Software and technology companies deal with a unique set of challenges as they face digital disruption. As tech companies, the world at large holds these organizations to a higher standard. They are expected – by customers, shareholders, partners, markets – to have a digitally savvy workforce and be well-equipped to sail through a digital revolution unharmed.

But are they well-equipped for digital transformation?

For many, the answer is no. Software companies and technology suppliers struggle to make organizational changes too, much like companies in other industries. While they may have ample in-house tech talent, it’s often the wrong kind. Development skills in an outdated, irrelevant legacy tool, for example, doesn’t do a technology company much good if the objective is fog computing. Or perhaps the skillsets are the right ones, but the employees are simply too busy keeping lines of business operating to redirect efforts to transformation initiatives.

How are the successful software and technology companies doing it? Below, we’ve outlined four strategies being leveraged by industry leaders in tech and software to redefine their business models and evolve along with Industry 4.0.

Software-as-a-Service (SaaS)

One of the greatest strategic advantages a software or technology company can take is adopting a SaaS (software-as-a-service), or flexible consumption, business model.

“The first and most significant challenge is the enterprise software industry’s continued reliance on the legacy perpetual license/maintenance model of transacting with customers. Under this model, software companies sell users a license that establishes ownership of the software,” writes Nick Ismail in Information Age. “Then, the vendor charges the user annual maintenance costs for patches and updates. But in the age of software as a service (SaaS), this practice is outmoded, and subscription-based sales is now emerging as the industry standard.”

While it sounds simple enough, this shift can be difficult. Switching from a licensing model to a cloud-based subscription model requires a significant amount of work. Companies will need to hire new skillsets internally and digitally reskill existing workers. Which is why companies, especially large ones, should consider a change management program to help define the scope of the initiative, identify action items, assign roles and responsibilities, and build momentum internally.

Internet of Things (IoT)

By now, most business leaders have likely heard of IoT, or the Internet of Things—a term that refers to the growing tech trend of interconnectivity across devices. Consumer products like smart watches, Alexa, connected vehicles and smart home appliances are the most-often cited examples of IoT. On the B2B side, applications like industrial sensors and smart agriculture are becoming commonplace. This trend is gaining so much traction that Gartner predicts more than 20.4 billion connected IoT devices will be deployed by the year 2020.

IoT devices gartner data

For each individual software and technology company, an IoT strategy will differ. A consumer-focused company may need to consider how their product’s data can or should connect with existing IoT devices (like a smart watch, for example). A B2B manufacturer in the building products space will likely need to consider the advent of smart home technology and consider digitizing their product or material so it can be accessed remotely. All companies, regardless of market and audience, may see a use case for IoT on the operational side – inventory management, for example, or building a remote workforce, may make more sense for your business than consumer-facing additions.

Regardless of the unique application, software and technology companies should begin thinking about incorporating IoT connectivity into their R&D cycles, if they haven’t already.

Fog Computing & Edge Computing

On-premise data storage won’t be going away anytime soon – but data storage architecture strategies are taking on new shape as the volume of data increases, and with it, the need to reduce latency and improve security. In the software and technology sector, particularly in IoT applications, fog computing and edge computing are becoming increasingly commonplace.

Edge computing and fog computing – two terms that are closely intertwined – are gaining popularity to accommodate the need for data processing to be closer to its source. Imagine a jogger wearing a smartwatch as she runs through Central Park. The effectiveness of that smartwatch depends on real-time data: monitoring her heart rate, her pace, calories burned, and other metrics. If the watch manufacturer’s data processing systems are closer to the device – literally at the edge of the network, or in the fog instead of up in the clouds – that data can be processed much more quickly, meaning the jogger has a much more effective product with more accurate data. That is what edge computing and fog computing aim to do. On the other hand, data that does not need to be immediately processed and accessed, such as predictive analytics or historical data for machine learning purposes, can be stored in a centralized public cloud instead of at the edge of the network.   

What this means in the software industry specifically is not totally unlike other markets: new skillsets will be required to operate new data storage architectures and applications, and the business infrastructure will change drastically, requiring close collaboration between IT, software or technology developers, and business leaders.

Artificial Intelligence (AI)

According to Gartner, “to keep pace with the demands of digital transformation initiatives, application development teams will augment their efforts with AI “co-developers” to streamline programming efforts.” Software and technology companies have a host of data at their fingertips, and that data holds the secret to growth for many. AI and machine learning are being leveraged as smart apps, or “AI coaches,” in software and application development. The AI coach can suggest a next step to a developer, provide real-time training, assist with QA prior to release, identify bugs, and even do some coding itself.

“With a 1% unemployment rate for software developers, the talent shortage is not forecasted to improve, and development managers have been lacking the means to dramatically improve team productivity,” writes Johan den Haan, Chief Technology Officer at Mendix, a SaaS company that offers application developers an AI assist platform. “They have also been unable to tap into the nearly 10-times as many non-technical developers available, and teaching and enabling them to build applications is key to jumping on the digital transformation train.”

The Key to Success: A Strategic Plan

While the trends above are exciting, they are not a complete strategic plan. Business model changes this drastic require care and conscientious forethought before making changes for change’s sake. The software consultants at Falls Digital recommend beginning a digital transformation initiative with a visioning session – a sometimes painstaking but always necessary first step to defining the company’s long-term objectives. Those objectives are then systematically broken down into digital initiatives, and further separated into discrete tasks and deadlines.

Remember: technology trends, while fun to discuss, are only one small piece of the digital transformation puzzle.

Topics: featured, Technology and Data, Cloud Infrastructure, IoT, AI

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