Technology is dynamic and disruptive and it seems like there is a new technology every new month that promises to radically transform your business in unprecedented ways. This has led to some critics claiming that terms like Big Data, Machine Learning, and Artificial Intelligence are just clever buzzwords whose only purpose is to market a company’s product or image. But there is nothing further from the truth.
Digital transformation is poised to be a key driver of business strategy in the coming years. For instance, the ability of sophisticated algorithms to learn from data without human supervision will lead to the development of technologies that are even beyond our current understanding. Every organization must, therefore, start realigning themselves in order to be relevant in this age of digital transformation.
Machine Learning vs. Artificial Intelligence
Machine Learning (ML) and Artificial Intelligence (AI) are closely related but they are not the same thing. AI has to do with using machines to perform tasks in an intelligent manner. These machines are however not programmed to perform single and repetitive tasks but they intelligently adapt to different situations. AI can be categorized into two broad categories; general AI and narrow AI. In general AI, machines will exhibit all human intelligence characteristics but in general AI, the machines will only exhibit some of the characteristics of human intelligence.
ML can be looked at as a branch of AI but it is based on the notion of building machines that can process data and learn on their own without human supervision. ML is, therefore, a way of achieving AI without having to write too many lines of code. Deep learning is an important facet of ML. It is based on Artificial Neural Networks which act much the same way the human brain does. For instance, a neural network system can go through a series of pictures and classify them based on the different elements on the pictures.
As absurd as it may sound, more people today have access to mobile phones than toilets. This is just one of those illustrations that clearly underscore the impact of digital transformation. Companies all over the world have no choice but to stay abreast with digitalization or they will miss out. Digital transformation totally disrupts the long-established business norms not only in the digital space but also in the physical world.
For instance, Uber is now the leading taxi Company and yet they do not own a single cab. Similarly, Airbnb is also the largest provider for accommodation and yet they do not own any hotels. In fact, Airbnb is larger than five of the world’s biggest hotel brands combined. In the traditional way of doing business, Uber would have had to invest in taxis and Airbnb would have had to invest in real estate.
But digital transformation makes it possible to create totally new business models that are efficient, cost-effective, and transformative. In light of these examples, current business processes need to be reevaluated as they might easily become obsolete in the digital economy.
Data Driven vs. Data Informed
In this era of big data, managerial activities can no longer be arbitrary. Decision makers must use data to make informed decisions. But exactly how can this be done? Well, your company can either be data informed or data driven. In a data driven company, data is what leads the decision-making process. In a data informed company, data is considered when making decisions but it is not necessarily treated as the silver bullet.
The data-driven approach
|All decisions are backed with evidence.||There is no interrogation of data because data is trusted completely.|
|Information is kept up to date because that’s what drives the decision process.||GIGO – bad quality of data can result in bad decisions.|
|Evidence-based decision making can help to |
increase longevity and productivity.
|Handling complex data will require skilled expertise and continuous input.|
The data-informed approach
|Gives room for the limitations that are in data.||Considering multiple voices in decision making slows down the process|
|Multiple sources are consulted for decision making rather than relying entirely on data.||May lead to laxity in monitoring and tracking of data especially in immature organizations|
|Facilitates an “out-of-the-box” thinking which |
A data informed approach would be ideal for startups because they do not have enough data on which they can base their decisions. But as the organization grows and evolves over the years, a data driven approach can be implemented. Still, there are some huge companies that are still using the data informed approach because they lack the systems to help them filter out noise from big data.
Operational Intelligence (Operational AI)
Businesses have traditionally relied on business intelligence for driving strategy and identifying opportunity. Several big data technologies have amplified the power of business intelligence by making it possible to analyze petabyte data sets in a matter of minutes. But as the digital transformation continues to make the digital era even more fastpacked, the traditional business intelligence is moving too slowly. Companies have no choice but to turn to operational intelligence.
Operational intelligence is the ability of a system to analyze data in real-time and subsequently give instant feedback. This takes business intelligence to a whole new level especially through the creation of new opportunities. An operation intelligence AI will use in-memory computing power to track data streams in real time, enrich them with relevant historical data and then do a parallel analysis. The result of this complex process is actionable intelligence that helps to identify opportunities.
The main difference between Operational AI and business intelligence is that Operational AI aims to discover actionable insights in real-time. Operational AI captures business opportunities that are perishable but since this is done in real-time, you can take advantage of them before time runs out. Business intelligence offers strategic guidance based on a data warehouse but operational intelligence adds value by acting on the frontlines.
Steve Jobs often said that Apple’s success can be attributed to its marriage of liberal arts and technology. In simple terms, he was saying that Apple’s innovation emerges from the intersection of technology and art. Most experts would agree that we are still very far from achieving autonomous systems that can process and reason at the same level as the human brain.
The need for replicating the intelligence of humans in AI is what led to the emergence of intelligence augmentation (IA). IA is the process of using technology to support and supplement human intelligence while maintaining humans at the core of the decision making process.
The underlying technologies that power Artificial Intelligence (AI) and those that power Intelligence Augmentation (IA) are more or less the same but the goals of AI and IA are anything but similar. AI seeks to create systems that can autonomously while IA seeks to create systems that can enhance human efficiency. By this definition, most of the available business technologies that are thought of and branded as AI are actually IA.
A practical example is the use of Machine Language for fraud detection and control. Once the machines flag the fraudulent patterns, employee interrogates the machine data and is their expertise and knowledge to make the final call. Studies show that such an AI system can save banks over $12 billion per annum.
Smart Analytics and Augmented Analytics
According to Gartner, Augmented Analytics is a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users.” Experts believe that Augmented Analytics will radically transform business just as much as the Internet has. In 2017, the global augmented analytics market size was estimated to be $4,094 million but analysts project the market size will grow to $29,856 million by 2025.
Companies that embrace Augmented Analytics increase efficiency because the users will spend less time on data exploration. Utilization of machine-generated insights helps to improve the speed and quality of decisions made. Several augmented analytics features work hand in hand to help the decision maker on their smart analytics journey. This journey starts with natural language query and then slowly advances to predictive analytics.
What does all this signal?
Even though the journey towards developing autonomous AI applications will take us down a winding road, it is indisputable that the current innovations in AI have already transformed the business environment for the better. Take, for example, the BioMind AI System that is already outperforming human specialists in detecting brain tumors.
Embracing AI across different sectors will improve efficiency in most mundane activities.
As a business owner, it is in your best interest to take tangible steps towards adopting relevant AI technologies that will make your business better. This calls for new tactics and fresh approaches.
In other words, you should develop services that actually work instead of merely paying lip service to AI. This is the only way to see technology leap beyond the buzzwords to the actual goals it promises to deliver.