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Trace3 2020 Predictions: What Won't Happen in 2020

VMblog Predictions 2020 

Industry executives and experts share their predictions for 2020.  Read them in this 12th annual VMblog.com series exclusive.

By Mark Campbell, Chief Innovation Officer, Trace3

What Won't Happen in 2020

The numeric symmetry of the year "2020" is simply irresistible to self-dubbed futurists. They are drawn to its magical glow like skeeters to a bayou zap-light. Trade journals are abuzz with top 10 predictions, hot technologies to watch and cringeworthy puns about ‘2020 Vision'. I guess it's my turn. Bzzzzt. There goes another one.

But, instead of adding to the annals of Captain Obvious (data will grow more this year than ever) or George Jetson (meat alternatives will be available on flying taxis), I'd like to mix it up and explore three commonly accepted predictions that will not happen in 2020.

Homegrown AI Will Eat the World

While you would be hard-pressed to find a company that has not deployed products with embedded AI, many believe 2020 is the year that in-house AI model development will explode. While the hyper-scalers and a handful of line-of-business shops have developed and deployed custom AI models, few mainstream enterprises have taken on the AI development challenge, preferring instead to buy pre-built AI. If enterprises have not already developed their own AI, they likely won't start this year.

Why?

1.       Skills Gap: Most enterprises do not have seasoned AI-developers on staff and cannot afford a bidding war with more attractive dedicated AI firms. As we've seen with the cybersecurity experts and data scientists, the skills gap will get worse before it gets better.

2.       Process: Many large enterprises have efficient software-development lifecycles and are just letting the paint dry on their corporate DevOps transformation. But before those new process smells dissipate, here comes AI development which does not play nicely in the typical unidirectional deployment pipeline. The answer - Machine Learning Operations (MLOps) - is being tackled by a few courageous startups but integrated end-to-end turnkey solutions are only now being demonstrated.

3.       Governance and Infrastructure: AI development requires more than tools, developers and processes. It opens a pandora's box of compliance, security, legal, ethics, explainability and privacy issues.

4.       Better to Buy: Embedded AI is now so good, pervasive and affordable that the build versus buy decision heavily favors off-the-shelf or off-the-net solutions. If you want AI to solve a nasty problem, there's probably a proficient model already available.

Please, don't get me wrong. AI will continue its inexorable march into all facets of IT including in-house AI development. Undoubtedly, there will be a handful of companies that develop their own smart game changers, but don't expect homegrown AI applications to represent more than one or two percent of the production footprint at any mainstream enterprises in 2020.

Multi-Cloud Will Unlock Cloud Vendor Lock-in

Multi-cloud tops the 2020 strategic roadmap of almost every company - even the cloud vendors. The promise of multi-cloud is to create a heterogenous architecture that spans several public and private cloud spaces allowing the migration of application, data and compute to wherever its fastest, cheapest and most available and thereby eliminate the reliance on any single cloud provider. This ‘soup that eats like a meal' is indeed a noble and compelling goal, but cloud vendor independence will not be achieved by multi-cloud strategies in 2020.

Why?

1.       Data Gravity: Applications tend to be drawn towards the data they consume, and data tends to be attracted to the other data with which it interacts. This gravitational pull tends to make IT architectures clumpy. So, if one were to move a large repository of critical data across public clouds, there would need to be an accompanying migration of dependent applications. Beyond this though, even with the best available WAN bandwidths, the fastest way to migrate petabytes of data involves a FedEx truck - not quite the simple click and flip envisioned by most multi-cloud efforts.

2.       Proprietary Cloud Services: The leading public vendors have expanded far beyond their infrastructure-as-a-service roots and now offer hundreds of specialized services like function-as-a-service, AI, blockchain, orchestration, database and even quantum computing. Many cloud-based applications rely heavily on these proprietary services making migration to another cloud provider an enormous re-development effort.

3.       On-Prem Cloud Stacks: Public cloud vendors are seeing growing adoption of on-prem cloud stacks, like AWS Outposts and Azure Stack. These give the same public cloud-like look and feel to on-prem private clouds which simplifies operations, skillsets, hybrid-cloud and cloud bursting while reducing overall costs. However, the adoption of an on-prem cloud stack also means the vendor lock-in concrete sets even harder.

A recent 451 Research report [1] shows 72% of IT enterprises use multiple cloud vendors and many of the remainders will adopt a multiple cloud strategy in 2020. While there are good reasons to adopt a multi-cloud strategy, breaking vendor lock-in is not a valid one.

Digital Transformation Will Launch a Wave of Digital Disruption

Digital Transformation (DT) has become a mandatory line item in most large enterprise's strategic plans and budgets. Most DT initiatives have a stated goal of disrupting their company from within and even their industry in general - what many have dubbed "digital disruption." A great goal but it will not happen.

Why?

1.       Incremental Innovation: ‘Transformation' is the changing of something from one form to another. Many DT efforts are quite effective in streamlining existing processes, automating manual steps or adding digital smarts to replace human decision making. All good stuff. But these are incremental innovations [2] - not disruptive innovations.

2.       Skunks Work: Many organizations have discovered that truly disruptive innovation must be drawn on a clean slate. Many form special sequestered teams, like Lockheed's famous Skunk Works, or spinout ventures totally separate from the mother company. The first thing these teams do is throw out the rule book  - including big company initiatives like DT. When disruption happens, you can be certain DT was probably not the enabler. The skunks work better without it.

3.       Not in My Backyard: The largest inhibitor of DT initiatives is often the people and jobs in the transformation crosshairs. Most people are eager to champion DT initiatives for someone else's group but cringe when smart chatbots are proposed to overhaul their call centers. Fifth columnists are very effective at whittling down the scope of radical change initiatives to something non-disruptive.

To be clear, DT will yield very impactful results across all industries in 2020. However, these will be incremental innovations and not corporate or market disruptions.

2020 will be a dynamic year in IT just like every year since Turing bought his first slide rule. But some of the expectations being set will fall woefully short of their objectives. This time next year will reveal if these anti-predictions survive the zap light. Bzzzzt.

##

About the Author

Mark Campbell 

Mark Campbell is the Chief Innovation Officer at Trace3. Prior to his role as CIO, Mark was Vice President of Research at Trace3 where he and his team reviewed forms of emerging technology including over 1,000 tech start-ups each year. Based out of sunny Denver, Colorado, Mark combines an insider's advantage from leading venture firms with his 25 years of real-world IT experience to help enterprises discover, vet, and adopt emerging technologies. His ‘from the trenches' perspective gives Mark the material for his frequent articles and speaking engagements.

 

References

[1] J. Sanders, "Public Cloud Lock-in concerns incongruent with successes seen in multicloud deployments," 451 Research, 2020.

[2] C. M. Christensen, The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail, Boston: Harvard Business School Press, 1997.

Published Tuesday, February 04, 2020 7:25 AM by David Marshall
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