Artificial intelligence (AI) is perhaps the most electrifying and controversial of the so-called “disruptive” technologies. As AI becomes more sophisticated and the technology evolves, it will increasingly help to perform more complex tasks whether for personal or commercial use.
Today’s AI machines can replicate certain elements of intellectual ability and they are constantly striving to achieve more. This includes applications of autonomous vehicles, domestic and industrial robots, surveillance and security, automation, personal assistants, forecasting, data analysis and more.
The global Ecosystm AI study reveals top drivers of AI adoption. Organisations are trying to incorporate AI in their existing processes for better competitor analysis and insights, cost-effectiveness, deeper customer engagement to provide personalised service/product offerings, and for process redesign or automation.
AI supporting technologies
AI is driving important technologies and processes and driving better, faster and more accurate decisions which help processes run more effectively and efficiently. With AI insights, business strategies will be more information-driven, efficient and consistent.
There are certainly many benefits that organisations are deriving from AI. Ecosystm AI study reveals that organisations implementing AI are using it to drive various business solutions including billing management, supply chain optimisation, predictive maintenance, enhancing operations and more.
Below is a list of some hot technologies driven by AI.
Advanced Analytics
The proliferation of Big Data has led to the creation of massive data sets that can only be effectively analysed with AI tools and statistical models. AI can spot complex patterns in the data which is difficult for humans to understand. AI’s usefulness as an analytics tool is highly gaining importance in the use of predictive analytics and decision automation. Once sufficient data is available for use by AI, which is further filtered and processed thoroughly, the system can suggest actions or outcomes based on various parameters such as trends, patterns, historical information, frequency and more.
For example, the financial services industry is using advanced analytics to evaluate how customers earn, invest, spend and make financial decisions, which is useful for the organisations to customise their customers’ preference and offerings accordingly.
Natural Language Processing (NLP) and speech recognition
NLP involves the learning of languages by machine through the means of interaction between computers and unstructured speech/text. NLP requires massive processing power and complex algorithms to reinforce learning mechanisms. To help in NLP, AI generates models, which are further improved to create NLP and speech simulations. Nowadays, Natural language is being implemented and used in various conversational interfaces, such as those with bots, artificial learning agents that can generalise to new environments, and autonomous vehicles.
Cognitive processing
Otherwise known as semantic computing, refers to a digital processing that attempts to mimic the operation of the human brain. In general, semantics means the meaning and interpretation of words and sentence structure and how words relate to other words. So, how is semantic related and what is the semantic analysis used for in AI? Semantic technology processes the logical structure of sentences to identify the most relevant elements in the text to understand the topic. It is especially suited to the analysis of large unstructured datasets with high efficiency.
Vantagepoint has an artificial intelligence tool to improve their trading results. It has a patented tool which can forecast stocks, futures, commodities, Forex and ETFs and claims an accuracy of up to 86%. The tool can predict changes in market trend direction up to three days in advance thus enabling traders to get in and out of trades at optimal times with confidence.
Robotic Process Automation (RPA)
RPA has grown out of Business Process Automation (BPA) and refers to the use of AI to automate workflow and business processes. The advantages of RPA demonstrate it to be a solid tool in attaining higher quality output at lower costs which is much quicker than traditional methods. RPA can be used in IT support processes, back-office work, and workflow processes. The rules are programmed, and bots extract structured inputs from applications like Excel and enter them into other software such as CRM, SCM or accounting. A good example is the use of NLP to scan incoming emails and undertake the appropriate action, such as generating an invoice or flagging a complaint in an automated manner.
Machine Learning
Machine learning is an application of artificial intelligence (AI) which involves a combination of raw computing power and logic-based models to simulate the human learning process. Machine Learning is proving to be a successful approach to AI. When humans learn, they alter the way they relate information and the world, similarly when machines learn, they alter the data and form it into a piece of information.
An example, Image recognition is a popular application of machine learning in which images are fed into an algorithm, which attempts to recognise the contents of the image based on patterns. For instance, Yelp’s machine learning algorithms help the company’s human staff deal with tens of millions of photos to compile, categorise, and label the images more efficiently.
