At the start of 2020, an impressive 37% of organisations were already utilising AI systems in some form. That’s a 270% increase in the last four years alone, and it’s hardly surprising considering available worldwide big data is set to grow by as much as 61% by 2025. In other words, as the need for unique data handling and simplified processes comes to the fore, companies who don’t get on top with AI soon stand to drown in the influx.
Countless industries are finding ways to put intelligent AI to good use simplifying a range of redundant processes that are fast-growing out of reach. While marketing and sales, particularly, prioritise AI and machine learning in programmatic ad buys, countless departmental focuses have been shifting in this direction over recent months and years. By 2021, in fact, experts predict that 80% of tech-based processes, in business and outside of it, will rely on some form of AI-powered technology. So, changing the face of operations with this in mind sooner rather than later is a current business must.
The question is, how exactly is artificial intelligence changing business right now, and what does that mean for these industries as we move into a new, tech-focused decade? We’ve got a few use cases through which to explore the subject.
# 1 - AI’s business intelligence revolution
Significant decisions in business have never been easy to come by. The amount of data generated by modern digital business processes (customer-facing or not) has promised to improve decision-making, but also complicated it. Although the right answer might be hidden in the data, that doesn’t mean it’s always easy to find.
The great strength of AI comes down to computational analysis. For a robust AI system, big data isn’t an administrative challenge — it’s the exact information needed to come to an optimised decision. IBM has been at the helm of this decision-making revolution since the release of their Deep Blue software way back in 1997. These chess-playing calculators were able to determine up to 100 and 200 million positions per second, and went on to beat even chess champions of the time. This relatively simple and explainable AI application proved, even when artificial intelligence seemed the reserve of sci-fi, the decision possibilities inherent in such systems.
Business Intelligence software describes a whole host of solutions deployed to centralise and simplify the analysis of business data. The best of these platforms are deeply integrated with AI processes to improve the analysis and insights they can deliver.
For example, SAP’s HANA deploys AI to comb through vast quantities of data within seconds — transforming even the largest database into near real-time insights. However, it’s not just goliaths like SAP deploying AI in this context. DOMO, founded in 2010, is a great example of a fast-growing BI developer that uses AI to enhance the types of insights that can be offered. Going beyond simply centralising data, their “Mr Roboto” feature can highlight real-time trends and help direct review to the most relevant information.
Ultimately, AI sits at the crossroads of transforming the huge quantities of data within the modern business world into truly valuable insights that can improve decision-making and outcomes.
# 2 - AI in the retail sector
AI-powered technology is also making significant ripples in the retail sector, with two in five retailers already seeing significant improvements from such implementations. And, with experts predicting revenue growth of up to 10% as a result, more retail companies are sure to follow suit.
AI can help retailers better understand customers, bypass spam filters, and generally improve the marketing process. What’s more, AI helps customers receive a more streamlined and frictionless buying experience — a hallmark of successful modern sales processes.
For example, digital native ASOS has paved the way using AI to recommend clothing sizes to shoppers based on past purchases. All of the way back in 2012, beauty company Sephora launched its ‘Color IQ’ product in stores — allowing shoppers to ‘scan’ their skin and receive customised foundation and concealer shade recommendations.
Customer analytics are the reverse side of the equation — allowing brands to better understand their customers and stay ahead of customer expectations. Pulling data from across entire purchasing ecosystems, a good AI can crunch the numbers to determine things like customer lifetime value, buying trajectories and geo-located shopping habits. All of this can be paired with aforementioned programmatic tools, along with driving optimised decision-making across a general retail strategy — driving sales and improving customer experiences.
It seems that in an age where we’re more at risk of losing touch with our consumers than ever before, AI in the right places can work to enhance, rather than detract, from the much-coveted personalised retail experience.
# 3 - Chatbots and customer service
AI-led chatbots are changing the face of customer service as we know it. Although this could be considered a subset of how AI is deployed in retail, the implications are far further reaching — playing a role in B2B and B2C industries across the economy. In fact, it’s estimated that chatbots be responsible for 85% of all customer interactions this year. That’s a massive benefit in an age where businesses simply don’t have time for long-winded processes like customer troubleshooting. And, it benefits customers with faster feedback on the back of almost immediate machine learning algorithms.
Few companies are proving the changes possible here more than HubSpot, who launched the aptly named ‘HubBot’ last year. Far from just incorporating this chatbot on their main site, HubSpot has gone above and beyond to utilise this AI benefit, including a Facebook messenger bot for consumers coming from social platforms. They’re also taking into account the needs of the 55% of consumers who still value human communication by using their bot as an efficient placeholder, rather than automating customer service altogether.
