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Artificial intelligence (AI) adoption is progressing rapidly — with generative AI like ChatGPT making headlines — but AI technology has had a foothold in business for some time. In 2017, McKinsey found that 20% of organisations had adopted AI in at least one business area; by 2022, the figure stood at 50%.
AI, which generally refers to the ability of machines to perform tasks using human-like intelligence, is a component of many business solutions utilised by companies today. As AI development continues, artificial intelligence will play a greater role in business systems and processes, with humans and machines increasingly working together.
Here’s what to know about artificial intelligence in business, the different types of AI we commonly see in business operations, and what the future of AI means for companies and staff.
What is artificial intelligence in business?
Artificial intelligence is a field of computer science focused on the simulation of human-like intelligence in machines. Artificial intelligence technology makes it possible for digital computers (or computer-controlled smart machines) to solve problems or perform other tasks that previously required human input. Examples of AI performing human tasks include ChatGPT turning out text, audio, or imagery in response to prompts or an autonomous vehicle navigating its surroundings.
The problems and tasks AI can assist with take many forms, as businesses are adopting AI to save time or drive up productivity in a variety of environments and use cases.
In manufacturing environments, for example, humans are working side-by-side with cobots (aka collaborative robots) capable of learning and adapting to new industrial tasks. Marketing teams are using generative AI like ChatGPT to personalise emails and write SEO-friendly content. And human resources departments are applying AI in hiring scenarios, like using AI-powered software to screen job candidates and analyse applications.
Types of artificial intelligence
The field of artificial intelligence includes a variety of subfields:
- Machine learning(ML) is an AI subfield concerned with giving computers the ability to learn and improve performance without being directly programmed. Machine learning applications consume and learn from data in the same way humans learn from experience. Email spam filtering is one technology use case that relies on machine learning to ‘know’ which emails are undesirable to a recipient.
- Generative AI is a type of machine-learning-based AI technology capable of generating new and unique outputs (such as text, images, audio, videos, or 3D models) in response to prompts. ChatGPT and other generative AI tools can even be used to help software developers write and improve code.
- Natural language processing (NLP) applications, meanwhile, rely in part on machine learning for their ability to understand, manipulate and generate human language. Natural language processing powers tools like text-to-speech apps and virtual assistants like Siri and Alexa.
- Computer vision (CV) is the subfield of AI and ML concerned with enabling machines to ‘see’ and derive understanding from images and videos. Computer vision enables computers to identify people or objects in digital images or videos and take actions in response. Self-driving cars use computer vision to maneuver the world around them.
Examples of artificial intelligence in business
The number of applications of artificial intelligence in business is continually growing across sectors. Here are some of the most common examples of artificial intelligence being used in business:
AI-generated product descriptions
It’s important for retailers to provide thorough information about their products to online shoppers. But writing up product details can be a time-consuming endeavor – especially for retailers with large digital catalogs. Generative AI-and NLP-powered tools can help retailers drastically minimise the time and effort required to create quality product descriptions by auto-generating content for human review.
Customer service
Companies are increasingly deploying AI-powered customer service chatbots on eCommerce sites and apps, as well as other web properties (such as financial services websites). And consumers are increasingly using them: across the contact centres of companies in various industries, Gartner projects that one in 10 agent interactions will be automated by 2026 – up from 1.6% in 2022.
Voice-command shopping
As consumers around the world get more and more accustomed to using voice assistants for search, experts predict that voice commerce (that is, shopping via voice commands dictated to voice assistants) will grow in popularity, rising from $4.6 billion in 2021 to $19.4 billion in 2023. An example of voice-command shopping is asking Siri or Alexa to reorder a recent purchase or searching for a specific product on Google.
Monitoring food waste
Food waste is a problem in the restaurant industry, with 4–10% of the food purchased by restaurant leaders never making it to customers. Computer vision AI is rising to the problem; solutions like Winnow can capture and recognise images of wasted food and track and analyse what’s being wasted, all so that restaurateurs can save money by making changes in what they purchase.
Inventory management
AI is enhancing inventory management in multiple ways. Solutions for inventory management, for example, are using machine learning and analytics to make more accurate predictions about inventory and automatically inform retailers and restaurateurs when they need to place orders to avoid shortages. Autonomous inventory robots, meanwhile, are helping retailers gain better understanding of what’s available on their shelves (and alleviate labor-shortage challenges) by scanning the floor for out-of-stock items while also handling tasks like checking price signs.
Personalisation for customers
Customers want relevant content, products and offers, and AI-powered search tools and recommendation engines help surface them. Machine learning powers tech solutions, such as Google’s Recommendations AI or Amazon Personalize, capable of analysing customer purchase histories, preferences, browsing behavior, search activities and other data points and uses that information to develop highly personalised recommendations.
Future of AI in business
Providing a highly personalised experience to customers will become a baseline expectation as AI continues to advance its impact on digital expectations. Companies that harness AI will likely see it improve productivity, create new efficiencies, and open up new opportunities for staff.
What does AI mean for staff?
AI accelerates and augments human work, but most output from AI-powered systems should still be reviewed for accuracy (such as the data AI is trained on can be wrong).
As with the example of manufacturing cobots, some of the most impactful applications of AI in business will involve humans and AI systems collaborating. MIT research posits that humans working with AI can be ‘superminds’ capable of ‘cognitive and physical tasks that could not be done before.’
Leveraging AI in business can also free up staff time by eliminating manual tasks. By delegating repetitive or time-consuming tasks to AI, staff can focus on more important and engaging areas of the business, like strategy development, marketing or customer-facing relations.
How can AI help create jobs and scale a business?
As AI expands its footprint in the business world, new jobs will emerge that require AI expertise. In fact, the responsibilities of working with AI are already creating new roles, such as the Prompt Engineers working with ChatGPT, the Business Intelligence Developers working on AI analytics dashboards and the AI-savvy digital marketers deploying the technology to segment email audiences and optimise ad strategies.
When it comes to scaling a business, AI can not only improve workers’ output by automating routine tasks, but it can also analyse data and deliver insights that help a business grow resources efficiently.
Manufacturers, for example, can use AI to analyse sensor-collected data, so they can better understand their machinery and forecast when it will fail. They can then apply predictive maintenance strategies to help the machinery last longer and cost their business less. Businesses of all types can use AI to help them calculate optimal prices for their products based on customer buying histories, competitor prices and other information.
Leveraging the benefits of AI in business
According to PwC, organisations that are considered leaders in the AI space are focusing on realising substantial AI value of increased productivity through automation (44%), improved decision-making (41%) and improved customer experience (40%), among other initiatives.
As AI continues to develop, businesses of all sizes can follow their lead and realse similar benefits by incorporating AI-powered solutions into their operations. Applications for ChatGPT in business, for example, include everyday tasks in marketing and content creation, customer service, research, and more. So while AI may be a complex and rapidly evolving field, businesses can already leverage AI technology to execute many tasks and realise new efficiencies.
This article is for informational purposes only and does not constitute professional advice. For specific advice applicable to your business, please contact a professional.
