If there is a single theme surrounding key trends and developments in the digital world over the past year, it is speed. Technology today is like a heavily loaded freight train barreling down the innovation mountain without even the slightest notion of where the brake is, or if it even works.

As a tumultuous year in technology begins to wind down, the research team at Wikibon Inc. assembled recently to discuss lessons learned in 2017 and predictions for what the next year may bring.

“We are no longer in control of the speed of transformation in our industries,” said Peter Burris (@plburris, pictured, left), head of research at Wikibon and host of theCUBE, SiliconANGLE’s mobile livestreaming studio. “We’re trying to bring ideas to the users out there about where they should place bets and where they might want to think about ratcheting down.”

Watch the video discussion below for Wikibon’s lessons and predictions, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Wikibon’s 2018 predictions.

A heterogeneous cloud

The Wikibon researchers identified several major trends. One of these is that the cloud is clearly becoming heterogeneous, a bubbling mix of hybrid models involving public and private networks with a growing number of native applications and microservices optimized to handle data.

In 2015, the Wikibon research team predicted that a new class of system would emerge to satisfy information technology needs for cloud benefits that could not be provided in the public model. They labeled it the “true private cloud,” and this has been a driver in the heterogeneous evolution.

“We’re seeing [true private cloud] develop very strongly,” said David Floyer (@dfloyer, pictured, right), research analyst at Wikibon. “We’re also seeing some very big changes in how systems are being developed.”

Software dines on the edge

A key factor in systems development will be the “internet of things” and the creation of new tools that can gather and process data at the edge. The edge is not homogeneous either, requiring a primary layer where the data is generated and then a secondary layer where processing functions, such as artificial intelligence, will live.

“Our observation is yes, software is going to eat the world, but it’s going to eat it at the edge,” Burris said.

The hybrid cloud model and pressure on edge compute capabilities have created a need for analytics tools and the ability to process expanding amounts of internet of things device-generated data. This has created pain points for the enterprise, starting with AI.

What began as a need for predictive analytics morphed into data science and then machine learning and AI. “The problem is the approach to analytics has jumped from one thing to another so quickly. I don’t think that anyone has really had a chance to perfect their approach,” explained Neil Raden (@NeilRaden), research analyst at Wikibon.

Headaches over big data

Cloud solutions to manage hybrid models have become increasingly more complex. Open-source ecosystems with Hadoop distributions designed to curate various components added new layers to infrastructure management. Big data has become the big headache.

“It’s safe to say that big data never really crossed the chasm,” said George Gilbert (@ggilbert41, pictured, center), research analyst at Wikibon. “The way [solutions providers] tried to attack that problem was so complicated, in terms of administrative demands, that most customers choked on it.”

The heterogeneous cloud and robust internet of things data at the edge are putting pressure on developers to create an effective workflow pipeline. But there is growing concern around a skills gap to implement new technologies specifically designed for the demands of edge computing.

“The current group of core developers are not prepared for this AI at the edge revolution,” said James Kobielus (@jameskobielus), research analyst at Wikibon. “There’s all of these decisions that need to be made and money that needs to be spent to invest in this entire development infrastructure.”

To buy or to build

The result is the classic “buy versus build” scenario, where enterprise IT organizations must make decisions over whether to purchase products from specialist solution vendors or develop technology in-house. Most of the researchers believe that there is already a move toward buying cloud services from companies with deep learning or AI tools, such as Amazon Web Services Inc. or Microsoft.

“We’ve seen this movie before when enterprises started to build out their applications,” said Gilbert, and then realized it would be more prudent to buy packaged AI applications.

However, there is also a belief in some sectors that AI is more fad than future. “We spend too much time talking about AI,” Raden said. “The average organization needs a computer that thinks like a human being about as much as we need airplanes that flap their wings.”

With the lessons learned in 2017, what is in store for the coming year? A gaze into the crystal ball shows that scale, the blockchain and machine learning will be major factors, while IBM will be forced to retool its Watson strategy and China’s influence in the global market will continue to grow.

For many emerging technology companies, reaching a healthy level of scale and sales volume will be critical in 2018. Failure means a major housecleaning looms on the horizon.

“The business models of venture capital-backed tech startups are getting smashed by cloud and, to a great extent, open source,” said Dave Vellante (@dvellante), co-founder of SiliconANGLE Media and Wikibon co-founder and chief analyst. “We expect massive industry consolidation is going to take place in the next two years.”

IBM Watson needs a makeover

IBM will be forced to confront the reality that its Watson AI strategy will have to be retooled. IBM’s goal for Watson was 8,000 clients, and it has achieved only 500 to-date, according to research data provided by Wikibon’s Raden.

“It was a marketing stunt that someone thought could be turned into a $20-billion-per-year business,” Raden said. “Watson has been a dismal failure, and IBM is going to reassess their whole approach to cognitive computing in 2018.”

There is also an expectation that the blockchain will emerge as a crucial element in how networks are constructed over the next decade. A shared and secure immutable ledger could become the common infrastructure for many businesses.

“Blockchain will be as fundamental to the growth of the worldwide digital infrastructure and digital markets as TCP/IP ]Internet protocol] was to the growth of the web,” Kobielus said.

Despite skepticism among some researchers about AI, machine learning will likely become more widely adopted in the coming year as a cornerstone for IT operations management. “Investment will driven by a realization that this is training wheels for IoT,” Gilbert said.

It’s impossible to assess the impact of technology in global markets today without including China. The country’s growing impact, led primarily by Alibaba, Tencent and Baidu, is expected to be a key part of the story over the next year.

“In 2018, we’re going to see a lot more conversation about the role that China plays in establishing some of the new rules for how cloud, application networks and security plays on a global basis,” Burris said.

Watch the video discussion below for Wikibon’s 2018 predictions, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Wikibon’s analysis.

Photo: SiliconANGLE