Today, Artificial Intelligence (AI) is at the top of every technology leader’s mind. What technologies should you incorporate? Who should you partner with to get the most out of AI investments? How best to implement AI within the workplace? To answer these questions, we first need a better understanding of how we got here.
AI has traversed an extraordinary journey from speculative fiction to integral technology reshaping industries today. Its origins, rooted in early 20th-century imaginative works, still guide the development of technologies emerging. And throughout time, as history shows, AI has been a deeply human endeavor. As we navigate the intricate landscape of AI, understanding its historical trajectory provides valuable insights into its current and future impact on business and the digital workplace.
The Development of AI: Early Beginnings
The dawn of AI can be traced back to the creative imaginations of early 20th-century artists and writers, who envisioned a future where sentient machines transformed human life. This period of speculative ideation found a concrete expression with Alan Turing, a pioneering figure whose contributions in the 1930s and 40s revolutionized the conceptual framework of intelligent machines. Turing's development of the Turing Machine and the provocative Turing Test catalyzed the initial waves of AI research, setting the groundwork for symbolic manipulation – a framework that would underpin much of the AI research to come in the next half century.
AI Research Begins in Earnest
By the 1940s and 50s, there was growing popularity among scientists to design machines which could make decisions like humans. The development of the ENIAC, the first electronic general purpose digital computer, meant what had previously only been a dream had real potential. And scientists were optimistic. In 1956, at the Dartmouth Conference, John McCarthy brought together some of the great scientific minds of the time to discuss the possibility of intelligent machines. It was here that McCarthy first coined the term “Artificial Intelligence.”
McCarthy also developed the LISP programming language, one of the earliest and most influential languages in the development of AI. LISP was designed to facilitate symbolic computation and manipulation of symbolic expressions, in line with McCarthy’s focus on Symbolic AI – otherwise affectionately known in the field as “good old-fashioned AI” (GOFAI). This approach to AI focused on manipulating symbols, leveraged the LISP language, and laid the foundation for early rule-based expert systems and logic programming languages.
Scientists in these early days focused on a distinctly human problem: could AI be developed to make medical diagnoses more quickly? At the time, computers lacked the memory and speed to achieve this – but the philosophical vision was there. Scientists pursued AI to solve a very human
issue.
The First Golden Age of AI
The subsequent decades witnessed the first golden age of AI. Symbolic AI and rule-based systems took up the lion’s share of research funds and energy. And although the dream of a computer that could easily make medical diagnoses never came to pass, this era did see the introduction of the world’s first interactive chatbot: ELIZA. Continuing the human-centric theme of AI milestones, ELIZA was designed to operate like a Rogerian psychotherapist. Though rudimentary, it was powerful enough to convince some people they were
speaking with a real human doctor.
Parallel advancements in neural networks, exemplified by Frank Rosenblatt’s Perceptron, expanded the horizons of machine learning – though the broader AI community remained primarily committed to rule-based symbolic AI. Despite these achievements, practical applications faced limitations, leading to a period of reduced investment and slower progress in the 1970s and early 1980s.
AI Enters the Workplace
The 1980s saw the proliferation of the first legitimately successful form of AI software in the workplace: expert systems. As the name suggests, an expert system is a computer system that attempts to emulate the decision-making process of a human expert using either rule based IF THEN logic, or conclusions if conditions logic. This period also saw the introduction of one of the most revolutionary technologies of the 20th century: the first IBM PC with the PC DOS operating system. This technology allowed for smaller, more economical chips and dramatically downsized computers.
Individual employee devices opened the door to a new structure within corporate computing: the client-server model. As businesses no longer relied on a highly specialized IT departments understanding of the mainframe to develop business applications, application development exploded – aided in part by expert systems, working to make human use of technology
easier.
Yet a failure to live up to the promise of full automation and internal political disputes led to the eventual decline of expert systems. Despite a brief resurgence in the first decade of the 2000s after Deep Blue, IBM’s chess computer, won a game against a reigning world champion under time controls, expert systems faded from use.
