The ten things that should happen in 2020

DesignOps collaboration will gain traction

The Nielson Norman Group defines DesignOps as “the orchestration and optimization of people, processes, and craft in order to amplify design’s value and impact at scale.”

DesignOps is more important than ever given the heightened user expectations set by the world’s digital giants and social media behemoths. Consumers now routinely include the quality of the digital experience in their buying criteria, and one could argue that user expectations are approaching the point where they’re looking to buy an experience versus buying a product or service.

DesignOps historically manages the design process and it must evolve to accommodate those heightened user expectations in a way that facilitates designer and developer collaboration; even the most creative developer isn’t skilled at design, and poorly designed experiences will fail. The DesignOps evolution should include:

• Inserting the designer earlier in the development process and keeping them there throughout testing and production feedback
• Implementing technology that allows the designer to use their tool of choice
• Taking a design-to-code approach that generates the design automatically with round trip collaboration for developer changes

Digital transformation will continue to evolve into digital innovation

Digital transformation isn’t going away and never will if there are ongoing opportunities for transformation. But it’s important to ignore market noise and ensure that your organization has its own definition of digital transformation, as well as its own set of goals and guiding principles.

For 2020, many organizations will augment or expand digital transformation efforts into digital innovation to drive business results. For example, Bain and Company found that revenues for digital leaders grew 14% over three years. This resulted in “more than doubling the performance of the digital laggards in their industries. Profitability followed a similar pattern—83 percent of the leaders increased margins over that period while less than half of the industry laggards did so,” said Bane and Company.

Making digital innovation part of the fabric doesn’t necessarily call for separate teams or big investments. It starts by developing a culture of innovation by grounding teams in objectives and their accountability for them, as well as giving them broad discretion to execute. Try hackathons that aren’t limited to writing software and think about how you can improve business processes, experiment with different GTM approaches and incorporate new sales and marketing efforts.

Organizations will better align application development and web development efforts

We tend to categorize digital efforts relating to customer experience and application development separately. From the customer experience side, we think about content management systems (CMS) or web content management systems, which are now being up leveled to DXP file system. From the AppDev side, we may think about PaaS or SaaS, which is being up leveled to MDXP. But there are many related principles, including multichannel user experience and needed integration with backend data, apps and authentication mechanisms.

For those on this alignment journey, industry analyst advice can be helpful, but it can also encourage the creation of silos that will hinder your business. It is better to coordinate AppDev and customer experience efforts by adopting flexible platforms and technology components to meet your specific requirements. While there’s no such thing as a single technology for all requirements, these technologies will become more interchangeable and agile with open standards interfaces.

The payoff is that content being managed by a CMS can be exposed to different digital touchpoints versus being completely web centric. For example, think of an integrated chatbot experience, or training content exposed via an augmented reality assistant.

Combining efforts will not only make both your customer experiences and AppDev efforts richer, it will also facilitate sharing across the organization for additional business value.

Increased machine learning and AI projects will begin production

Going from lab to production with machine learning and AI projects is challenging, even for organizations with a full contingent of data scientists and the data analysts required to prepare the data.

But there can be issues with data, such as difficulties in running algorithm experiments, assessing the results accurately, moving things into production, integrating results with operational systems, change management, keeping up with changes, establishing the compute infrastructure required to process scale at the individual asset level and more.

A lot of the difficulty comes from the vast amount of manual processes. The good news is that there are several cloud-accessible packaged services and automated data science tools and platforms that fit more elaborate use cases where custom models are needed. They don’t eliminate the need for data scientists, but they make them more productive and help lower the bar so that other roles, such as app developers and business analysts, can participate effectively in the process.

Another encouraging sign is the acceptance and support these projects are receiving from AppDev. Many colleges are making data science a part of the computer science curriculum, and forward-thinking AppDev leaders are looking across their offerings to determine where machine learning and AI can play a role.

Automated conversational interfaces will start delighting users

We’ve all done our time dealing with automated phone messages where escape by pushing zero to get a human is impossible. And now automated chat is replicating this infuriating experience. Why?

In many cases, it’s because the chatbot has been designed and implemented with decision tree logic. A developer working with a business expert tries to code every possible conversation path, which can lead to a stilted conversation and a possible dead end or inaccurate conclusion to a conversation.

The good news is that an AI-driven chatbot can solve many of these problems:

• It reduces the amount of development required to build a chatbot and allows business experts to be more involved in design and implementation
• It can be trained much like you are training a person, making it ideal for transactional chat interfaces that can schedule appointments, submit claims and more.
• It delivers a more natural intuitive conversation as it learns from experience and is designed for specific business purposes
• It can be integrated into multiple interfaces, such as web sites, mobile applications and kiosks.

Finally, while we think about chat as a front-end construct, the accessibility of back-end services allows consistency for different digital channels, enables data and application integration, enterprise and social authentication integration that the chatbot can use, and it can be extended to support other forms of conversational engagement to home devices.

In the next installment, we’ll look at 2020 trends for modernization, employee and partner user experience expectations, breaking down tech silos, rethinking the edge and business and tech alignment.

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