Top 4 Reasons Why Now is the Time for Enterprise AI

Corporations must embrace enterprise AI services in order to remain competitive

Artificial intelligence, machine learning, deep neural networks, algorithms, the terminology is intimidating. They are foreign to executives who are more familiar with the traditional manufacturing-based business models that have driven corporate success since the start of the 20th Century. Yet, AI use has gone mainstream and become a primary and necessary building block for 21st Century corporations.

Today, AI is one of the fastest-growing areas, not only in science but also in business applications and venture capital investments. In 2018 AI investment hit an all-time high with over $9.3 Billion raised by AI services and product companies. As a result, organizations that do not recognize the shift will soon find themselves as market laggards at best and out of business at worst. In sum, the time to incorporate enterprise AI solutions is now.

1. Advancements in AI Technology Move out of the Lab

AI is not a new concept. In fact, its roots date back more than half a century, so, why is it gaining traction now? A handful of recent advances coalesced, moving the technology from the test lab to the production line. Significant progress has been evident in five areas: Drivers of Enterprise AI

  • Dramatic increases in computing power, so systems process large volumes of data
  • Movement to cloud simplified software development and computer infrastructure management
  • Data science advances, such as the development of more sophisticated algorithms and movement away from specialized, proprietary expensive software to standard software interfaces and programming tools
  • The continued extension of connectivity to smaller devices starting with mobile and now with the rise of the Internet of Things (IoT)
  • The ability to collect more data and analyze information via Big Data platforms

2. The Global Economy Evolves  

In addition, markets have become highly competitive. The emergence of the Internet broke down traditional barriers, so corporations today can reach potential customers anywhere in the world. The economic development and industrialization of countries, like China, India, and South Korea, extended competition around the globe. The rise of DevOps programming sped up application development and empowered corporations to respond to market and customer needs faster. The growth of social media altered how organizations connect with and support customers. Facing such intense competition, businesses desperately search for differentiation, and AI offers them an opportunity to create it.  Conversely, the decision to postpone or forego the development of an AI strategy may be very costly in the long run as illustrated by this simulation from McKinsey.

AI Services laggards simulation

3. AI Services Gain Traction

Corporations are deploying AI services in new ways. The emergence of IoT enables enterprises to collect information from small devices, like sensors, so they gain visibility into areas, like equipment performance. Machine learning, which Gartner predicts will move into mass adoption in the next two to five years, empowers corporations to correlate information and glean new insights into their operations. For instance, computer vision applications enable government agencies to deploy drones to inspect bridges and identify areas in need for repair, and doctors to use image pattern recognition to highlight certain features, identify early predictors of diseases, streamline diagnoses, prioritize treatments, lower costs, and improve results. Consequently, businesses are investing significant $$$$ in AI technology and services.  IDC forecasts that spending on AI solutions is expected to reach $49.2 billion in 2020, a 31% increase from 2019.

4. A Framework for Applying AI in the Enterprise Emerges

Yet while spending is increasing, many companies struggle to leverage the technology effectively. An inability to collect, clean, and correlate data; a dearth of qualified employees; and legal and ethical challenges slow deployments and limits application effectiveness in many organizations.

Data is the fuel that powers AI algorithms, but enterprises have tens, hundreds, or thousands of different applications generating and collecting oodles of information. These systems were built for one purpose, they identify information in unique ways, and they were siloed, used primarily in one department. Consolidating this information enables enterprises to cross-reference information and glean new insights into their operations; however, the process of collecting it is extremely complex, time-consuming, expensive, and error-prone.

As a result, the AI explosion has left enterprises in a quandary. They want to take advantage of the technology but lack the in-house personnel and expertise needed to take that step. The college and professional training and certification programs in place to develop such talent have been woefully behind the adoption curve, so there are many open enterprise AI jobs. In fact, the number of data scientist vacancies has been increasing by 110 percent year-on-year, and data engineers see an 86 percent job growth, according to Harnessing the Power of AI: The Demand for Future Skills, a study by recruitment firm Robert Walters

Finally, questions revolve around how organizations can use the information that they collect. Does the data belong to the organization, the person, or both?  Government is setting guidelines about what are legitimate and illegitimate uses. They are also laying down the law about what steps organizations need to take to collect, protect, and use information. But the process is in an early stage; guidelines are unclear; and the thinking is constantly changing.

Where does that leave enterprise AI?

The end result is many organizations, even the world’s most sophisticated conglomerates, invest in AI projects but do not realize the potential benefits. In many cases, they start down this path but discover that they lack the technical skills, experienced personnel, and institutional expertise needed to succeed.

Consequently, corporations need assistance from experienced third parties. Dynam.AI is an end-to-end AI services company specializing in computer vision and machine learning. The company provides a broad suite of services that complement what organizations already have in place as well as what they lack to deploy AI and machine learning successfully. Supplemental technology resources from Dynam.AI can augment and guide internal teams from the start of an AI readiness evaluation until its ultimate deployment. We fill voids and enable corporations to maximize their AI investments, develop differentiating products and services; and position themselves for success in the 21st Century.

About the Author

Dr. Michael Zeller

Dr. Michael Zeller has over 15 years of experience leading artificial intelligence and machine learning organizations through business expansion and technical success. Before joining Dynam.AI, Dr. Zeller led innovation in artificial intelligence for global software leader Software AG, where his vision was to help organizations deepen and accelerate insights from big data through the power of machine learning. Previously, he was CEO and co-founder of Zementis, a leading provider of software solutions for predictive analytics acquired by Software AG. Dr. Zeller is a member of the Executive Committee of ACM SIGKDD, the premier international organization for data science and also serves on the Board of Directors of Tech San Diego.  He is an advisory board member at Analytics Ventures, Dynam.AI’s founding venture studio.