Machine Learning, Large Language Models, and the AI Revolution
The decline of expert systems opened the door for a new technology to take its place: machine learning. In a surprising twist, this newest innovation in the AI space was really more like a refocusing, as machine learning was underpinned by that other 20th century invention:
neural networks.
Although they had taken a backseat to rule-based systems for the latter half of the 20th century, in the late 1990s, the field of statistical computation reorganized as machine learning and coupled with new developments in neural networks as well as growing memory and computational power of computers, exploded into the field we know today. After 50 years focused on rule-based systems, researchers found a new perspective on an old problem and the field of AI truly took off.
Today, as generative AI technologies built on Large Language Models proliferate and tools incorporate predictive analytics guide decision making, the core of AI’s purpose remains the same: solving human-centric problems. As organizations seek guidance and solutions on implementing these technologies, remaining focused on the human issues that underpin business challenges and goals can only help leaders navigate the turbulent waters of the AI revolution.
When push comes to shove, the core benefits of artificial intelligence in the workplace can be boiled down to four primary areas: solving complex problems, increasing business efficiency, making smarter decisions and automating business processes (AWS). But what does that mean specifically in the DEX industry?
In the world of IT, DEX is an innovation. Just like AI researchers when they pivoted to machine learning in the early 2000s, DEX professionals take a new perspective on digital workplace issues: a human-centric perspective. DEX asks the question: what if we evaluated technology by how humans experience it? DEX experts seek to solve human issues – and so the DEX space is uniquely and strongly positioned to incorporate AI’s capabilities to solve human problems.
Practically, this means expediting the ability to hel organizations move from reactive IT troubleshooting to proactive and even preventative IT management environment wide and employee deep.
- Solving complex problems: When hardware issues occur for employees, AI technology instantly scans all available datapoints in the device and across your environment to correlating point in time events and provide immediate root cause analysis.
- Increasing business efficiency: AI removers IT guesswork with data driven profiling to rapidly assess your environment and call out common causes of issues, like the likelihood of binary version stability on a given OS – to remove technology pain points for employees.
- Making smarter decisions: AI with access to data from 15M global endpoints lets you make decisions based on benchmarking data from other companies around the world, understanding the issues and resolutions other companies are using, so you can work faster, not harder.
- Automating business processes: training AI to create a workflow for a problem seen and resolved in the past, such as reconnecting the VPN on employee devices whenever it disconnects.
At Nexthink, AI is in our DNA. Our company was born in the AI lab at the Swiss Federal Institute of Technology, where CEO and founder Pedro Bados conducting research into different AI models, including neural networks, and developed the first real-time end-user monitoring technology, authoring two patents on behavioral modeling in the process. Since the beginning, our technology has sought to solve human problems – first how to prevent security threats due to human behavior, and now, how to improve the human experience of technology in the workplace. This technology, and philosophy, remains core to Nexthink’s products today.
Hear more about Pedro’s AI research on this recent episode of the DEX Show.
Nexthink’s AI analyzes digital workplace data from millions of devices using an anonymized data lake to uncover insights not available at the individual organizational level. AI is only as intelligent as the data is has access to. Nexthink’s AI offers insights no business could get at the individual level. Using big data technologies like Apache Kafka and Spark, along with machine learning algorithms like outlier detection, Nexthink artificial intelligence gives IT teams an edge against digital workplace issues, spotting them fast and diagnosing immediately. And now with addition of new generative AI capabilities (through large language models such as OpenAI’s GPT-4o and Anthropic’s Claude 3) Nexthink enriches data and recommends fixes in real time.
For 20 years, Nexthink has been leading the way in real-time end-user monitoring. Today, we are the market leader and category creator of digital employee experience (DEX) and offer the most robust set of AI capabilities available in a DEX solution.
As businesses look to establish their AI strategy, they need a partner with the depth of expertise and insight that only comes from 20 years of experience. As a company born out of an AI research institute, we are uniquely positioned to guide digital workplace teams through the AI revolution and into the future of the digital workplace.
