WHY IT MATTERS: Digital Transformation

We grouped the most common mistakes into four categories: (1) mispricing on one side of the market, (2) failure to develop trust with users and partners, (3) prematurely dismissing the competition, and (4) entering too late.

Brick-and-mortar retail stores need to up their game. Technology could give them significant boost.

They indicate that consumers' mobile phones will soon become remote controls for the commercial exploration of the physical world, or, better said, they will become tools to unlock Easter eggs within shopping experiences that will vary from one individual to the next.

“We chose right from the very start to not hide the technology,” Hanrahan explained.

In this report, TDWI uncovers deeper insights that give users new perspectives on business questions. AI will transform BI and the way people make decisions and act. Rather than start with a hypothesis, data analysts will begin with an AI-driven insight. Instead of querying data to prove or disprove their hypothesis, users will query data to expand or validate a machine-generated insight or recommendation—or they might act on the AI-based insight at face value. But to get to that point, AI-infused BI tools will need to gain people's trust by consistently delivering accurate, relevant, and transparent insights within the context of a business user’s existing workflow.
In the future, AI-infused BI tools will go beyond just surfacing insights; they will recommend ways to address or fix issues, run simulations to optimize processes, create new performance targets based on forecasts, and take action automatically. And yes, machines will make some decisions for us—especially operational decisions in real-time environments. We see this today with fraud detection and online trading systems, but it will become more pervasive.

Digitalization is now going mainstream. For example, the Economist has devoted a full issue to it.  “Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. Meanwhile, artificial-intelligence (AI) techniques such as Machine Learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease.”

Service performance has improved recently for most CPG companies. But getting goods from plant to shelf involves more hurdles and more complexity than ever before.

Digital transformation is a long game. At the same time, it has to start somewhere. Over the years, the biggest digital transformation priority for companies has continued to be the customer experience. That work is aimed at fixing and modernizing the customer journey. Once this happens, digital transformation tackles more sweeping efforts that involve multiple groups. This is why the organizations who are further along the six stages of digital transformation develop customer-centered roadmaps and then divide and conquer against those initiatives across the enterprise.

Digital transformation was initially intended to modernize technological infrastructure, migrating costly, dated, and complicated hardware and software solutions to more agile and scalable cloud and mobile platforms. But since the early days of digital transformation, the speed of technology innovation has only increased. Now the digital in digital transformation has to keep up with all relevant technology trends as they represent disruptive threats and competitive opportunities. This is likely a key driver of 2018’s dramatic rise of substantial digital transformation budgets.

The Montreal Declaration for responsible AI development has three main objectives:​

1. Develop an ethical framework for the development and deployment of AI;

2. Guide the digital transition so everyone benefits from this technological revolution;

3. Open a national and international forum for discussion to collectively achieve equitable, inclusive, and ecologically sustainable AI development.

One of the biggest factors that differentiate the top economic performers from others is how quick and adaptable they are in setting, executing, and adjusting their digital strategies—in other words, the velocity and adaptability of their operating models for digital strategy. Both are necessary for companies to achieve first-mover (or very-fast-follower) status, which we know to be a source of significant economic advantage. So how do they do it? We looked at the frequency with which companies follow 11 operational practices of digital strategy. With the exception of M&A—which typically requires a much longer time frame than the other ten, often due to regulatory reasons—respondents in the top revenue decile say their companies carry out each one more frequently than their peers (Exhibit 1). The link between frequency and performance also holds up when looking at earnings before interest and taxes (EBIT).

Tech Trends 2019: Emerging technology trends can seem both elusive and ephemeral, but some become integral to business and IT strategies—and form the backbone of tomorrow’s technology innovation. The eight chapters of Tech Trends 2019 look to guide CIOs through today’s most promising trends, with an eye toward innovation and growth and a spotlight on emerging trends that may well offer new avenues for pursuing strategic ambitions.

To succeed in the digital age, mall operators will need to instill a culture of fact-based decision making throughout the organization. In addition to implementing advanced-analytics tools, they should invest in collecting more of the valuable data that will inform their business decisions. For instance, they can deploy new technologies (such as beacons, granular Wi-Fi, and facial-recognition cameras) to capture behavioral data. They can launch mallwide loyalty programs to gather individual transaction data and generate insights into the customer journey across the entire mall ecosystem. They can also pursue partnerships with tenants—for instance, by negotiating preferred rents in exchange for data sharing. Armed with robust data and advanced analytics tools, malls have the potential to revitalize and revolutionize not just their own business performance but that of the rest of the retail industry as well.

This report lays out a maturity model for AI adoption in the enterprise. It outlines five stages of AI maturity based on how organizations approach business strategy, data science, product and service design, organization and culture and ethics and governance.

Digital transformations are, by their very nature, complex. There are multiple moving parts, integrated processes and technologies, and the need for expertise that cannot already be found in the company. In most firms, however, there is a small cadre of technology professionals who can impose relative order on the proceedings. CEOs and CIOs should consider the benefits of pulling enterprise architects closer to the center: bringing them to the table with business leaders, devising metrics that reveal the value of their work, and creating the type of incentives that will challenge them and prompt them to stay for the long term. Such an approach is critical not just for limiting risk and protecting against the potential challenges and downsides of digital transformations but also for ensuring a clear upside—a close and lasting partnership between the business and IT.

To support this process we have developed a comprehensive framework that combines four clusters of enablers for digital transformation (shown in the figure below). The “4E-Framework” gives companies a structured approach to analyze and build capabilities that position their business in a rapidly changing commercial environment. In practice, this framework enables companies to focus activities and resources on key strategic areas, and to measure the results.

« Dans tous les domaines où une décision affecte la vie, la décision finale devrait revenir à un être humain »

Cette semaine, qui verra également une réunion du G7, sera aussi l'occasion de la présentation d'un texte historique, la « Déclaration de Montréal » que nous publions ci-dessous en exclusivité. Signée par plusieurs chercheurs éminents comme Yoshua Bengio, professeur à l'université de Montréal, Joëlle Pineau, professeure agrégée en sciences des données à l'université McGill, ou encore Marc-Antoine Dilhac, professeur au département de philosophie de l'université de Montréal, elle dresse une liste de principes pour encadrer les recherches en intelligence artificielle.

Retailers at the Future Stores event in Seattle had a lot to say about where they thought the future of the store is headed. Here are some of the top takeaways.

The Marketing technology category is growing in leaps and bounds. Unfortunately, our most concise view into the category was built in 2011. While companies have been added highlighting new entrants, the Lumascape inspired view has been used primarily to emphasize how complicated and fast growing the space is.

A.B.A: always be automating: Every IT team should have a culture of constant improvement - or movement along the path toward the goal of automating whatever the team feels confident in automating, in ways that are easy to change as conditions change. As the needle moves to the right, the team learns from each other

As digital technology erodes profit and revenue growth, why aren’t companies responding with bold strategies?

We’re announcing seven principles to guide our work in AI. We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. There we made clear our intention to take a long-term perspective, even if it means making short-term tradeoffs. We said it then, and we believe it now.