- Machine learning is widely embedded in many new technologies and solutions, delivering comprehensive insight into business metrics and improving data-based decision making
- First Horizon National Corporation, a leading financial services company, uses an NLP-powered solution to deploy and analyze advanced free-text employee surveys
- Blockchain represents a decentralized, encrypted secure system of record, with applications in government, HR, and countless other industries
This article on AI innovation 2019 originally appeared in Forbes.
In today’s world, every successful company is a tech company.
It’s become impossible to separate business strategy from technological innovation, so everyone from retailers to healthcare professionals are investing heavily in tech solutions to help them market, improve offerings, and drive business. We’re also living and working in the age of industry overhaul, where tech companies like Google, Netflix, and Uber have essentially obliterated longstanding giants like Zagat, Blockbuster, and traditional taxis. The underlying message is clear: adapt to the digitalization of the workplace, or become obsolete.
For many executives, this push to embrace innovation is perplexing due to the constant deluge of exciting new technologies. The U.S. economy grew 2.9% last year, but technology is expensive and examples abound of companies that went bankrupt because they invested in the wrong technology—or the right one, but too soon.
Fortunately, early-adoption case studies offer opportunities to gauge the effectiveness of new technologies. The following tech trends are revolutionary, rapidly maturing, and have been successfully applied broadly across industries. From these examples, we can all learn how to leverage emerging technology to better serve our employees and customers.
Artificial intelligence (AI) triggers a substantial amount of both excitement and fear as well as lots of media coverage. It’s not a new concept—the term was originally coined in 1956—but developers finally have the processing power and data necessary to train programs to solve organizational problems and optimize efficiencies. In a recent Gartner study, every company surveyed relayed their intention to incorporate AI-driven solutions—with 41% already in the pilot or adoption phase. Machine learning has become increasingly embedded in many new technologies and solutions, delivering in-depth insight into business metrics and improving data-based decision making.
Consider supply chain management, where proper warehouse management depends on maintaining accurate inventory and demand forecasting. While even highly seasoned professionals are prone to under- or overstocking, machine-learning forecasting engines apply algorithms and hierarchies to predict future need with exceptional accuracy, minimizing inventory discrepancies and maximizing revenue. Food giant Nestlé uses supply chain forecasting to improve forecasting accuracy on a global level, with more than 447 factories operating in 194 countries. This strategy improved Nestlé’s sales precision by nine percent in Brazil alone.
Meanwhile, Salesforce debuted a CRM solution that uses machine learning to build comprehensive data-based customer profiles, identify crucial touch points, and uncover additional sales opportunities. Lowe’s in-store “LoweBot” applies sophisticated voice recognition, autonomous movement, and machine learning to assist customers while simultaneously processing inventory and searching for product or price discrepancies. And SPS Companies, Inc., a manufacturing and wholesale distributor, has improved talent metrics and completely redefined its employee experience by implementing an AI-based HCM solution that can identify employee pain points in real time.
Careful data analysis is crucial for organizations to truly understand performance. This insight is increasingly valuable when coupled with analytical benchmarking, which allows organizations to compare themselves with their peers and competitors in terms of web traffic, customer churn rate, or employee engagement. This industry-specific information helps identify gaps in an organization’s performance and can be leveraged to achieve a competitive advantage.
Additionally, AI is bridging the gap between operational and predictive reporting. Predictive analytics can foresee everything from employee retention to long-term weather patterns, and machine learning continuously and automatically improves predictions with experience.
Boston Medical Center applies predictive analytics to determine staff and room allocation during peak times, optimizing scheduling while improving efficiency and wait times. Netflix applies its own advanced algorithms to predict not only whether certain content will be well-received, but also pinpoint exactly which users are likely to enjoy it. And the streaming giant, which has almost single-handedly disrupted the long-standing (and frequently lamented) cable industry, says an astonishing 80% of its viewed content results from their predictive recommended algorithms.
Natural Language Processing
We’re communicating with the digital world in unprecedented ways. AI-based conversational tools have certainly advanced, but when technology relies on artificial language like Java or C++, it’s automatically limited to literal translation. Human language is complex and brimming with subtleties, so there’s ample opportunity for misunderstanding.
In contrast, natural language processing (NLP) solutions actually learn to speak organically through practice, just like people do. These tools can even discern a wide range of emotions and recognize the differences among anger, frustration, and fear.
NLP has limitless potential in the workplace. First Horizon National Corporation, a leading financial services company, uses an NLP-powered solution to deploy open-ended employee surveys, uncovering not only what their employees are saying, but how they actually feel. Empowered by these unbiased insights, managers have taken immediate action to enhance the employee experience and improve business performance.
Deloitte recently partnered with Kira Systems to develop NLP models capable of rapidly digesting complex documents and extracting important information for further analysis. This type of solution is likely to have incredible implications for law, finance, and other contract-heavy industries.
Organizational Network Analysis (ONA) is also gaining steam, where companies use NLP to track all internal communications – including email, HCM data, and collaboration platforms like Slack – to identify top performers, locate bottlenecks, and even detect fraud. General Motors, Cigna Heath Insurance, and Cisco Systems are all experimenting with ONA in their organizations.
Blockchain is another exciting technology that’s been around for years but is just beginning to garner mainstream attention. While most people associate it with cryptocurrency, blockchain represents a decentralized, encrypted secure system of record, with applications in government, HR, and countless other industries.
Shipping companies like Maersk are already experimenting with blockchain to track cargo and discourage tampering, while farmers (and even mega-chains like Walmart) are using the technology to follow and ensure quality of livestock transactions. Even diamond-makers are following suit; Everledger has significantly eliminated counterfeiting by registering its diamonds in a blockchain.
These technologies have incredible implications, and we’re just beginning to see their capabilities. More importantly, we’re just beginning to imagine real-world applications for AI innovation in 2019. I challenge leaders everywhere to consider the potential of these burgeoning technologies and the positive impact they could have on their organizations and the future of work.