Irving Wladawsky-Berger

About Me

Irving Wladawsky-Berger, a Guest Columnist at WSJ CIO Journal, is Research Affiliate at MIT Sloan School of Management, Adjunct Professor at Imperial College, London, and Chairman Advisory Board at r4 Technologies.

How to Support the Widespread Adoption of AI

AI is the biggest commercial opportunity for companies and industries over the next 10-15 years. Yet, despite the promise of AI, many organizations’ efforts with it are falling short. Most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only a few firms are engaged in practices that support widespread adoption. Why the slow progress?  To support the widespread adoption of AI, companies must make three fundamental shifts.

Blockchain Marks the Next Step in the Internet’s Evolution

While the open, decentralized internet is alive and well in the InternetOne layer, the InternetTwo layer has become highly centralized, dominated by a few huge companies. Blockchain technologies have the potential to address these serious internet problems by enabling the exchange of the critical data required to validate identities in a secure, decentralized manner without the need for a central platform or other intermediaries. Over time, blockchain-based applications could be used to coordinate the self-organizing activities of large numbers of individuals and institutions in a secure and decentralized manner, as was the case with the internet’s early objectives.

Blockchain Beyond the Hype

Is there too much hype surrounding blockchain?  Absolutely,… but not surprising.  All potentially transformative technologies are oversold in their early stages.  Remember the dot-com bubble of the late 1990s.  Blockchain is still in its early phases of experimentation and adoption.  Much work remains to be done on standards, platforms, interoperability, applications and governance.  But, does blockchain have the potential to become a truly transformative technology over time?  Yes, said McKinsey in a recent article on the strategic value of blockchain beyond the hype. 

Using Agile Processes to Develop AI-Based Solutions

The behavior of an AI system based on machine learning depends on the information we use to train its algorithms rather than on the precise set of software instructions that tell the computer what to do. Agile development is particularly well suited to AI-based products and systems, where it’s important to involve users early in the development cycle to help test, refine and improve the AI features in the product by sharing their real-time feedback with the development teams.

The State of AI in the Enterprise

Most current AI projects are focused on augmenting the capabilities of the workforce, and AI is transforming many jobs, leading to moderate or substantial changes in roles and skills. Also AI empowers employees to make better decisions, and it will increase job satisfaction.  Perhaps the biggest advantage could be new ways of working that blend the best of what machines do with human experience, judgment, and empathy. AI-based augmentation of workers will fuel new ways of working.

The Blockchain Value Framework

The blockchain value framework is aimed at helping organizations identify the concrete value of blockchain technology in their use-case proposals and build a corresponding business case. The framework has three distinct dimensions: improved productivity and quality, increased transparency among parties, and reinventing products and processes.  Each dimension includes a distinct set of blockchain-enabling capabilities that provide a solution to a concrete pain point or present an area of opportunity. In addition, the framework will help identify where the real value will be created. 

The Business Value of Augmented Reality

The augmented reality (AR) will become the key interface between humans and machines that’ll help bridge the gap between the digital and physical worlds. AR comprises a set of technologies that superimposes computer generated digital data, images and animation on real world objects.  The technology is still in its early stages. Today, most AR applications are focused on entertainment and delivered through smartphone and tablet apps. But, they’re being increasingly applied to commercial and industrial applications. 

Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Machine learning is a statistical modelling technique, like data mining and business analytics, which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships.  Determining causal relationships requires tried-and true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions. And, in particular, as we’re frequently reminded, correlation does not imply causation. Here are the key benefits of AI solutions based on augmenting statistical methods with domain-based models.

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