Introduction and practical applications
Using artificial intelligence (AI) in your business may not be something that is on your agenda, but the chances are you may have already begun to use AI without even knowing it. The Airbnb application for instances uses Aerosolve to deliver its dynamic pricing feature.
Amazon’s Machine Learning – part of its AWS cloud services – allows businesses to analyse massive datasets to reveal patterns and also train its algorithm. And Google’s Translate API uses machine learning to deliver much more accurate translations, as it assesses how words relate to each other.
In a report into the possible impact of machine learning, Simon Raik-Allen, MYOB’s Chief Technology Officer, said: “As machines get smarter, there will be a time when someone creates a machine that can learn. We are not there yet, but a lot of progress is being made.”
Certain areas of your business will feel the impact of AI first – whenever data needs to be analysed, AI is the perfect vehicle to achieve this. With companies collecting masses of information thanks mostly to social media, making sense of this information and finding value is perfect for an AI. Salesforce predicts that nearly 60% of business’ sales teams will increase their use of sales analytics this year.
Developed in partnership with digital commerce technology agency and software solutions provider Fluid and powered by IBM’s Watson cognitive computing technology, The North Face shopping experience harnesses Fluid’s Expert Personal Shopper (XPS) software to create a more engaging, personalised and relevant shopping experience.
“Digital retail continues to transform the way we shop, and embedding cognitive technologies is the next major step in engaging customers,” said Kent Deverell, CEO of Fluid. “By tapping into Watson, XPS aims to provide The North Face shoppers helpful, relevant and intuitive product recommendations. We believe this kind of engaging, personalised interactive experience will become the norm for online shoppers in years to come.”
Customer services is also ripe for an AI makeover, as many of the repetitive aspects of customer services could be handled by an artificial intelligence. Whether consumers will be happy to speak to a machine is another matter entirely, as automated switchboards continue to be a major pressure point for consumers when contacting businesses and organisations.
A good example of how AI can be applied to a practical business application is Amelia. Developed by IPsoft, Amelia is an artificial intelligence platform that can understand, learn and interact as a human would to solve problems. Amelia reads natural language, understands context, applies logic, infers implications, learns through experience and even senses emotions.
Unlike other technologies that purely detect and match words used in queries to retrieve information, she understands what is meant, not simply what is said. She applies context to distinguish between different uses of the same word in order to fully understand the implied meaning.
Jonathan Crane, CCO, IPsoft, commented: “At present the effects of IPsoft’s Amelia are largely being felt by larger companies which are the first to adopt and implement new systems and embrace a shift in working practice.
“AI is driving a huge change in the way we can target our marketing and advertising – even for smaller companies. This means that businesses are able to target their spend and increase ROI and allow advertising to do what it should – giving people adverts they want to see.”
Brave new world
Development of AI
To gain an understanding and insight into the current and future development of AI within the small business sector, Techradar Pro spoke with Sean Owen, director of Data Science at Cloudera.
Techradar Pro: How is AI changing how small businesses operate their enterprises?
Sean Owen: Artificial intelligence conjures images of chess-playing robots and phones that can crack jokes. This is still – mostly – science fiction, but for the SME, real benefits of machine intelligence have already arrived. They’re just mostly invisible, and more prosaic.
Small businesses have long since turned to software and services for accounting, for example. These services are more than glorified spreadsheets. An accounting service can already optimise cash movement to minimise tax and financing costs, or proactively predict that a current account is likely to hit overdraft next week.
Likewise, the SME can easily tap into third-party services specialising in analysing customer sentiment on social media, or that learn customer preferences and can serve up product recommendations, or target ads to the most receptive audience.
Each of these involves complex machine learning, and yet are available on-demand to even the smallest of enterprises. These services are as yet young – we can expect even better support of customer experience and operations from smart technology even within five years.
TRP: What are the pitfalls to be aware of when using AI in a small business?
SO: SMEs are taking advantage of AI and ML (Machine Learning) processes through their existing vendor relationships. For example, using an expense solution like Concur allows an SME to take advantage of their Fraud Detection and Audit capabilities which leverage ML algorithms, or with rich relevance they can incorporate personalised interaction capabilities into their customer’s discover and purchase experience. These “as-a-service” offerings are bringing the capabilities of AI to the SME where they can plug these capabilities into their business.
The risk is a fragmented view of the customer or channel where each individual vendor’s AI capability is not integrated into the holistic view. Virtually all functions and processes affect others. If these interaction effects are not monitored, the way the processes and functions are executed may not be aligned with the overall desired outcome.
Bringing these AIaaS (AI as a Service) in as a black box to the enterprise’s data enables an app-like enablement approach and a consistent experience.
TRP: How will small businesses use AI systems in the future?
SO: At the beginning, it makes sense to outsource these functions. In the long run, tapping machine intelligence from third-party services has its own opportunity cost. The data, learning and know-how are accruing in part to the service provider rather than the enterprise itself.
However, it’s more feasible than ever for enterprises to collect, store, transform and learn from data themselves. At some point, it will become more beneficial to build an “AI” capability in-house rather than buy it.
The same types of technology available to the Facebooks and Googles of the world is now generally available as open source software, like Hadoop and its ecosystem. The largest and most sophisticated enterprises will eventually develop their own AI capability into data products themselves, not just in-house support functions.
Brave new world
IPsoft’s Jonathan Crane told Techradar Pro: “Regardless of what field you operate in, you must be aware that AI and the digital revolution will have an impact on your world in some small, if not dramatic, way. Take some time to see what is out there and think how you could use it to your advantage to win more business.”
With Andrew Nichols, head of analytics at Tungsten Network, concluding: “Artificial intelligence isn’t just about robotic dogs. The possibilities it represents for businesses are endless, or at least further than the human eye can see. For SMEs with insight, now is the time to start using it to make our businesses more efficient, more cost effective, and more intelligent.”
There is little doubt that AI will advance to the point where it can offer real-world advantages to all kinds of businesses – large and small. “We’re working on AI because we think more intelligent services will be much more useful for you to use,” said Facebook creator Mark Zuckerberg. Many of the services your business now uses will have an AI component that will make them more efficient to deliver better products and services to your business’ customers.