Companies across industries are loaded with information, so it’s challenging to gain insights from the deluge of knowledge into actionable intelligence. That’s where the applications of artificial intelligence in business comes into the picture. This intelligence drives major and minor industries by providing them with the leverage to experiment with new concepts that cater to what the market wants. Gartner, an MNC famous for its “Magic Quadrants” in numerous sectors ranks firms on different metrics and provides a thorough analysis of the present scenario across tech and non-tech industries. Gartner predicts that 40% of all new enterprise applications will be supported by computing technologies. In such a situation, the data science market is certain to witness tremendous growth within the years to come.
There are several areas where Artificial Intelligence in business is being applied: personalised apps, security applications or more popular applications in data analytics within recent times. The opportunities are endless given that businesses and institutions are capturing an increasing amount of data across their processes, due to the proliferation of data across the globe.
What are the different types of analytics?
There are four major applications of data analytics, which can be classified as follows:
1) Prediction: Predictive analytics, as the name suggests, uses machine learning algorithms to sift through the data obtained to predict potential outcomes. Predicting the price of a stock in the stock market based on the growth, profits, news etc, are interesting examples.
2) Prescription: Given a set of conditions, the algorithms give the best set of decisions that can be taken. The efficacy of these algorithms depends on the quality of the data obtained. Popular apps that offer medical advice on the basis of the patient’s inputs is a relevant example. The outcome of these apps is a list of possible illnesses based on the symptoms, with recommendations and precautions to avoid the illness becoming worse.
3) Diagnosis: Identification of the problem at hand is crucial for many companies. In the instance of profit margins spiralling down for a company, they’ll need a thorough analysis of their operations, sales and marketing data to understand the factors that are influencing the decline so that the company can take steps to correct it.
4) Description: Describing the current market scenario, for instance, is an example of the same. Sentiment analysis gives information about the tonality and image of a company in the market. Finding the factors that lead to the right positioning and marketing is done through descriptive algorithms. For example, ThoughtSpot, a company working in the space of data analytics is working on providing descriptive solutions to marketing bottlenecks that emerging companies face. Identifying the key areas of improvement and providing actionable solutions for the same describe descriptive analytics.
Check out the main differences between data science, machine learning and artificial intelligence.
How will AI affect business outcomes?
The impact of Artificial Intelligence in business has immense potential, but there’s still a long way to go. Traditional business analytics requires high IT involvement as businesses generate a large number of data points. It is tough for a common man to navigate through a large number of data points. With AI-driven analytics, businesses can automate data preparation and reveal hidden patterns through sensible data discovery and interactive exploration. These technologies are enabling businesses to foresee outcomes, thus letting them act at the right instant when the opportunity strikes. Gartner predicts that language generation and AI capabilities will be a standard feature in 90% of contemporary analytics platforms by 2020 in which a minimum of 50% of the analytics queries will be generated or automated using the existing technologies within the same time-frame.
Instances of AI applications in business
There are various emerging companies that offer data analytics and AI services for a nominal amount. They enhance the company’s business prospects by identifying key areas of action. The user interface and the natural language capabilities of these systems improve the ease with which managers in companies can leverage large amounts of data.
As stated before, the scope of Artificial Intelligence in business has the potential to be vastly impactful. Results obtained are simplified and additional personalised insights and necessary set of actions and recommendations are captured. Users utilize the services by searching through keywords in the form of queries. For example, a query that reads, “which product has the least profits?” has keywords product, least profits in it. Using these keywords, the algorithms search automatically through the databases and output the results in a visually appealing way. Moreover, autonomous capabilities can now deliver results that the algorithm thinks is going to be useful, based on the user’s interaction with the software. Thus, businesses are leveraging AI tools and techniques, significantly machine-learning algorithms to generate actionable insights.
How far has machine learning come?
In development for several years, machine-learning algorithms will “learn” over time through the increasing volume of data input, developing the flexibility to detect hidden patterns and generate predictions. Machine learning is associated with the trial and error-driven sub-field of AI with roots in applied mathematical modelling. When we try to intellectually code a real-time system, the complexity with which the world works is way beyond the level that we as humans can conceptualize and convert into a logical set of statements. Therefore, it permits systems to find out and improve following prolonged exposure to information while not being programmed explicitly. Models are “trained” from meticulously crafted ready to use datasets that learn from inputs with expected outputs.
For instance, traditional code development excels at capturing rules we can categorize explicitly— say, calculating the cost of the premium for insurance. Consider predicting the ideal and most profitable cost of the premium from customers through which the customers attract the most number of consumers and derive the highest profit. The solution to such large-scale problems is beyond the knowledge obtained solely from human experience. When many parameters are present the complexity of the problem increases multifold, and therefore machine-driven analytics is the smart way to make judgments. By inferring significant relations directly from the data, machine-learning techniques usually attain human or above-human performances on new examples not seen throughout the training period, effectively working out a solution for hand-coded rules. Machine learning permits firms to use code to deal with issues that traditional coding techniques couldn’t.
“Machine learning isn’t some obscure magic that happens with data scientists in a backroom, it’s pervasive in how we use and consume data,” says Rich Clayton, VP of product strategy for Oracle Analytics. “Instead of looking at just enough data to support a hypothesis, machine learning does inductive discovery— it looks at what the data is telling me, even things I didn’t think about”. The increase in the use of machine learning across verticals is becoming increasingly pervasive and an integral part of the digital transformation. India is a major example of an efficient and smooth implementation of digital technologies in every aspect of bureaucracy.
