Artificial intelligence in business
Artificial intelligence (AI) is not new. It has been around for decades. However, due to greater processing speeds and access to vast amounts of rich data, AI is beginning to take root in our everyday lives.
From natural language generation and voice or image recognition to predictive analytics, machine learning and driverless cars, AI systems have applications in many areas. These technologies are critical to bringing about innovation, providing new business opportunities and reshaping the way companies operate.
This guide introduces some of the main concepts around AI. It explains what artificial intelligence is, how it is different from machine learning and how businesses across diverse sectors are using it for competitive advantage.
It also outlines the business benefits of artificial intelligence and gives examples of common AI systems in use today. Finally, it highlights some of the risks and limitations of artificial intelligence in business.
What is artificial intelligence (AI)?
Artificial intelligence (AI) is a branch of computer science. AI technologies aim to reproduce or surpass abilities in computational systems that are generally deemed intelligent if performed by a human. These abilities include:
- visual perception
Different types of artificial intelligence
There are two main types of artificial intelligence:
- Applied AI - is more common and includes systems designed to intelligently carry out a single task, eg move a driverless vehicle, or trade stocks and shares. This category is also known as 'weak' or 'narrow' AI.
- Generalised AI - is less common and includes systems or devices that can theoretically handle any task, as they carry enough intelligence to find solutions to unfamiliar problems. Generalised AI is also known as 'strong' AI. Examples of true strong AI don't currently exist, as these technologies are still in very early stages of development.
Many modern AI applications are enabled through a sub-field of AI known as 'machine learning'.
What is machine learning?
The roots of machine learning (ML) are in statistics. ML uses algorithms and statistical models to perform a specific task without using explicit instructions, instead relying on patterns and inference. For example, ML applications can:
- read a text and decide if the author is making a complaint or a purchase order
- listen to a piece of music and find other tunes to match the mood
- recognise images and classify them according to the elements they contain
- translate large volumes of text in real time
- accurately recognise faces, speech and objects
How are AI and machine learning used in business?
Over the years, AI research has enabled many technological advances, including:
- virtual agents and chatbots
- suggestive web searches
- targeted advertising
- pattern recognition
- predictive analytics
- voice and speech recognition
- face recognition
- machine translation
- autonomous driving
- automatic scheduling
Many of these are now commonplace and provide solutions to a great number of business challenges and complex, real-world problems. For more AI use cases, see also how businesses are using artificial intelligence.
How are businesses using artificial intelligence?
Artificial intelligence (AI) is steadily passing into everyday business use. From workflow management to trend predictions, AI has many different uses in business. It also provides new business opportunities.
Application of artificial intelligence in business
You can use AI technologies to:
- Improve customer services - eg use virtual assistant programs to provide real-time support to users (for example, with billing and other tasks).
- Automate workloads - eg collect and analyse data from smart sensors, or use machine learning (ML) algorithms to categorise work, automatically route service requests, etc.
- Optimise logistics - eg use AI-powered image recognition tools to monitor and optimise your infrastructure, plan transport routes, etc.
- Increase manufacturing output and efficiency - eg automate production line by integrating industrial robots into your workflow and teaching them to perform labour-intensive or mundane tasks.
- Prevent outages - eg use anomaly detection techniques to identify patterns that are likely to disrupt your business, such as an IT outage. Specific AI software may also help you to detect and deter security intrusions.
- Predict performance - eg use AI applications to determine when you might reach performance goals, such as response time to help desk calls.
- Predict behaviour - eg use ML algorithms to analyse patterns of online behaviour to, for example, serve tailored product offers, detect credit card fraud or target appropriate adverts.
- Manage and analyse your data - eg AI can help you interpret and mine your data more efficiently than ever before and provide meaningful insight into your assets, your brand, staff or customers.
- Improve your marketing and advertising - for example, effectively track user behaviour and automate many routine marketing tasks.
See further examples of artificial intelligence use in business, as well as examples of digital innovation in business.
Business benefits of artificial intelligence
Many businesses take up artificial intelligence (AI) technology to try to reduce operational costs, increase efficiency, grow revenue and improve customer experience.
For greatest benefits, businesses should look at putting the full range of smart technologies - including machine learning, natural language processing and more - into their processes and products. However, even businesses that are new to AI can reap major rewards.
Artificial intelligence impact on business
By deploying the right AI technology, your business may gain an ability to:
- save time and money by automating and optimising routine processes and tasks
- increase productivity and operational efficiencies
- make faster business decisions based on outputs from cognitive technologies
- avoid mistakes and 'human error', provided that AI systems are set up properly
- use insight to predict customer preferences and offer them better, personalised experience
- mine vast amount of data to generate quality leads and grow your customer base
- increase revenue by identifying and maximising sales opportunities
- grow expertise by enabling analysis and offering intelligent advice and support
According to a recent Infosys study, the main driving force for using AI in business was competitor advantage. After that, the incentive came from:
- an executive-led decision
- a particular business, operational or technical problem
- an internal experiment
- customer demand
- an unexpected solution to a problem
- an offshoot of another project
Benefits of AI and humans working together
Research suggests that AI doesn't always perform best on its own. AI technologies are great at driving or even replacing the lower-level, repetitive tasks, but businesses often achieve the greatest performance improvements when humans and machines work together.
