By finding smart ways to make the most of in-house potential, tapping into the growing global ecosystem of expertise, and being open to automation and other tools that might help bridge gaps, leading companies are drilling new sources of advanced analytics talent.

KPIs should be central organizing principles for leadership investment in data and decision-making.

Feeling pressure to become more agile and “networked,” organizations tend to overwhelm employees with collaboration demands, putting a drag on performance and engagement. But through analytics, they can scale collaboration more effectively, improve collaborative design and execution, drive planned and emergent innovations through networks, streamline work by diagnosing and reducing collaborative overload, and engage talent by identifying social capital enablers.

To win over skeptics, champions of AI-driven customer service need to show more benefits than just saving money. There are economic, technical, political, and cultural barriers that savvy managers must overcome in order to successfully integrate AI into their company’s customer service systems.

The future of customer service will allow bots and humans to focus on what they do best. For chatbots and virtual agents, that is handling routine cases quickly and efficiently. For the human supervisors, that is solving more complex problems, using empathy and emotional intelligence.

Augmented reality sensor data creates a feedback loop for businesses to better understand how a product is being used and where opportunities for customization exist.

Digital customer service agents (known as virtual assistants, chatbots, or softbots) are typically used to sift through and process only the most straightforward customer inquiries, such as requests for basic information. At most companies, complex issues get passed along to human agents. In that regard, public sector agencies in Australia are ahead of the curve: They are using digital agents to handle complex inquiries from citizens, and businesses stand to learn much from these applications.

Leaders and managers must inevitably consult the analysis, advice, and research of people whose expertise exceeds their own in a variety of domains. Getting the most from that perspective means understanding precisely how to question the experts’ wisdom, no matter what form it takes. Eight specific lessons can help leaders and managers use others’ expertise to the greatest possible advantage in everyday business decisions.

While most organizations still rely on traditional methods such as résumé screenings, job interviews, and psychometric tests to find the right people and match them with the right roles, three new approaches to talent identification are quickly gaining traction. Gamified assessments, digital interviews, and candidate data mining have the potential to make hiring more precise and less biased, as long as they’re used responsibly.

A relatively small number of purchase and return metrics can accurately predict customer profitability — and the likelihood of policy abuse. By using analytics to identify the few customers who cost the company the most money by abusing return policies before they make their next purchase, companies can prevent abusive returns, avoid PR disasters, and boost their profitability.

David Harcourt of Yum! Brands Inc. shares his research and experimentation with HR analytics and shows how to align strategic talent management goals with HR analytics projects and activities.

AI and IoT offer significant benefits — as yet untapped — for facilities maintenance. In most large companies, maintenance and repair is done piecemeal with tremendous inefficiency, something automated, data-centered management practices could reduce. Operations executives should take notice, because the companies that allocate resources toward putting technology to work in this area are likely to gain an important competitive advantage.

Companies have yet to apply analytics to human resources — but that’s about to change. And lessons learned in applying analytics to customer-focused areas can help avoid mistakes in strategic workforce decisions.

Employee odometers and technostress; big data trumps analytics; profiling the chief data officer.

How to evaluate and implement people analytics; weighing the virtual shareholders’ meeting; the robotic workforce is here and now.

Organizations across an increasing number of sports and levels of competition are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports. And learnings from the sports analytics revolution are applicable to a broad range of other industries.

New advances in machine learning are allowing sales teams to manage huge amounts of data to become more effective and efficient. Our research suggests three main ways machine learning is being successfully integrated into sales processes. First, it allows for a scientific approach that clarifies opaque parts of the sales process. Second, it enables more-effective data-driven experimentation. Third, it automates administrative duties that take time away from higher-value tasks.