Businesses are rapidly digitizing their operations. Technology is being used everywhere — from marketing and customer relations to logistics and finance — and software now regulates most business processes.
The increasing use of technology is transforming the way businesses operate; a process we often call ‘digital transformation’.
Technology is also demanding unprecedented speed from decision-makers. The pace of business is so dynamic that decisions need to be made instantly. At the same time, the volume of business data available from the use of technology in business operations has increased manifold. Optimal decision-making requires the data to be reviewed and analyzed in almost real-time to make data-driven business decisions. The challenge then is to derive actionable insights from the data rapidly.
This combination of circumstances has helped the rise of Artificial Intelligence in analytics. AI-powered business insights are now a need of the hour.
AI: The new frontier
New advances in data analytics have brought AI out of the realm of science fiction and into our increasingly digital workplaces. AI/ML and RPA have changed the way organizations now approach business processes and data analysis.
AI is taking charge of business data in large corporations, making predictive recommendations, providing automated insights, driving quick responses in business decisions and providing forecasts to boost productivity and efficiency. AI, in other words, is now functioning as a business consultant! According to Chih Yan, CEO & Co-founder Appier, as more and more businesses experience positive results from predictive analytics and automated insights, they will start relying more on recommendations from AI.
With AI at the forefront of technological development, many data and analytics technology trends have emerged over time, which are likely to prove disruptive. Rita Sallam, Vice President of Research at Gartner, advises:
“Organizations need formal mechanisms to identify technology trends and prioritize those with the biggest potential impact. Data and analytics leaders should actively monitor, experiment with, or deploy emerging technologies. Don’t just react to trends as they mature.”
Some of the top trends for AI-powered business analytics are:
1. Augmented analytics
Augmented analytics helps to enhance the decision making by automating the discovery of crucial business insights. It achieves these insights in significantly less time than is needed to derive them without automation. It also reduces reliance on highly skilled and expensive resources like specialized data analysts, data scientists, and machine learning experts. At the same time, augmented analytics will require increased data literacy across all roles in the organization, as it makes insights available to all business roles.
As Gartner’s Rita Sallam foresees:
“Augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms.”
2. Augmented data management
Another big challenge of business analytics is that despite the exponential growth of data, the supply of technical skills is very limited. Automating data management tasks using AI can help free up time for scarce human resources and allow them to engage in higher-value tasks. AI/ML is being used to make data management processes self-configuring and self-tuning.
3. Continuous intelligence
Time is another crucial aspect of today’s dynamic markets. AI helps in achieving real-time intelligence to perform a given set of tasks by using data collected from sensors in the Internet of Things (IoT), cloud, and many other sources of information simultaneously. According to a Gartner report, by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
4. NLP and conversational analytics
NLP and conversational analytics will become the easiest way for business users to ask questions about data and derive insights.
NLP could provide real-time answers, searching through volumes of information and making sense of the context, to provide the most likely answer. This will help improve service by front-office workers, provide personalized services and enhance knowledge sharing.
What does it take to become a data-driven enterprise?
While businesses are flush with data, many still struggle to understand what data is important to use and what data-derived actions should be taken based on the data.
But when they use tools that enable AI-powered business insights, they can create competitive advantages for their business.
The question is not if we should incorporate AI to derive automated business insights, but when. And the answer is now. The biggest challenge to use AI to get business insights has always been insufficient data. AI needs large volumes of data of high quality to give the best results. But now, we have a surplus of data ranging from unlabeled and unstructured data to structured and organized data.
To remain competitive in the market, companies and businesses need to innovate and move fast in time. Brands need tools that can help them understand what the customer wants both quickly and accurately. This is when sophisticated data-gathering tools powered by artificial intelligence can help. AI can improve profitability and revenue growth with fast researches and reduced human dependency.
Be it Nokia or Yahoo, no matter how big a company is, if it fails to innovate with time, it will eventually be left out of the competition and will perish.
Trend analysis and forecasting can give companies valuable business insights to make decisions, and to remain competitive in the industry.
Using different machine learning and deep learning techniques, trend analysis powered by AI can provide companies with reliable insights that enable them to make strategic choices based on real-time and accurate information.
Highlights of a data-driven decision-making process
In organizations that use AI to enable data-driven decision-making, strategic decisions are made based on high-quality data. The decision-making is not centralized and several key executives could make decisions as the AI-powered analytics engine provides automated insights to all major business roles.
Most of the operational decisions are automatically made by machine learning models that learn continuously from available data. Some decisions which cannot be automated are assigned to key executives, who make these decisions based on recommendations by the insights engine or on data reports from the system. It is only if data is specifically lacking that decisions are made based on the executives’ opinions, gut-feel or subjective judgement.
Before this level of maturity can be attained in automated decision-making, the organization needs to achieve a certain level of digital transformation, coupled with the adoption of AI-based tools. With the help of digital transformation consultants, AI/ML service providers and state-of-the-art AI-powered data analytics platforms, organizations can roadmap an organizational framework that allows the optimal mix of automated decisions and manual decisions supported by data insights.
To be successful, such a program requires the right data ecosystem, advanced tools, skilled teams, as well as an inculcated culture of data, starting at the very top rungs of leadership.
What is the future of AI?
According to Ray Kurzweil’s Law of Accelerating Returns, the rate of change in technology (and all evolutionary systems) tends to increase exponentially. Kurzweil, in his essay, The Age of Spiritual Machines, says: “Within a few decades, machine intelligence will surpass human intelligence, leading to the Singularity—technological change so rapid and profound it represents a rupture in the fabric of human history.
Whether the technological “Singularity” proposed by Kurzweil sees the light of day or not, one thing is for certain – the future of AI is bright. It is expected that the next big paradigm shift will occur with a combination of AI/ML techniques with data analytics, helped along by advances in High Performance Computing. This shift, referred to as Cognitive Analytics, is expected to be the next step forward from prescriptive analytics.
With more research and development, AI is well-poised to become a core technology with faster, more accurate, and more versatile results. Other technological developments in cloud computing, blockchain technology, and quantum computing will also help AI achieve greater heights in the near future.
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