Potential Impact of Future
Jim Rutt, CISSP, CISM, CISA, CGEIT, C|CISO, CRISC, CCSK
CIO, The Dana Foundation
• CIO of the Dana Foundation
• President/Chairman of the Board of Technology Affinity Group (TAG)
• Advisor to multiple VCs and dozens of startups
• VCs: Lightspeed, Work-Bench
• Minerva Labs-advanced evasive malware platform
• PIXM- Anti PHISHing leveraging Computer Vision
• Stealth-Edge Computing
• Few others: advanced SIEM, streaming analytics, email inoculation, etc etc
• Key focus on practical application and governance of emerging enterprise
Putting a name to the future
• Deloitte: “Symphonic Enterprise”
• Gartner: “Digital Mesh”
• Work-Bench: “Digital Transformation”
The Key conveyance is the continuance of CONVERGANCE, or..
Business intimacy as opposed to Business Alignment:
“It’s about the synchronicity of action that becomes possible between
individuals and groups who are acting from a shared and common perspective and
Marc Shiller, “The 11 Secrets of Highly Influential IT”
Lots of media hype about the impact
of future tech…
Dire predictions for the human
• “Humans Need Not Apply” YouTube video claims 45% of currently employed
people will be out of a job thanks to AI (http://bit.ly/1mITYwo)
• There will be changes in the workforce, but the more likely scenario: what
Deloitte calls “No-Collar Workforce”, where AI-enabled components work with
humans in both an augmented and automated fashion.
……but in terms of enterprise impact,
……it may take some time
• We’re still dealing with slightly less stimulating issues…
(Practical) Tech in The Enterprise
with (Practical) Future Impact
• Cloud Native
• Cybersecurity/Quantum Encryption
• IoT/Edge Computing
• Blockchain/Smart Contracts
• Serverless Computing
• Smart Dust
drives our algorithms for demand forecasting,
product search ranking, product and deals
recommendations, merchandising placements,
fraud detection, translations, and much more
AI right now
• Vendor oversaturation similar to late 90’s Dot Com era.
• Too many entrants, too many scattershot approaches.
• Location of data (to avoid lock-in) biggest concern for enterprises
• Cognitive agents
• Self-Driving Cars/Trucks
• Good progress on all fronts here.
Where is AI’s true future impact In
the Enterprise? Behind the scenes
• In operations, most notably business process re-engineering.
• Augmentation of current work processes as well as Automation.
• “Machine learning drives our algorithms for demand forecasting, product search
ranking, product and deals, recommendations, merchandising placements, fraud
detection, translations, and much more”- Jeff Bezos
AI’s True Impact in Enterprise:
Powering Systems of Intelligence
From Jerry Chen, “The New Moats”
Cloud vs. Systems of Intelligence
• Cloud moat =unbundling “capabilities” into individually deployed “microservices”
for scale advantage
• Systems of intelligence moat =bundling “capabilities” into processes advantage
• SCALE ADVANTAGE (broad) vs. PROCESS ADVANTAGE (competitive advantage)
Systems of Intelligence examples
• Manufacturing: predictive maintenance on equipment
• Insurance: Automation of insurance filing claims
• Pharmaceuticals: Optimization of R&D resource allocation for a portfolio of drug
candidates in clinical trial
• Financial services: Automated customer interactions with chatbots
• Process advantage drives Systems of Intelligence
AI/ML: Future Impact
• There will be a Github of AI*, as we are already seeing with shared algos and
models similar to plain vanilla source code on GitHub.
• Vertical impact Drivers:
• Finance: Compliance has become the prime mover. AI will augment this discipline
• Pharma: Increase in efficiency of predicting successful compounds
• Talent Alignment Challenges/Opportunities
• Today most talent is oriented towards consumer applications
• A path to exploiting the (much larger) opportunities in enterprise will need a solid plan
for talent development/realignment.
*Work-Bench 2017 Enterprise Almanac
AI/ML: Future Impact Continued
• Deep Learning and more exotic forms of AI are great in theory, but difficult to
implement in practice due to the intensive parameter tuning and amount of data
required to train an algorithm…
• New trend: make AI methods that require less data more accessible by adding
representation schemes from “traditional” ML
Corporate Attitude on AI
• Ambitious attitudes: “AI is a competitive differentiator. We want to own the
model, we don’t want Palantir to own it.”
• •Smart recruiting tactics: Avoiding talent wars with the webscales by sourcing
data scientists in India rather than US, and Masters-levels rather than PhDs.
• •…But some skepticism, particularly around deep learning:
• “We have lots of existing regression models that are finely tuned. Deep learning
is just going to be incremental and more expensive right?”
Other challenge: Siloed efforts
• Line of business vs. CDO/governance oversight
• Enterprise data science functions are decentralizing to get more funding/buy-in
from across the enterprise.
• Most organizations lack culture of collaborative data exchange, and data
governance teams slow projects down.
• Value will be in tight integration of ML workflows spanning the entire pipeline
CLOUD NATIVE: THE NEXT GENERATION
• 2017 is shaping up to be a pivotal year for Fortune 1000 deployments of cloud
• Container orchestration is the ‘VMware’ anchoring the cloud native ecosystem.
