Learn artificial intelligence basics, business use cases, and more in this beginner’s guide to using AI in the enterprise.
Artificial intelligence (AI) is the next big thing in business computing. Its uses come in many forms, from simple tools that respond to customer chat, to complex machine learning systems that predict the trajectory of an entire organization. Popularity does not necessarily lead to familiarity, and despite its constant appearance as a state-of-the-art feature, AI is often misunderstood.
In order to help business leaders understand what AI is capable of, how it can be used, and where to begin an AI journey, it’s essential to first dispel the myths surrounding this huge leap in computing technology. Learn more in this AI cheat sheet. This article is also available as a download, Cheat sheet: Artificial intelligence (free PDF).
When AI comes to mind, it’s easy to get pulled into a world of science-fiction robots like Data from Star Trek: The Next Generation, Skynet from the Terminator series, and Marvin the paranoid android from The Hitchhiker’s Guide to the Galaxy.
The reality of AI is nothing like fiction, though. Instead of fully autonomous thinking machines that mimic human intelligence, we live in an age where computers can be taught to perform limited tasks that involve making judgments similar to those made by people, but are far from being able to reason like human beings.
Modern AI can perform image recognition, understand the natural language and writing patterns of humans, make connections between different types of data, identify abnormalities in patterns, strategize, predict, and more.
All artificial intelligence comes down to one core concept: Pattern recognition. At the core of all applications and varieties of AI is the simple ability to identify patterns and make inferences based on those patterns.
SEE: Artificial intelligence: A business leader’s guide (free PDF) (TechRepublic)
AI isn’t truly intelligent in the way we define intelligence: It can’t think and lacks reasoning skills, it doesn’t show preferences or have opinions, and it’s not able to do anything outside of the very narrow scope of its training.
That doesn’t mean AI isn’t useful for businesses and consumers trying to solve real-world problems, it just means that we’re nowhere close to machines that can actually make independent decisions or arrive at conclusions without being given the proper data first. Artificial intelligence is still a marvel of technology, but it’s still far from replicating human intelligence or truly intelligent behavior.
AI’s power lies in its ability to become incredibly skilled at doing the things humans train it to. Microsoft and Alibaba independently built AI machines capable of better reading comprehension than humans, Microsoft has AI that is better at speech recognition than its human builders, and some researchers are predicting that AI will outperform humans in most everything in less than 50 years.
That doesn’t mean those AI creations are truly intelligent–only that they’re capable of performing human-like tasks with greater efficiency than us error-prone organic beings. If you were to try, say, to give a speech recognition AI an image-recognition task, it would fail completely. All AI systems are built for very specific tasks, and they don’t have the capability to do anything else.
Since the COVID-19 pandemic began in early 2020, artificial intelligence and machine learning has seen a surge of activity as businesses rush to fill holes left by employees forced to work remotely, or those who’ve lost jobs due to the financial strain of the pandemic.
The quick adoption of AI during the pandemic highlights another important thing that AI can do: Replace human workers. According to Gartner, 79% of businesses are currently exploring or piloting AI projects, meaning those projects are in the early post-COVID-19 stages of development. What the pandemic has done for AI is cause a shift in priorities and applications: Instead of focusing on financial analysis and consumer insight, post-pandemic AI projects are focusing on customer experience and cost optimization, Algorithmia found.
Like other AI applications, customer experience and cost optimization are based on pattern recognition. In the case of the former, AI bots can perform many basic customer service tasks, freeing employees up to only address cases that need human intervention. AI like this has been particularly widespread during the pandemic, when workers forced out of call centers put stress on the customer service end of business.
Modern AI systems are capable of amazing things, and it’s not hard to imagine what kind of business tasks and problem solving exercises they could be suited to. Think of any routine task, even incredibly complicated ones, and there’s a possibility an AI can do it more accurately and quickly than a human–just don’t expect it to do science fiction-level reasoning.
In the business world, there are plenty of AI applications, but perhaps none is gaining traction as much as business analytics and its end goal: Prescriptive analytics.
Business analytics is a complicated set of processes that aim to model the present state of a business, predict where it will go if kept on its current trajectory, and model potential futures with a given set of changes. Prior to the AI age, analytics work was slow, cumbersome, and limited in scope.
SEE: Special report: Managing AI and ML in the enterprise (ZDNet) | Download the free PDF version (TechRepublic)
When modeling the past of a business, it’s necessary to account for nearly endless variables, sort through tons of data, and include all of it in an analysis that builds a complete picture of the up-to-the-present state of an organization. Think about the business you’re in and all the things that need to be considered, and then imagine a human trying to calculate all of it–cumbersome, to say the least.
