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