Business Apps of AI
Smart City tech potential: Commuting
- 15-20% reduction in this - Use of digital signage or mobile apps - Ease road congestion
Step 1: formulate your customer experience strategy
- 5 Steps to Understand Customers More Efficiently Through AI - - Help lead the CX initiative - Set the strategic goal of making CX a priority across the org - Make sure that measurements are in place to track CX - Keep reinforcing the importance of CX
Step 2: map and analyze your customer journeys
- 5 Steps to Understand Customers More Efficiently Through AI - - Who and why - How - When and where - What
Step 4: decide whether to build or buy
- 5 Steps to Understand Customers More Efficiently Through AI - Our enterprise AI survey says that 45% of businesses would buy an AI solution — either buy and then customize it to their industry or buy an already customized solution. This is compared with 33% that build their own custom solution internally.
Step 3: familiarize yourself with the AI solutions(s) suitable to understand your customers
- 5 Steps to Understand Customers More Efficiently Through AI - There are a myriad of AI technologies to use currently, but for this specific task — understanding your customers from a CX perspective — they can be categorized in three buckets: - Language - Vision - Sentiment
Step 5: track and measure success
- 5 Steps to Understand Customers More Efficiently Through AI - _____ and ______ success
Limitations of AI use: Potential bottlenecks
- Access to software libraries and other tools - Organizations able to scale AI deployment
System models
- Cloud-native - Pack-adjunct - Open-algorithm
AI capabilities that could be used to benefit society
- Computer vision - Natural language processing - Speech and audio processing - Reinforcement learning - Content generation - Structured deep learning
Limitations of AI use: Critical barriers for most domains
- Data accessibility - Data quality - High-level AI-expertise availability - High-level-AI-expertise accessibility - Regulatory limitations - Organizational-deployment efficiency
Limitations of AI use: Contextual challenges
- Data availability - Data integration - Access to technology - Privacy concerns - Organizations receptiveness
Limitations of AI use: Critical barriers for select cases
- Data volume - Data labeling - AI-practitioner talent availability - AI-practitioner talent accessibility - Access to computing capacity
Smart City tech potential: Better Health
- Improvement in disability-adjusted life years (DALYs) - Prevent, treat, and monitor chronic conditions - Remote patient monitoring
Smart cities change infrastructure and create opportunity for private-sector partnerships
- Infrastructure needs can be met dynamically without long-term investment (uber vs. taxis) - Possibilities of initial financing from private sector - Adding private sector actors increases adoption and innovation in smart cities
Strategic Technology Trends for 2018: Recommendations
- Pilot robots immediately - Map cultural, ethical, & societal effects - Use persona-based analytics to determine how humans & machine work better together
Benefits of digital labors
- Productivity/performance - Employee satisfaction - Scalability - Quality/Reliability - Auditability - Cost efficiency - Consistency/predictability
Reshaping Business with Artificial Intelligence
- Surveyed 3000 executives and managers, 2/3 from outside the US - AI could be a risk if competitors adopt things first - Essentially, you just need to conduct good change management and make sure you acquire good talent, also get buy-in from others ---- People don't quite understand that you need data for machine learning to work, and have other misconceptions
Smart City tech potential: Environmental benefit
- automated energy usage - water consumption tracking - Air quality sensors to determine pollution sources
value-adding cases
AI has massive potential, but enterprise architecture & tech innovation leaders struggle to identify _____-_____ _____; overwhelmed by number of available AI tech
270%
AI implementations up _____ in the last four years
understanding the context of data
AI is limited on ______ ___ _______ _____ ______this is where humans excel (i.e. a chatbot is poor use of AI, training it to do human tasks, will "replace humans") - we need to rethink the use cases for AI - AUGMENT TECH W/HUMAN
Down, down, up, up, up
As value of machine prediction goes ____, value of human prediction goes ____. while value of data goes ____, human judgment goes____, and action goes ____
3 stages in the journey for full utilization of AI
Assisted Intelligence, Augmented intelligence, Autonomous Intelligence
resilience, public health and safety
By 2020, 50% of smart city roadmaps in emerging economies will incorporate strategies on ______, ____ _____, and ______ to stimulate economic development.
