It is that time of the year again when we channel our inner psychic about enterprise technology trends. We expect these secular technology trends to play out over 2017 and into early 2018.
- The beginning of scary AI democratization
2016 seemed like the year Artificial Intelligence (AI), and its subset machine learning, turned the corner. A lot of this has been due to the cloud-enabled ease in advancing computing resources, a pre-requisite for AI/machine learning. Self-learning and “intelligent systems” give rise to a multitude of applications. Technology giants such as Amazon, Facebook, Google, IBM, and Microsoft are leading the way through dedicated groups and high-profile hires for advancing the AI mandate. As the technology looks for sizable scale, we are going see the “democratization of AI” help broaden access to its immense potential. For example, Elon Musk’s Open AI alliance released Universe, a software platform for measuring and training an AI’s general intelligence across games, websites, and other applications. Google’s parent Alphabet is putting its entire DeepMind Lab training environment codebase on GitHub. Microsoft is also taking a multi-pronged approach to help make its AI resources available to anyone via the cloud. Although these moves are aimed at catalyzing innovation, only vendors with real skin in the game and an inherent understanding of AI’s potential stand to benefit, limiting the impact system integrators can have via the typical operating model (which is focused on incorporating the latest buzz words in pitch decks and sales collateral). While this democratization will help people accelerate the time-to-value of their AI-enabled initiatives by avoiding the heavy lifting, it will lead to a certain level of commoditization as well. Since every solution will bear the AI tag, enterprises will find it hard to separate the wheat from the chaff. The AI market resembles the cloud wave from some years ago where there was a clot of “cloud-washing” versus “true cloud” implementations. Although the buzz around AI and machine learning is visible from the profile of its varied use cases – whether it is a hedge fund streamlining most of its operational tasks using algorithmic model from its employees’ brains or a law firm hiring an AI lawyer for its bankruptcy practice. Even outgoing U.S. president Barack Obama weighed in, reflecting the importance of AI in shaping our future. - Conversational analytics to focus on engagement
While conventional analytics – which sounds quite like an oxymoron – will still be viewed through the lens of reporting-descriptive-predictive-prescriptive, conversational analytics and engagement solutions will continue to receive a tremendous fillip. We have analytics engines where one can almost talk or query in human language. At an overall level, this theme represents an amalgamation of product design, natural language processing (NLP), AI, and the platform economy for the right user experience. In the consumer world, Amazon has made it a reality with the Alexa-powered Echo speaker/personal assistant, while Google is trying to replicate that through its assistant-enabled speakers. One particular use case – chat bot – is increasing exponentially, and is now beyond the realm of fancy to tangible use cases across multiple industries. While Facebook Messenger is the most mainstream (consumer-facing) example of these phenomena, there are various interesting experiments across Google (Allo), Microsoft (Tay, Xiaoice), Salesforce (NLUI Server Demo), etc. New conversational analytics offerings will take inspiration from chat bots, and also try to become context-aware for greater value tasks. - SaaS mess becomes messier
The enterprise applications market is going to witness more bloodshed as legacy and SaaS natives continue to battle it out for supremacy, further stepping on each others’ toes. While Workday leads its surge beyond the HCM domain to financial management, SAP and Oracle will put their considerable resources behind their cloud push. As these players (and others) continue to eat into each others’ pie, there will be an increase blurring of lines between segments and dilution of a unique value proposition. Also, large independent software vendors have confused the market by masking their hosted offerings as SaaS (read: more cloud-washing), which has increased the competitive intensity. Additionally, the SaaS-ification of everything has left the enterprise application landscape looking very messy. CIOs have a considerable governance and management issue when it comes to their app portfolio, upgrades, patches, visibility, usage, etc. You get a feeling that something’s gotta give, and there will be more consolidation among SaaS vendors in the race for market dominance and scale. - Workplaces to move the needle on productivity versus just engagement
The emergence of Facebook at Work and Slack, (among others), represents the true vision of what is often called the “consumerization of IT.” This refers to the confluence of consumer imperatives such as seamless user experience (UX), BYOD/CYOD convenience, and boundary-less communication within an enterprise IT framework. Digital technologies are reshaping the workplace of the future, while enterprise applications tend to be stuck in a bygone era. Slack/Facebook and their ilk aim to redefine this paradigm. Microsoft has also bought the bait, and launched Teams aka “Slack-killer” in November 2016 (selling it as a part of the Office 365 subscription suite). The focus of these changes has largely been on improving employee engagement, while focus on productivity has been diluted. We expect enterprises to focus more on productivity through differential strategies. Buyers tend to approach these policies in wide variance depending on their business and culture. Think Amazon, which abhors meetings and has a ruthless focus on execution, or Apple, which actively encourages meetings as ideation pods, coming from a culture which is obsessed with making the best products. Bringing machine learning into the mix can help coalesce people, organizational resources, and data. True that workplace collaboration and productivity seems almost unrecognizable. - The automation tsunami will begin to gather speed
Automation dominated headlines (and earnings calls) in 2016, as enterprises and their service partners alike realized the widespread implications of this technology wave. We believe that the impact on the jobs market is just starting as automation takes roots in mature people-intensive processes/functions. The short-medium kerfuffle will be focused on job losses and pricing pressures, with more pertinent questions around the fundamental nature of employment going forward. 2017 will continue to see similar headwinds, with the bulk of the bloodshed still a couple of years away. Beyond the immediate hullabaloo, the automation narrative is much more profound. While machines can already perform many forms of manual labor, the workforce needs to be reskilled/upskilled to remain relevant as the machines move toward more cognitive tasks. Contrary to popular belief, any wave of industrialization such as automation almost invariably leads to a redistribution in the profile of work, with employees focusing on higher value processes/functions. We are just getting started with the immediate pain that precedes an upward shift in the workforce. Unlike others waves of hyper industrialization, e.g., when cars were invented, there are less systemic problems for technology to solve. Riding this wave will require massive social changes in terms of organizational design, training programs, incentive structures, STEM education, demographic policies, etc.
While most of these themes are already playing out in certain shades, they will gather more wind beneath their sails as we dive into 2017. Although Niels Bohr presciently remarked that “prediction is very difficult, especially if it’s about the future,” we would love to hear what you have to say about enterprise technology in 2017 and beyond.
