Machine LearningWhat is Artificial Intelligence (AI) for the Enterprise?

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As artificial intelligence technology continues to advance, more companies are willing to explore the benefits of AI in business.

So, what is enterprise AI, and what are the business applications? Likewise, why do organizations fail when implementing AI, and what are the right steps to ensure success?

 Keep reading to find out!

What is Enterprise AI?

Enterprise AI, or enterprise artificial intelligence, is the technology that allows businesses to leverage machines to obtain valuable insights and organizational knowledge – which provides them with a competitive advantage. This process is very similar to how humans analyze and understand data, hence the name!

Artificial intelligence relies on technology that mirrors the human brain to solve problems through logic, make decisions, respond to evolving scenarios, and communicate with business decision-makers.

It is a computer science branch that uses the following key technologies: automated machine learning, deep learning, and natural language processing.

 

Machine Learning

Enterprise machine learning has become an increasingly popular tool to help organizations make data-driven decisions. Machine learning allows companies to analyze large amounts of customer and organizational data to better understand underlying trends and patterns.

For example, machine learning can use an algorithm to decide if an email is spam or recommend products to customers based on their buying history.

Designing an enterprise machine learning model requires significant development efforts and can be time-consuming and resource-draining. These difficulties had made the technology cost-prohibitive, but automated machine learning has solved this problem!

Automated machine learning can make these decisions and predictions automatically. The model learns by studying a test dataset where the results are already known. From this, the model can begin to recognize patterns that can later be applied to new data.

Once the model has been trained, it can be used to solve real-world challenges. It eliminates the complexity of traditional machine learning development and produces easy to understand and interpret insights.

 

Deep Learning

How advanced is AI? Deep learning demonstrates just how impressive enterprise artificial intelligence technology has become.

Deep learning models can interpret data the way the human brain does – the complex algorithms resemble neural networks and can recognize layers of patterns across large amounts of data.

Deep learning tools allow weather forecasters to make predictions and driverless cars to understand traffic signals and maps. Simply put, this technology enables machines to think and make decisions the way a human would!

 

Natural Language Processing

AI capabilities allow for natural language processing – a trait that is unique to artificial intelligence. The ability to process natural language is essential for human-machine collaboration within an organization.

Think about it this way, if the output of the machine learning model is just 1s and 0s, it will not help business leaders make decisions. With natural language processing, automated machine learning models can deliver predictions in plain English, allowing organizations to quickly and effectively make accurate decisions!

This type of AI allows tools like virtual assistants to have a conversation with your customers and help them with their questions. These same tools can be used to detect credit card fraud and predict when a customer is likely to churn.

 

What are Enterprise AI Applications?

There are many benefits of AI in business. Artificial intelligence platforms help firms automate processes and make data-driven decisions from insights that would otherwise be impossible to uncover.

The applications are not limited to just one industry – in fact, enterprise AI applications are found in healthcare, manufacturing, retail, transportation, and logistics.

 

Healthcare

Artificial intelligence has many implications in the healthcare industry. Imagine the benefit to a doctor if an automated machine learning model could make predictions that aid with diagnosing and treating patients.

AI can also help prevent medical errors – an average of 37% of these errors are due to incorrect dosage, which could be easily corrected using artificial intelligence.

Other applications include robotic-assisted surgery and early detection of epidemics and disease outbreaks. It can even aid clinical trials and medical research since artificial intelligence can process large amounts of data relatively quickly.

 

Manufacturing

The benefits of AI in business also extend to manufacturing. A study performed by Accenture found that AI capabilities will boost profit for manufacturing firms by almost 40% by 2035.

Manufacturing companies can utilize artificial intelligence to predict when machines will need maintenance. This knowledge will allow issues to be corrected proactively, further reducing delays and downtime. Likewise, they can predict the appropriate calibration for each machine.

AI-driven product design and industrial robots will allow manufacturers to improve how their products are built.

 

Retail

All of us have likely interacted with AI in retail somehow – whenever you get purchase recommendations from Amazon or movie recommendations from Netflix, you are interacting with artificial intelligence!

Retail has captured the benefits of AI in business to better engage with customers and personalize their experiences. It can also be used for inventory management, demand forecasting, and fraud detection.

 

Transportation & Logistics

Artificial intelligence presents exciting transportation and logistics firms exciting opportunities, but it is still in the early development stages. McKinsey’s research found that the companies that adopted AI early on boosted their profit margins by more than 5% compared to their competitors.

Applications include demand forecasting, optimizing routes, automatic labeling and sorting, and predictive fleet maintenance.

 

Why do Enterprises Need to Invest in AI?

Now that you understand the benefits of enterprise AI, it is time to discuss the importance of investing in the right tools.

AI is already transforming various industries across the globe, and those that choose not to invest in AI will likely get left behind. Simply put, organizations must have the tools in place to deal with rapid changes while maintaining practical user experiences.

Customers today will only engage with your brand if content and messaging are personalized to their needs. Artificial intelligence allows firms to tailor content to their unique audience and learn from their suggested products’ responses.

According to Forbes’s research, 90% of the information and data that we have access to was created in just the last two years. This demonstrates the sheer volume of data that businesses have to manage, and this number is growing exponentially. Investing in AI will allow your firm to use this data to gain meaningful insights that can drive better decision making.

Lastly, organizations need to invest in AI to address aging technology and infrastructure. Companies have always been structured to organize data by department or business function – but this has resulted in data silos that prevent strategic decisions from being made properly. AI can help eliminate these silos and ensure that all information is considered when making business decisions!

 

Why Organizations Fail with AI

Although artificial intelligence can significantly benefit an enterprise, organizations often fail to implement the technology properly.

This can be caused by failing to define a clear business application – before you invest in AI, there must be a relevant use case within your organization. It is necessary to understand how machine learning will impact your business and how it can solve your challenges. Like any business venture, a strategic approach is vital!

AI projects fail because business leaders tend to assume that it is a single technology solution. This is not the case, and artificial intelligence tools must be layered throughout your organization to facilitate automation and digitalization.

Along the same lines, AI is not guaranteed to fix every problem plaguing your business – it is still an emerging technology that requires humans to validate the processes to work and scale effectively.

One of the main reasons organizations fail with AI is that the available information is not yet ready for the tools. Sure, deep learning models can think like humans, but they can’t replace them!

The data has to be presented in the right way so that the machines can read it. Often businesses that fail to clean up their data before implementing AI end up with low-quality results – as the saying goes, garbage in, garbage out!

 

Getting Started with Enterprise AI

So, how do you get started with enterprise AI?

First, you must define the role that artificial intelligence will play in your organization. There should be clear goals and expectations laid out so that you can set your business up for success. Start small and be realistic – you can’t just flip a switch and expect all of your business processes to be automated overnight!

Next, you must dive into your current systems and processes to determine their current capabilities. What does your existing data look like, and which processes will be affected by the introduction of enterprise AI? Remember that for AI to succeed, the data must be in machine-readable formats.

As you discover which areas of your organization will benefit the most from artificial intelligence, start developing a strategy that will tie all of these goals together. Detail out how you will measure your progress and be sure that your AI strategy is incorporated into the organization’s long-term metrics.

It is essential to start with the areas that need urgent attention – from there, work towards creating a plan to incorporate artificial intelligence that is sustainable and builds in governance. For instance, be sure to create standard operating procedures to maintain data integrity and support scalability!

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