Artificial intelligence (AI) in business remains a hot technology topic, especially since the pandemic forced many to find ways to gain greater operational efficiency and reduce costs. Enterprise AI has surfaced in these conversations, but many do not understand what exactly it means.
In this series on Enterprise AI, we will cover the topic, diving into AI for business and what it means for enterprises in general. We’ll start with the basics, moving into more advanced concepts to show how AI can apply across different fields and disciplines.
In this introduction, we’ll talk about:
- What is Enterprise AI?
- How does Enterprise AI differ from consumer AI?
- Why an Enterprise AI platform?
- What companies should use AI?
- What will we cover in the rest of the blog series?
What is Enterprise AI?
Enterprise AI is a category of artificial intelligence in business that deploys machine learning and cognitive capabilities. It’s deployed throughout an organization’s ecosystem of systems to uncover actionable data insights to inform better decision making and more generally optimize cost and efficiency.
How does Enterprise AI differ from consumer AI?
Consumer AI focuses on the interactions with prospects and customers. Organizations use the technology to enhance customer experiences, automate certain aspects of those touchpoints, and as a result, ultimately scale the business. Insights obtained through AI inform how to better connect and personalize offerings to improve customer satisfaction.
AI customer service technologies are an example of Consumer AI. Most of us have probably interacted with some form of customer service AI without knowing. In this context, the technology helps reduce reliance on agents, decreases costs, and overall improves the customer experience and can be a component of an Enterprise AI strategy.
On the other hand, AI in the Enterprise focuses more internally, at least initially, at the organization level. Enterprise AI looks at deploying cognitive capabilities across all systems to extract tangible value through automation and data insights previously unobtainable. Value comes in the form of reducing human touchpoints in processes to streamline operations and informing decision-makers with data to act on, thereby increasing efficiency and profitability.
Why an Enterprise AI platform?
An Enterprise AI platform sits beneath everything as that foundational piece of technology to implement such a strategy. Without out, developers must build, scale, and operationalize AI-enabled applications in siloes. As a result, projects do not have consistent infrastructure with teams spending an inordinate amount of time using different tools rather than a common solution.
The following chart shows the difference between working across multiple tools rather than an Enterprise AI platform:
What companies should use AI?
While many business leaders understand the importance of AI and the essence of what the technology can do, many have yet to fully adopt it. Part of the disconnect stems from not fully understanding how they can use AI in their own business operations today. And beyond that, what value such capability will yield to the bottom line, day-to-day operations, and overall decision making.
AI will become a necessity to remain competitive and operate in a highly complex, digital world with colliding offline and online realities. However, to adopt AI, one needs to overcome their digital transformation hurdle first. Then, most organizations quickly realize the multitude of problems that they face once transformed.
“People are still confused about AI and machine learning for the enterprise,” said Ryan Steelberg, President of Veritone, on eWeek eSpeaks podcast. “I think for the supermajority of companies who are not trying to radically change their business but want to greatly improve it, AI still starts with having a great understanding of your data assets. If corporations haven’t made the digital transformation to become a data-driven business, don’t even worry about AI and machine learning. AI is built upon data and leveraging it. It’s an incredible tool, but you still need to have a clear strategy in what you are trying to achieve.”
The number of businesses using AI has increased as more organizations have overcome that first digital transformation hurdle. In fact, over half of survey respondents in the latest McKinsey State of AI report, said that AI was adopted in at least one function, a 50% increase from the previous year. And adoption continues to grow exponentially fast in emerging markets including China, Middle East, and North Africa. All things we’ll cover in our next blog in this series.
What will we cover in the rest of the blog series?
As a five-part series, we’ll cover in greater detail the following topics underneath the subject of Enterprise AI:
A Look at Enterprise AI Adoption
As we’ve already teased, AI adoption continues to grow. We’ll explore how the pandemic played a role in accelerating adoption and what it looks like in the coming years.
4 Use Cases for Enterprise AI
We’ll cover the most common use cases and explore how organizations can become AI enabled to uncover new applications of the technology that fit their business needs.
Conversational AI in the Enterprise: How Does it Work?
One of the more widespread use cases for AI, we’ll explore conversational AI and how chatbots have evolved to be more intelligent and improve the customer experience.
Enterprise AI Platforms: A Brief Guide
To implement a sustainable and scalable Enterprise AI strategy, you need to have a robust Enterprise AI platform to act as the foundation. We’ll explore the capabilities you should look for in a platform.