Demystifying AI for the Enterprise
A Playbook for Business Value and Digital Transformation
Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan
- 418 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Demystifying AI for the Enterprise
A Playbook for Business Value and Digital Transformation
Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan
About This Book
Artificial intelligence (AI) in its various forms ââ machine learning, chatbots, robots, agents, etc. ââ is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets.
With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today's people, processes, and products.
There is still considerable mystery, hype, and fear about AI in today's world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don't consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow's AI.
This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes.
AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow's enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
Frequently asked questions
Information
Chapter 1 AI Strategy for the Executive
Contents
- Introduction
- Applications of AI
- Determining Practical Realization: Considerations
- Definitions
- IMPACT Framework for Enterprise AI
- Imagination
- Maturity
- Dimensions of AI Maturity
- Assessing and Increasing AI Maturity in Your Organization
- People
- Considerations on the Data Scientist Role
- Automation, Amplification, and Augmentation
- Culture
- Transformation
- Best Practices for the Use of Data in AI
- Volume
- Variety
- Velocity
- Value and Veracity
- Value
- Veracity
- Data Fidelity over Data Quality
- Conclusions
- Notes
Introduction
- Competitive disadvantageâan enterprise that puts all its data to work via AI is better placed to create new advantages and positive differentiation w.r.t. the competitionâexisting and upcoming. In todayâs globalized landscape, the competition is not only known entities within a country but also foreign/transnational enterprises and startups/unicorns that are leveraging AI extensively
- Customer and employee experiencesâusing AI to understand a customerâs or employeeâs personalization needs, and address it via micro-campaigns/products/experiences creates a better human experience and drives competitive advantage
- Suboptimal leverage of human resourcesâby focusing on mundane and robotic tasks in lieu of enabling employees/associates/professionals focus on what matters more
- Misalignment between business strategy and tactical solutionsâtechnology is no longer a nice to have. The best business strategy canât be realized unless the enterprise can identify/design/develop appropriate data and technology solutions
- Top line and bottom lineâby not using AI, an enterprise wonât be in the best position to manage opportunity or risk, and maximize top- and bottom-line revenues, margins, and profitability
- Prepare for new conditionsâmeasuring whatâs working or not and predicting known and unknown âunknowns.â Managing appropriate/corrective measures are increasingly being recognized as being necessary to thrive, or at the very least, survive in turbulent times and when faced by âblack swanâ events such as pandemics, war, major recessions, and market conditions (the Kodak Effect being a well-studied example of not responding in a timely fashion to market and user preferences)
Applications of AI
- Customer engagement and happiness
- Marketing and Sales
- Supply chain and operations
- Finance and accounting
- Regulations, compliance, and risk management
- Product development and pricing
- Talent acquisition and human resource management
- Operations
- More granular/specialized subdomains and processes
Determining Practical Realization: Considerations
- First, do no harm to your customers, employees, or business
- Build continual feedback loops to ensure and maintain a high degree of trust
- Historical data can be biased; pay attention to resulting algorithmic results and how these results are integrated into transaction systems or operational/business processes
- Set aside time and money to address ethical/responsible dimensions on each individual project
- Encourage collaborationânot just between data scientists and other technologists; but more importantly, between data scientists, data and analytics leaders, business stakeholders, and customers
- Interpretability (enabling a data scientist to understand how the algorithm came about its results from mathematical and data perspectives) and explainability (the ability for a nontechnical user of the solution to understand the process and results in plain language) are related but also serve different uses and audiences
- Understand that we will have to address tradeoffs between interpretability, explainability, time, and accuracyâand there is no single formula. Keep in mind that not every AI solution needs to be interpretable to the nth degree, depending on the use case it supports (some use cases will require more; others will need less)
Definitions
- Human intelligence, is the âmental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate oneâs environment.â1
- Artificial Intelligence, is âa branch of computer science dealing with the simulation of intelligent behavior in computers, or the capability of a machine to imitate intelligent human behavior.â2 In this context, AI as defined for the purposes of this book is the ability to decide on an action or make an inference given some data or information.
- Structured data: These are data whose meaning is agreed upon ahead of time and is defined at an atomic level. For example, a table containing names and contact information for customers would typically be structured data. It can be arranged in rows and columns where each row pertains to a different customer, and each column i...