Marketo is another company providing similar chatbot benefits, only their MarketBot stands out above the crowd for naturalistic language that can add a personal touch the moment consumers access their site. These efforts alone are leading to reduced service time, fewer mistakes, and generally improved customer service across industries.
# 4 - The very pressing need for virtual assistants
Virtual, or voice, assistants are behind some of the most fundamental changes in how individuals engage with computers. Most obviously, this impacts how people search for and buy products online. However, the impact of this technology doesn’t end with a consumer focus. Cisco’s Spark assistant, for example, brings virtual assistants into the heart of business processes themselves, with an emphasis on recording meetings, summarising plans, and generally improving transcription efficiency.
The technology is not perfect, and anyone (particularly those with regional accents) who has spoken with Alexa or Google Assistant can relate to the frustration of having your words misunderstood. However, companies like X.ai (which has, alone, received over $44 million in total investment) have extensive and dedicated teams solely focused on perfected the meeting scheduling capabilities of such programmes. The ability to automate just that one feature may transform certain administrative jobs and tasks across business — generating untold efficiencies.
For the public, however, it’s Alexa, Siri and Google Assistant that have made waves. Various smart home devices are already paving the way for faster interactions, more personalised shopping experiences, and generally closing the consumer-company gap. And, with Google announcing that around 20% of all searches are now voice-based, it’s fair to say that more companies could benefit from integrations like these.
Whether used to target customer needs, tailor internal processes, or both, this is AI at its best, and we can expect to see it used on a much broader scale this coming year.
# 5 - Capturing creative machine learning/AI for video strategies
In an age where YouTube is the second most popular social media platform, and consumers have an average browsing attention span of around just 8 seconds, the implementation of video strategies in business processes has become non-negotiable. And, as many companies are discovering, using machine learning and AI technologies can bring new ease to this otherwise time-consuming and costly method of content processing and management.
For example, and most notably in recent news, Microsoft AI-led software has transformed the video and image capturing techniques used by the Snow Leopard Trust. In the past, researcher Koustubh Sharm had little choice but to tediously sort through countless hours of footage before real revelations were possible. Yet, with the help of Microsoft’s MMLSpark, machine learning and convolutional neural networks (CNNS) allow for automatic identification in a matter of minutes, on a much more reliable scale.
And, this isn’t the only AI-enabled video software of its kind. A quick look at the market proves that countless video asset management platforms are utilising similar processes with a focus on object detection, real-time cloud-based editing, and archive management that would otherwise cost companies hours. As well as drastically reducing price points, such implementations bring video marketing strategies into much clearer focus and allow easier access to such methods for companies across industry lines.
# 6 - Improving healthcare access and outcomes
AI decision-making power isn’t just limited to strategic and tactical business decisions. Increasingly, AI is being deployed in the medical community to accelerate drug development, improve diagnostic capabilities, and expand healthcare access.
An early pioneer in AI-healthcare was, again, IBM, and the successor to Deep Blue — Watson. First developed for the US television show ‘Jeopardy’, Watson has been deployed in a wide range of decision-making tasks, and has been used to help suggest treatment options since 2011. Watson’s cognitive computing capabilities have also been used for drug discovery, helping healthcare providers determine and invest in the best new drugs on the market. Watson chatbot-based questionnaires have even helped to identify patient preferences, such as opinions on potential surgery relocations.
UK-start up Babylon is another great example of AI in the healthcare space. They have taken the diagnostic ideas of Watson and made them public-facing. Founded in 2013, the company has partnered with the UK’s NHS to provide on-demand healthcare information through an online portal called ‘Ask Babylon’. Since then, they have expanded internationally to Rwanda, Canada and China.
While AI healthcare is still a controversial subject, particularly when replacing direct contact with GPs, it shows a roadmap for a future in which health care (or at least diagnostic information) is far more widely available.
Are you using AI yet?
If you aren’t already one of the 83% of businesses prioritising AI strategies right now, you should be. Changes have already led to extreme shifts, but they’re just the tip of the iceberg on what we can expect over the coming years. Even if AI implementation appears above your head, seeking the services of a company who can help with implementation is your only possible chance of staying ahead, and evolving your processes to meet growing customer needs.
If you're not using AI, chances are your competitors already are ahead of you by doing so. Find out how AI can be deployed so that your business gains competitive advantage.