When we look at emerging and developing technologies in 2018, there are few examples that are receiving as much attention as the fields of Artificial Intelligence and Machine Learning. This is especially true for businesses and companies that are looking to extend their competitive edge by providing customers with cutting edge products and services.
There are many reasons for companies to want to leverage these AI based technologies, as they have the potential to reshape and redirect how companies perform common tasks that every day business functions rely on, thereby strengthening and streamlining a company’s ability to compete in the market. The potential for innovation is huge and has applications in marketing, customer service and finance, but it doesn’t end there.
Innovation and development has traditionally followed set phases where groups of people and decision makers plan, analyse, design, build and test systems and strategies before deploying them. AI works quite differently, and could possibly reshape the structure of how businesses plan and build their systems, as well as the way in which these systems interact with developers and data.
AI relies heavily on data sources, from which it gathers relevant information based on the learning that the system is undertaking. As such, the AI systems will rely on different data sets depending on the task that it is being asked to learn about, meaning that not all data is created equal when it comes to AI training. It also means that business leaders need to adopt new strategies and think differently about how their businesses will interact with the markets and customers that they serve.
While this all sounds great in theory, in reality there are still many obstacles that need to be navigated before we start to see the full power of AI systems being unleashed on the markets.
Some of the most common problems that were alluded to are not insurmountable, but will require dedicated focus from all major stake holders if AI is to be successfully implemented. Some common issues are:
Differing Development Approaches: AI has very different requirements when comparing systems development to traditional software and systems development. This can cause strain on teams that don’t understand how the differing development approach of AI systems, resulting in unnecessary delays and additional development time. The key to developing an efficient AI system is to assemble a team that understands the requirements for this specialized task.
Poor Data Quality: An AI system is only as good as the data sets that are provided to it. If you are trying to gain measurements from incomplete or incorrect data sources, then the learning algorithms will not generate the correct outcomes, and your insights will be inaccurate, or worse, unusable.
Uncertainty around Insight Generation: Any new technology has a mystery factor around it in the beginning, and this takes time for consumers to adapt to. The same is true of AI, and the way in which it is able to learn, adapt and improve. The results that AI can produce are often eerily accurate, but because of the way in which these systems arrive at their answers leads to uncertainty from business owners that need clarity in the methodologies that are used in AI systems. The way that AI systems work with large data sets is inhuman, and is very difficult for people to understand, even those that build the systems themselves.
AI can be thought of as a simulation not of intelligence itself, but rather the aspects of intelligence that can provide insights and understanding from massive collections of data. The current goal of most AI projects is to achieve something comparable to human intelligence, but as the goal posts shift, the research will eventually lead to systems that far exceed human intelligence.
AI Innovation is seen by many as an augmentation of human ability, rather than as a replacement. In creative and dynamic industries where human creativity can create solutions and ideas to business problems, AI Innovation can work in tandem to illustrate alternatives, or to strengthen these concepts. AI training can take time, but when enough data has been collected and learned from, the systems can provide amazing results. As things stand currently, AI is more of a purpose built tool for a specific function, and needs to be trained and developed for each task. We are quite a way off from realizing the vision of general AI, at least in an accessible and affordable version for businesses, but the day is coming.
There has never been a better time to look into AI for your business, and who knows, maybe your competitors have already started on AI systems of their own?There has never been a better time to look into AI for your business, and who knows, maybe your competitors have already started on AI systems of their own?