way to approach emerging technology is to determine the desired objective that can be achieved using one or more different tools. For example, the complexity of IT infrastructure and demands for timely results drive a need for automation. Automation is not something that is purchased as a standalone item; instead, a company might employ IoT devices, artificial intelligence, and data analysis to automate certain functions. By focusing on the end goal rather than the discrete technologies, it can be easier to determine which pieces provide the most value.

Finally, companies can consider their internal structure and processes, making adjustments as necessary that will help with internal evaluation and adoption. This approach is closely tied to digital transformation efforts, where the real change occurs not from installing new tools but from changing the mindset and behavior of the organization. Larger companies might build internal innovation labs or cross-function teams that evaluate options and drive cultural change. Smaller companies might reconsider their use of technology partners in order to build a team with the right skill set. At the end of the day, businesses need to find some way to cut through the noise around technology and organize their priorities so that they can invest in the correct tools and skills to remain competitive in the digital economy.

THE DRIVE FOR OPERATIONAL EFFICIENCY

Revenue generation alone isn’t key to long-term growth and the overall health of a business. Reality is that even the best sales results will have diminished impact if a company’s profit margins are leaking because of inefficient internal processes, poorly gauged ROI, or exorbitant customer acquisition costs.

As the channel makes the turn to new business models, it is more essential than ever that firms optimize operations for maximum efficiency in order to reap the full benefit of its revenue intake. This includes fine-tuning the business across a range of areas, from sales, marketing and finance to human resources and supply chain logistics. For many, this means taking a serious look in the mirror. The key to operational efficiency requires an unflinching examination of how well – or not – operations run today, especially before taking the plunge into a new business model.

In December 2017, CompTIA research took a look at how channel firms self-assess their current state of operational efficiency, asking respondents where they see areas of success and challenges, and what actions they are or will be taking to shore up their businesses. As a baseline, the majority of respondents in the study reported undergoing a moderate or high degree of business transformation, which likely includes transitioning to a recurring revenue model such as managed services, specializing in a vertical industry or other niche, or becoming a business consultant focused on helping customers adapt to the digital economy.

Based on topline results, 2 in 10 respondents assessed their current state of operations as very efficient, with another 39% deeming themselves mostly efficient at present. Thirty-six percent ranked themselves in the middle of the pack in terms of efficiency, with some areas in good standing and others not. Finally, 7% acknowledged having mostly inefficient operations.

Not surprisingly, the companies that reported experiencing a high degree of business transformation were more likely to assess their operational efficiency in the superlative. Thirty-six percent of this group described their business as very efficient; that compares to just 13% and 15%, respectively, of firms that report either a low or moderate degree of business transformation. It’s likely the early movers are far enough down the road in their transition to cloud and other business models that they have smoothed out the operational speedbumps that inevitably arise in business model shifts.

Of best practices that respondents believe are critical to improving efficiencies, the top two are calculating ROI/time to profitability before embarking on new projects, and creating repeatable processes across the company. More than half of respondents cited these actions as imperatives.

In terms of challenges, profit margin leaks are a concern for channel firms across the board. The No. 1 impediment to maximizing profit is pricing pressure from customers, a problem cited by half of respondents. Increased pricing pressure is attributed to expanding customer alternatives and negotiating strength, and new types of competitors.

Top 5 Drivers Behind More Complex Operations

• 53% More streams of data to manage/analyze
• 49% Expansion in new business lines/models
• 48% Customer engagement more challenging/complex
• 48% Introduction of emerging tech to portfolio
• 38% More vendors/suppliers in the mix