To make the most of this powerful technology, you should consider AI as a means of augmenting rather than replacing human capabilities.
AI opportunities for business
Whatever your reason for considering AI, the potential is there for it to change the way your business operates. All it takes to start is an open-minded attitude and a willingness to embrace new opportunities wherever and whenever possible.
Keep in mind, however, that AI is an emerging technology. As such, it is changing at a fast pace and may present some unexpected challenges. Read more about the risks and limitations of artificial intelligence in business.
Risks and limitations of artificial intelligence in business
Businesses are increasingly looking for ways to put artificial intelligence (AI) technologies to work to improve their productivity, profitability and business results.
However, while there are many business benefits of artificial intelligence, there are also certain barriers and disadvantages to keep in mind.
Limitations of artificial intelligence
One of the main barriers to implementing AI is the availability of data. Data is often siloed or inconsistent and of poor quality, all of which presents challenges for businesses looking to create value from AI at scale. To overcome this, you should have a clear strategy from the outset for sourcing the data that your AI will require.
Another key roadblock to AI adoption is the skills shortage and the availability of technical staff with the experience and training necessary to effectively deploy and operate AI solutions. Research suggests experienced data scientists are in short supply as are other specialised data professionals skilled in machine learning, training good models, etc.
Cost is another key consideration with procuring AI technologies. Businesses that lack in-house skills or are unfamiliar with AI often have to outsource, which is where challenges of cost and maintenance come in. Due to their complex nature, smart technologies can be expensive and you can incur further costs for repair and ongoing maintenance. The computational cost for training data models etc can also be an additional expense.
Software programs need regular upgrading to adapt to the changing business environment and, in case of breakdown, present a risk of losing code or important data. Restoring this is often time-consuming and costly. However, this risk is no greater with AI than with other software development. Provided that the system is designed well and that those procuring AI understand their requirements and options, these risks can be mitigated.
See also Industry 4.0 challenges and risks.
Other AI limitations relate to:
- implementation times, which may be lengthy depending on what you are trying to implement
- integration challenges and lack of understanding of the state-of-the-art systems
- usability and interoperability with other systems and platforms
If you're deciding whether to take on AI-driven technology, you should also consider:
- customer privacy
- potential lack of transparency
- technological complexity
If you're considering writing a tender document to procure AI, you can seek help from the Northern Ireland Artificial Intelligence Collaborative Network.
AI and ethical concerns
With the rapid development of AI, a number of ethical issues have cropped up. These include:
- the potential of automation technology to give rise to job losses
- the need to redeploy or retrain employees to keep them in jobs
- fair distribution of wealth created by machines
- the effect of machine interaction on human behaviour and attention
- the need to address algorithmic bias originating from human bias in the data
- the security of AI systems (eg autonomous weapons) that can potentially cause damage
- the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently
While you can't ignore these risks, it is worth keeping in mind that advances in AI can - for the most part - create better business and better lives for everyone. If implemented responsibly, artificial intelligence has immense and beneficial potential.
Examples of artificial intelligence use in business
Artificial intelligence (AI) is all around us. You have likely used it on your daily commute, searching the web or checking your latest social media feed.
Whether you're aware of it or not, AI has a massive effect on your life, as well as your business. Here are some examples of AI that you may already be using daily.
Artificial intelligence in business management
Applications of AI in business management include:
- spam filters
- smart email categorisation
- voice to text features
- smart personal assistants, such as Siri, Cortana and Google Now
- automated responders and online customer support
- process automation
- sales and business forecasting
- security surveillance
- smart devices that adjust according to behaviour
- automated insights, especially for data-driven industries (eg financial services or e-commerce)
Artificial intelligence in e-commerce
AI in e-commerce can be evident in:
- smart searches and relevance features
- personalisation as a service
- product recommendations and purchase predictions
- fraud detection and prevention for online transactions
- dynamic price optimisation
Artificial intelligence in marketing
Examples of AI in marketing include:
- recommendations and content curation
- personalisation of news feeds
- pattern and image recognition
- language recognition - to digest unstructured data from customers and sales prospects
- ad targeting and optimised, real-time bidding
- customer segmentation
- social semantics and sentiment analysis
- automated web design
- predictive customer service
These are only some of the examples of AI uses in business. With the pace of development increasing, there will likely be much more to come in the near future. And with the emergence of new and diverse Industry 4.0 technologies, there are many other examples of digital innovation in business.
Find out more about the business benefits of artificial intelligence.