A case study report from MIT SMR Connections and AWS spotlights advanced AI implementations at Liberty Mutual, Samsung SDS, and Zalando, and offers expert perspectives on the issues that enterprise leaders must work through as they develop strategies for applying cognitive technologies in their organizations.

Success in analytics requires having data you can trust. On Feb. 14, MIT SMR Connections and SAS will hold a Twitter chat on the best practices of building a foundation of trust toward data.

An infographic highlights research into the three dimenstions of trust-building practices that enable analytically mature organizations to deliver more value via data-driven innovation.

New research by MIT SMR Connections and SAS shows that organizations with advanced use of analytics and AI are intentionally building a foundation of trust across three critical dimensions to gain value from these technologies. Those applying analytics that incorporate AI-based technologies are fostering trust in data quality, safeguarding data assets and customer privacy, and developing organizational cultures that trust data-driven decisions.

AI-powered systems are increasingly joining humans in the workforce. Dain Hansen from Oracle and Thomas W. Malone from MIT Sloan explore how humans and computers working together will bring new capabilities to the enterprise — while requiring management to consider new strategies for process and organizational design.

Retailers have already invested in tech to optimize marketing and business processes, but new AI-based tools have the potential to automate routine work and support better decision-making. Pratik Pal from Tata Consultancy Services provides an industry perspective on where algorithmic retailing can create the most value; Sharmila C. Chatterjee from MIT advises retailers on important success factors in implementing the new technology.

Technology is redefining how businesses plan their workforce resources, thanks to gig economy platforms and demands for talent with new skills. These customer demands, and tech-enabled competitors, require new approaches by incumbent staffing companies. Nagaraj Ijari from Tata Consultancy Services offers an industry perspective on how to succeed in the face of these changes; Geoffrey Parker from Dartmouth and MIT suggests how enterprises can compete with new platform-based competitors.

How are brands working through technology, culture and workforce issues to implement data-driven strategies? Marketing leaders at Sprint, Bayer, and Tapestry tell their stories: where they started, how they’ve managed technology and organizational change, what they’ve tried and what they are aiming to achieve.

Despite a solid track record of professional achievements some leaders are nagged by insecurities as they ascend the corporate ladder. Secret fears of not being truly qualified, even when unfounded, can ultimately hamper actual job performance. What business trends seem to be exacerbating “imposter syndrome,” and what can executives do to confront and banish it?

Michael E. Porter and James E. Heppelmann discuss which AR use cases are generating the most value today and the five questions for incorporating AR into your own strategy and implementation plans.

Pete Maulik and Joey Bergstein discuss three fundamental truths for building an effective future-focused growth strategy.

Michael Schrage and Mark Foster discuss how technology leaders can best create and inspire change. The have a free-flowing conversation about building the capabilities, talent, culture, and communications necessary for the kinds of experiments that lead to breakthrough innovation.

A webinar on how a documented data and analytics strategy can serve as a blueprint to guide marketers and decision makers in their daily roles.

Nearly 90 percent of senior marketers in a recent survey said an understanding of user journeys across devices and channels is critical to marketing success. No wonder: Leading brands are those that build a foundation of data and analytics to deliver personalized, relevant experiences throughout the customer journey. Determining and measuring the most effective customer engagements is something each organization must do through experimentation, which requires new skills, mindsets and processes.

AI is making headlines — and not just in futuristic technologies like self-driving cars. It’s transforming business processes in established industries, from retail to financial services to manufacturing. But what’s the best way to adopt AI for your organization?

Amid the pressures of fast innovation and consumer expectations for personalized delivery experiences, high-tech businesses are working with logistics partners to instantaneously see data patterns from the end-to-end supply chain. Through predictive, prescriptive and real-time supply chain analytics, high-tech companies can get the insights they need to reduce risk, increase agility and ensure customer satisfaction.

Most businesses have embarked on their IoT journey or plan to, according to a recent study. To move beyond simple goals and realize the transformative benefits of IoT, it’s essential for these organizations to identify key market opportunities, encourage innovative thinking, invest in the IT foundation, disrupt business-as-usual, and incorporate a well-designed, centralized IoT platform into their strategy.

Developing and deploying successful analytics technologies means recognizing that the human factor is paramount.

Many financial firms in South Africa are investing in analytics technologies and human capital to build capabilities that strengthen customer relationships and set the stage for long-term growth through data-driven services.

The Internet of Things represents an unprecedented opportunity for businesses. To realize IoT’s full economic impact, however, businesses should adopt a “systems” rather than a “things” mindset. By doing so, they will see the value of using IoT data to understand—and then optimize—complex systems, be they supply chains, production plants or smart cities. By investing in an integrated, scalable analytical platform, businesses can open up possibilities for staff to make sense of the data generated by IoT and realize its transformational promise.