20%
By 2020, ______ of operational bank staff engaged in back-office activities will rely on AI to do the nonroutine work.
a digital harassment policy
By 2021, 70% of enterprises will integrate AI to assist employee's productivity, causing 10% of them to add _ _______ ________ _______ to workplace regulation
the social impact of a digital economy
By 2021, ___ ______ ______ ___ ___ _______ _______ will force 75% of cities with smart city ambitions to continuously audit for biases toward citizens.
integrated services and data analysis
By 2023, 30% of smart city initiatives will lose political and public support and be discontinued for lack of ____ __ ____ ______
centaurs
By 2023, AI combines with human programmers to create "________" performing 50% of traditional programmer workloads — doubling the throughput of stand-alone programmers.
fraud detection
Chatbots have displaced ______ ______as the top application of AI
people
City leaders are finding smart-city strategies start with ______, not technology
Tripled
Companies doing AI _______ in 2018
Top 3 barriers to AI implementations
Defining AI strategy Identifying use cases for AI Lack of staff skills
Five things to keep in mind about AI
Develop a thesis on time to AI impact, AI growth will likely be exponential, Trust the machine, Know what to predict, Manage the learning loop.
Narrow AI
Examples are: Playing strategy games, writing newspaper articles, etc.
Smart City tech potential: Fighting Crime
Help with determining detection strategies -8-10% reduction in fatalities -30-40% lower theft - 20-35% reduction in emergency response times
build, buy or partner plan
How to automate intelligently: Define a _____, _____, or ____ ____ to unlock the right talent and technologies
priorities and the culture transformation
How to automate intelligently: Link the automation strategy to business ______ and the _____ _____ agenda
talent needs and skill gaps
How to automate intelligently: Perform an organizational review to prioritize potential automation projects, identify _____ _____ and ____ _____
creating 2.3 million and eliminating 1.8 million
In 2020, AI becomes a positive net job motivator, creating ________ jobs while only eliminating _________ jobs.
How tech is used
Issue w/tech taking jobs is unfounded, issues stem from ____ ____ ___ _____, not tech itself
Augmented Intelligence
ML capabilities layered on top of existing info management systems work to augment human analytical competencies Lane detection drivers
problems and behaviors
Rather than construct work from products and specialized tasks, we can choose to construct work from ________ _____ _______
State of AI in the Enterprise: Risks
Risks: cybersecurity vulnerabilities, making the wrong strategic decision based on AI, legal responsibility, failure in mission-critical context, regulatory non-compliance risk, erosion of customer trust from AI failures, & ethical risks
infrastructure and baseline starting points
Smart City applications perform differently from city to city based on _______ ____ ______ ______ ____
local labor markets more efficient and lower cost of living
Smart cities don't create jobs, but make _____ ____ _____ ___ ___ and ____ ___ __ _____
environmental, economic and demographic
Smart cities has been used by local government and industry as an umbrella for the digital change management necessary to tackle _____, ______, and ______ issues in a holistic urban
Examples of intelligent interfaces
Snapchat & AR with adding features like filters, faces, etc. Delta's use of facial recognition tech to decrease passenger boarding time
risk & change
State of AI in the Enterprise Companies should improve ____ & _____ management
talent, not just tech skills
State of AI in the Enterprise Early adopters need the right mix of ____, ____ ____ ___ _____ to accelerate progress
10-30%
Study finds that some smart tech can improve quality of life indicators by __-__%
Usage
Three layers of a smart city: (by cities, companies, and the public)
Technology base
Three layers of a smart city: (smartphones and IoT sensors)
Applications
Three layers of a smart city: (translate data into alerts, insights, and actions)
2020
Through what year will employers with data- and AI-for-good programs will have 20% higher retention and 50% faster data and analytic job-fill rates.