Increasing adoption of AI and ML by businesses
Over the past few years, there has been an increase in the adoption of Artificial Intelligence in business. Deloitte expects giant and medium-sized businesses to “intensify their use of machine learning” in 2020. The number of experiments and pilot programs using machine learning algorithms can double, and this accelerated adoption is going to be a tipping point for the entire global economy. For example, Flipkart has come up with its range of products ranging from washing machines to air conditioners. The objective of this venture is to create products which have the best features from products across multiple companies product lines. Hence, providing it in one single product enhances the customer experience. How did they get the data in the first place? It is through customer feedback and reviews published on their website.
In retail, firms are investing in machine learning and autonomous analytics to derive user insights that equip retailers to deliver targeted and contextually relevant offers and client experiences. Many financial companies are investing in machine learning in a variety of host of applications from algorithmic stock trading and customer service chatbots, to real-time fraud prevention. Health-care and biotech firms are driving innovation by coming up with new drugs, new therapies and new ways to automate complex surgeries through the involvement of robotics.
Digital transformation needs the employment of both adaptive and autonomous analytics. This implies using machine learning to power the business analytics chain, which starts with discovery, moves to the organization and augmenting of information, then to analysis, modelling, and eventually prediction. ML capabilities have traditionally been out of reach, due to price, availability of talent, and out-of-date infrastructures. To drive large scale digital transformation, enterprises need an optimized technology stack— from hardware and storage to efficient and intelligent algorithms and applications that leverage machine learning techniques—all designed to run analytics and AI applications seamlessly in any setting. Trendy analytics platforms infused with machine learning and natural language processing eliminate the requirement for a lot of of the up-front technical work, automating information preparation and facilitating sensible information discovery via interactive visual exploration. With cloud-based intelligence, autonomous analytics and the consequent advancement of ML is comparatively inexpensive and also allows high-capacity in terms of processing power, storage, networking, and new memory technologies. Businesses are already experiencing a huge leap within the rate of change in the quality offered by these data-driven insights.
What technologies are propping up the rise of AI and ML?
The role of IoT and 5G is going to be huge, as the process of data collection is going to grow both in terms of speed and size. 5G is being prepared to be ML ready and thus high bandwidth interfaces allow the handling of large ML model execution and thus lead to real-time analytics accessible from the simplest of an internet-connected edge device.
Companies with more than 100 years’ worth of data which is well documented in the form of papers, can utilize the services that autonomous analytics systems provide. It is ideal for non-technical industries to invest in such services as there is no up-front cost, and neither do they have to employ anyone to do the same. The existing employees can use these interfaces and come up with insights. This is what the data revolution looks like. An oil company decided to adopt a cloud-based, autonomous analytics system. The company aggregated its diverse data sources and obtained insights in weeks, doing so otherwise would have taken years— if employees had even thought of asking various questions.
Another example is of Tesla Motors. The self-driving cars project wing under the company has inserted several cameras in each car, and thus have a huge dataset built. What is the relevance of the size of a dataset? The size matters, especially because, the probability of a larger dataset capturing all the corner cases will be higher when compared to a smaller one. The corner cases, in the scenario of a self-driving car, can be a crucial one, especially because of the consequences that such applications shall have. The idea of safety, feasibility, and scalability are the key bottlenecks all these platforms need to consider and master.
Investment optimization is a key aspect these platforms concentrate on. The objective is to maximize profits and thus, all companies need to be future-ready, that is, focus on their scaling capabilities in terms of the advances in the field.
How can companies leverage AI?
There’s no one-size-fits-all approach for applying artificial intelligence in business, but here is a normative approach:
- Formulate a holistic and long-term data strategy, not just a tactical road map for disparate projects. Doing so requires an assessment of business objectives along with a clear definition of success criteria and the data maturity of the enterprise. Evaluate such variables as existing legacy applications, workload placement, agility and time to market, security, compliance, capacity, legal/regulatory requirements, workforce skills, and maturity of offerings from cloud solutions providers.
- Identify use cases with a high return on investment (RoI) that are tied to business goals and objectives, which will help demonstrate initial proof points and benefits. An important part of this exercise is to identify the foundational data that will drive such use cases.
- Capture key lessons learned and make them available for future endeavours.
- Allocate enough resources for change management, which is perhaps the most difficult aspect of supporting a data-driven culture. The prescriptive actions might be counterintuitive and thus making data governance and lineage a top priority are critical to take decisive actions.
- Depending on platforms that provide analytical services can only get us so far. The remaining work that needs to be done might need specialized requirements and thus investing in data science research and having a Research and Development team would help the growth of the company.
Challenges in AI adoption for companies
Data storehouses and a disjointed approach to analytics have created sub-optimal conditions, thus preventing organizations from fully harnessing the power of intelligent insights. Says Oracle’s VP Rich Clayton, “If we continue with the same analytical processes and technology with big data, we won’t be able to reap the full potential.” The path to digital transformation success requires a massive update in underlying infrastructure tightly coupled with a well-defined strategy for data, analytics, and AI. The three are intrinsically linked. To enable new applications that can run both at the edge (such as devices and sensors) and in the cloud, or to run workloads seamlessly across the edge, in the cloud, and on-premises, the need to democratize insights will be greater than ever.
Therefore, democratizing access to actionable information has become the new business imperative for organizations that aspire to win in the digital economy. Those enterprises with a vision for intelligent, autonomous analytics will experience a significant increase in the variety of problems that can be tackled, moving beyond decision making that drives efficiency and productivity to an innovation agenda tuned to competitive advantage.