Predicting the future with an established model of the past can be easy enough, but prescriptive analysis, which aims to find the best possible outcome by tweaking an organization’s current course, can be downright impossible without AI help.
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There are many artificial intelligence software platforms and AI machines designed to do all that heavy lifting, and the results are transforming businesses: What was once out of reach for smaller organizations is now feasible, and businesses of all sizes can make the most of each resource by using artificial intelligence to design the perfect future.
Analytics may be the rising star of business AI, but it’s hardly the only application of artificial intelligence in the commercial and industrial worlds. Other AI use cases for businesses include the following.
If a problem involves data, there’s a good possibility that AI can help. This list is hardly complete, and new innovations in AI and machine learning are being made all the time.
What AI platforms are available?
When adopting an AI strategy, it’s important to know what sorts of software are available for business-focused AI. There are a wide variety of platforms available from the usual cloud-hosting suspects like Google, AWS, Microsoft, and IBM, and choosing the right one can mean the difference between success and failure.
AWS Machine Learning offers a wide variety of tools that run in the AWS cloud. AI services, pre-built frameworks, analytics tools, and more are all available, with many designed to take the legwork out of getting started. AWS offers pre-built algorithms, one-click machine learning training, and training tools for developers getting started in, or expanding their knowledge of AI development.
Google Cloud offers similar AI solutions to AWS, as well as having several pre-built total AI solutions that organizations can (ideally) plug into their organizations with minimal effort. Google’s AI offerings include the TensorFlow open source machine learning library.
Microsoft’s AI platform comes with pre-generated services, ready-to-deploy cloud infrastructure, and a variety of additional AI tools that can be plugged in to existing models. Its AI Lab also offers a wide range of AI apps that developers can tinker with and learn from what others have done. Microsoft also offers an AI school with educational tracks specifically for business applications.
Watson is IBM’s version of cloud-hosted machine learning and business AI, but it goes a bit further with more AI options. IBM offers on-site servers custom built for AI tasks for businesses that don’t want to rely on cloud hosting, and it also has IBM AI OpenScale, an AI platform that can be integrated into other cloud hosting services, which could help to avoid vendor lock-in.
Before choosing an AI platform, it’s important to determine what sorts of skills you have available within your organization, and what skills you’ll want to focus on when hiring new AI team members. The platforms can require specialization in different sorts of development and data science skills, so be sure to plan accordingly.
With business AI taking so many forms, it can be tough to determine what skills an organization needs to implement it.
As previously reported by TechRepublic, finding employees with the right set of AI skills is the problem most commonly cited by organizations looking to get started with artificial intelligence.
Skills needed for an AI project differ based on business needs and the platform being used, though most of the biggest platforms (like those listed above) support most, if not all, of the most commonly used programming languages and skills needed for AI.
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TechRepublic covered in March 2018 the 10 most in-demand AI skills, which is an excellent summary of the types of training an organization should look at when building or expanding a business AI team:
Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.
Getting started with business AI isn’t as easy as simply spending money on an AI platform provider and spinning up some pre-built models and algorithms. There’s a lot that goes into successfully adding AI to an organization.
At the heart of it all is good project planning. Adding artificial intelligence to a business, no matter how it will be used, is just like any business transformation initiative. Here is an outline of just one way to approach getting started with business AI.
Determine your AI objective. Figure out how AI can be used in your organization and to what end. By focusing on a narrower implementation with a specific goal, you can better allocate resources.
Identify what needs to happen to get there. Once you know where you want to be, you can figure out where you are and how to make the journey. This could include starting to sort existing data, gathering new data, hiring talent, and other pre-project steps.
Build a team. With an end goal in sight and a plan to get there, it’s time to assemble the best team to make it happen. This can include current employees, but don’t be afraid to go outside the organization to find the most qualified people. Also, be sure to allow existing staff to train so they have the opportunity to contribute to the project.
Choose an AI platform. Some AI platforms may be better suited to particular projects, but by and large they all offer similar products in order to compete with each other. Let your team give recommendations on which AI platform to choose–they’re the experts who will be in the trenches.
Begin implementation. With a goal, team, and platform, you’re ready to start working in earnest. This won’t be quick: AI machines need to be trained, testing on subsets of data has to be performed, and lots of tweaks will need to be made before a business AI is ready to hit the real world.