Exactly who will play this critical role will become clearer this year.
• Cloud native is reshaping databases, middleware, big data, developer tools, and
Legacy to future impact: Containers
are the key
• Apps on VM’s
• Containers on VM’s
• Containers on Bare metal
• Containers are lighter — 10’s-100’s of MBs vs. multiple GBs, just the right size for
component based microservices
• Containers are faster — they can be spun up and down in seconds vs. minutes to
realize the true agility, resilience, and portability of cloud computing
• Containers are more efficient — you can fit 4-8 times as many app components (or
microservices) on a bare metal container server than you can on a VM because of the
way containers share OS resources to free up space
• Microservices extends service-oriented architecture by decoupling apps into
single-purpose services that communicate with other microservices via APIs or
Source: PWC “Agile coding in enterprise IT: Code small and local”
Cloud Native Adoption
• Driven by developer demand for containers.
• Container Orchestration is the new data center operating system:
• AWS: Mesos
• Microsoft Azure: Kubernetes, Docker Swarm, and Mesos,
• Google :Kubernetes
• New development paradigms like functional programming and pipeline-to-
serverless fit perfectly towards a cloud native approach.
Future Impact of Cloud Native
• Middleware will be disintermediated
• Former ESB functions will be taken over by pipelines and code distributed within app
• Application servers reduced to function level actors
• Database Paradigm shifting from single point of non-agility to a disruption of the
• Middleware, once a core layer of the IT stack, is shedding significant weight as
middleware functions now reside in distributed code.
• Functional pipelines (aka “serverless”) = no more complex event processor
• Ephemeral snippets of code govern app data traffic, rendering the need for
dedicated services for business rules obsolete.
Streaming: Data and Apps in one stack
• STREAMING IS THE NEW COMPLEX EVENT PROCESSING SERVER & ESB
• Data and app stacks have been separate until now… Container orchestrators
like Kubernetes and Mesos distribute data workloads better than Hadoop’s
Yarn. Spark, Kafka, Herron and other new school stream processing engines all
integrate directly with container orchestrators
• SecDevOps blurs the lines between networking and application security as the
race for cloud-native security products intensifies.
• Beyond the 1%: SOIs as consumable microservices will bring advanced security
technology to the 99% of companies who previously couldn’t afford.
• Systems of Intelligence (SOI) is where its at.
SOR PLATFORMS WILL HAVE TO EVOLVE
INTO SOIS TO REMAIN COMPETITIVE
• Gartner SOAR
• Distributed analytics will be critical for remote/low-bandwidth industrial IoT
• IoT is potent for competitive advantage amongst industrials like gunpowder was
for kingdoms of the 1200s.
• Industrial IoT = earlier than most of us think because distributed infrastructure
remains in its infancy.
• Security for IoT will spawn directly from distributed analytics architectures.
• The next frontier is systems management software bridging disparate IoT
Real value of IoT
• IoT = new era of industrial competition
• IoT requires highly distributed infrastructure
• Industrials are building IoT platforms — highly specialized PaaS not available
from traditional cloud giants
Edge Computing and IoT
• Compute resources will slowly shift from the cloud to devices
Future Impact: Edge Computing
• 3 Key impacts
• Compliance localization
• Reduction of chatter needed to aggregate data
• Bring intelligence to static CDN paradigm
Benefits of edge
• Bridges gaps in:
• •Networking throughput that render real-time data processing too slow for the
• •Compute for local data preprocessing that may be too resource intensive for
• •Intermediary data store for efficient, spoke-hub distribution of sensor data
• AWS Greengrass
• Governing data flow will be a new analytics architecture as ANALYTICAL TOOLS
BUILT FOR THE INTERNET DON’T WORK FOR IOT
• Distributed analytics architectures instrument deeply into endpoints in the
gateway, and thus will be the providing data to security solutions focused on
device anomaly detection and distributed policy-based prevention.Traditional
security vendors talk a big game about IoT but they are going to struggle to get
into the industrial space because operators aren’t going to want to instrument
connected assets 10 ways like IT does in the data center.
• Because of this dynamic, distributed analytics vendors have an opportunity to
become security vendors themselves.
• Vertical AI software is highly specialized, and creating a full stack solution tuned
to a particular use case often means developing proprietary hardware to obtain
data from older, non-IoT enabled physical assets.
• These startups intend to convince OEMs to manufacture the devices on their
behalf. We believe this wishful thinking because OEMs will not be able to extract
enough value from hardware purpose-built to serve even the largest of vertical
• “But perhaps most mind-bending of all is considering what happens when
sensors, antennas, and even computing equipment can be combined into
information gathering devices on the microscale. This is a concept known as
“smart dust,” ..what will happen if miniaturization continues on its current
trajectory—the point at which devices can be scattered to the winds inthe
millions, billions, or even trillions to measure the world in breathtaking detail. Big
data and the Internet of Things gone wild.”-Jason Dorrier, Singularity University
• Visual and spatial analysis
• Paired with AI, powerful future impact
• Robotic Process Automation (RPA)-”Robobosses”