Chatbots and virtual assistants
Chatbots are robotic processes which simulate human conversation and automate functions. The technology is also used for so-called ‘virtual assistants’, which uses AI to interact with humans and aid with specific queries. They are increasingly being used to handle simple conversation and tasks in B2B and B2C environments. The addition of chatbots reduces human assistants and they can work throughout the clock. Chatbots and Virtual assistants improve with AI and can be trained to review conversations, past transactions and to draft a response based on context. If the user interacts with the bot through voice, then the chatbot requires a speech recognition engine.
Chatbots have been used in instant messaging (IM) applications and online interactive platforms. To exemplify, chatbots are deployed to assist online shoppers by answering noncomplex product questions, pricing, FAQ’s, order processing steps or forwarding information to human agents on complicated questions such as shipping delays or faults.
With AI technology evolving and improving so rapidly, many organisations are looking to use AI in their business, but there are still many questions to which adopters are seeking answers such as how to integrate AI into their existing systems, how to get access to data that will enable AI as well as the persistent technology concerns around cybersecurity and cost. The goal of many AI providers is to reach a stage where AI will support humans, control machines for us and automate repetitive tasks and processes.
I ran several roundtables over the past few weeks speaking to business and technology leaders about their AI investments – and one factor came up many times – that it is hard to build a business case for AI because 70% accuracy was not good enough…
What this means is that companies have thousands of things to automate. Most of those automations in the short-medium term will deliver 100% accuracy using RPA and other simple automation tools. Every time you run that process you know the outcome.
Along Comes AI and Machine Learning
These dumb processes can now learn – they can be smart. But originally they won’t deliver 100% accuracy. They might only deliver 60-70% to start with – climbing perhaps to 90%. The benefits of these smart, learning processes can amaze – costs can fall, processes can improve, outcomes can accelerate. But traditionally we have built technology business cases delivering 100% accuracy and outcomes.
So we need a new way to think about AI and a different language to use about the way it works. The people who sign off on the business cases might not understand AI – they will come to the business case with the same lens they use for all technology investments (and evidently – all business investments). We also need to be better at selling the benefits to our leaders. CEOs and Managing Directors in the roundtables are surprised to hear that AI won’t deliver 100% accuracy – they said unless they know more about the capability, savings and outcomes that the solution might drive, they are unlikely to fund it.
Make Your Dumb Processes Smart
I take this as good news. It means we have moved beyond the hype of AI – the need to “do AI in our business” that drove many of the poorer chatbots and machine learning projects. It means that businesses review AI investments in the same way as any business investment. But it also means we can’t over-promise or under-deliver on AI. Woodside did this with their initial foray into AI, and they are still playing catch up today.
While there are many opportunities to use “dumb automation” and save money, reduce or redeploy headcount – or have employees focus on higher value activities or make real differences to customer experiences – there are as many opportunities to make dumb processes smart. Being able to automatically read PDF or paper-based invoices – processes usually done by humans – could be a huge saving for your business. OK – maybe you can’t redeploy 100% of the staff, but 70% is still a big saving. Being able to take human error out of processes will often help to save money at two steps on the process – automating the human input function up front and also getting rid of the need to fix the mistake.
Start Your AI Journey With The Low Hanging Fruit
Ecosystm’s Global Ongoing AI study has shown that most businesses are focusing their AI investments on internal initiatives – on reducing process time, cost savings and driving productivity – which makes the most sense today. They are the easier business cases to build and the easiest benefits to explain.
Perhaps AI is also a chance for businesses to acknowledge that “efficient” does not always mean “good”. Many of the processes we automated or coded to ensure 100% compliance don’t give customers or employees what they are looking for. And maybe making the customer happy 70% of the time is better than not making them happy at all…
If you’d like to dig deeper into Ecosystm’s reports exploring the data from our ongoing AI study – check them out here (you’ll need to register if you have not already – it is free to register, but some content is premium):
4 Vendors Emerge as Leaders: Understanding the AI Vendor landscape
Use Cases Drive AI Software Adoption: Understanding The Industry Landscape