False
True or False? AI can be free of bias
False
True or False? AI is all about algorithms & models
False
True or False? AI will only augment/automate repetitive jobs that don't require expert skills
True
True or False? Black-box AI doesnt need to be explainable
False
True or False? Deep neural networks are the best AI
False
True or False? Intelligent machines learn on their own
Truth! shes the best!
True or False? Kevin's wife Diane is pretty damn great for helping make the quizlets ;P
True
True or False? ML & AI are different
False
True or False? There is one best AI vendor
True
True or False? We need AI strategy
True
True or False? AI doesnt work the same was as a human brain
Applying AI for social good
Use cases: - Crisis response - Economic empowerment - Education - Environment - Equality and inclusion - Health and hunger - Information verification and validation - Infrastructure - Public and social sector - Security and justice
Science Fictioning
When AI works beyond just improving efficiency to driving transformations
artificial intelligence
ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, & even exercising creativity
Robotic examples
assisted surgery, greetings at a hotel, prevention of crime, advanced agriculture, cities of the future, safer law enforcement, self-driving taxis, safer automobile transportation, & intelligent things on the battlefield
Four categories of intelligent things
autonomous vehicles, drones, robotics, & AI-driven IoT
Intelligent interfaces considerations
bandwidth, cloud & edge computing, & IoT
Intelligent Things: Recommendations
challenge status quo, anticipate cyber hacking, manage robotics data risk, create threat models of IoT communication & entry points, understand cultural & societal effects, identify how distribution models will shift, expect indirect impacts to be just as effective as direct impacts, use persona-based analysis to determine how humans & machines work better together
Utilize enterprise architecture practices
customer journey maps, scenario planning, road-mapping, & innovation exploration
Machine Learning
detect patterns & learn how to make predictions and recommendations by processing data & experiences, rather than by receiving explicit programming instruction; also adapt in response to new data & experiences to improve efficacy over time - Supervised, unsupervised, & reinforcement
Make vs buy dilemma
do you give someone all your data or do you do it yourself?
Benefits of AI
enhance current products, optimize internal operations, make better decisions, optimize external operations, & free workers to be more creative
Popular & easier path
enterprise software w/AI, co-development w/partners, cloud-based AI, open-source dev tools, automated ML, data science modeling tools, & crowdsourced dev
State of AI in the Enterprise: Issues
implementation challenges, integrating AI into roles & functions, data issues, cost, lack of skills, & challenges in measuring & proving business value
Assisted Intelligence
large-scale data programs, power of cloud, & science-based approaches to make data-driven business decisions Cruise control
Intelligent interface
latest in series of major tech transformations that began w/transition from mainframes to PCs w/emergence of web & mobile - more natural, contextual, & ubiquitous - Voice use cases like Siri & Alexa - Computer vision, gesture control devices, embedded eye-tracking platforms, bio-acoustic sensing, emotion detection/recognition technology, & muscle-computer interfaces
Intelligent things: semi-autonomous or fully autonomous
operate intelligently & autonomously unsupervised for a defined period to complete a task; self-driving vacuum
Autonomous Intelligence
processes are digitized & automated to degree whereby machines, bots, & systems can directly act upon intelligence derived from them Self-driving cars
State of AI in the Enterprise: Moving forward
pursue execution excellence, address cybersecurity risks, apply AI beyond IT function, buy some off-the-shelf apps, staff wisely, decide where to automate vs augment
State of AI in the Enterprise: Needed skills
researchers, software developers, data scientists, UX designers, change management experts, project managers, business leaders to interpret results, subject-matter experts
Foundational tech
robotics, 5G networks, IoT, AI, digital reality, cloud & edge computing
General AI (AGI)
systems that have human level problem-solving across many different domains
High AI investment/High return
tech/media & entertainment/telecommunications & professional services
Intelligent interfaces opportunities
tracking customers' offline habits, new products & solution sets (think, micro-personalization), & efficiency
Smart City tech potential: Community enhancement
two-way communicate with local officials (residents feel connected)
Deep Learning
type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, & can often produce more accurate results than traditional machine-learning approaches; requires lots of data - Convolutional neural network & recurrent neural network
Smart Cities
using tech and data to improve the quality of life