As enterprises enter an era ruled by data, AI and Machine Learning are on everyone’s mind. With a number of articles being written on AI in the business, and a number of use cases being talked about, the temptation to adopt AI is natural. According to Statista, the worldwide AI market is expected to touch $118.6 billion by 2025! But with BI already having reached maturity in the enterprise, how do you best use AI and Machine Learning for your business? How will AI help in enhancing your BI outcomes? Do you have sufficient budget for the deployment? Is your workforce ready for adoption? Do you know what it takes to implement it all? Answers to all this and more are critical before you embark on the AI journey.
Where you are on the AI readiness spectrum?
The disruption that AI is causing across industries is phenomenal. While some organizations are aiming at driving revenue growth, others want to better serve customers, remain competitive, and strengthen their position in the market. No matter what the reason for AI adoption, it is important to assess where you stand on the AI readiness spectrum.
Considerations and recommendations for moving forward with AI
Like with any enterprise system, AI implementation needs to be done with utmost care and planning. You need to have the right mindset, tools, processes, and strategy in place to ensure success with your efforts. Here are some considerations and recommendations for moving forward with AI:
- Invest in your people
One of the first things to do before taking the AI plunge is to ensure business preparedness. So, invest in your people, and drive efforts to make them understand the benefits AI will bring to their daily tasks. Address apprehensions and questions in time, and make sure there is minimum resistance from the organization.
- Start small
Considering the repercussions of a failed implementation, it makes total sense to start small, and see how people embrace the new technology. You can witness immediate incremental value with small pilot efforts, learn from your mistakes, and then drive increased value through a wider implementation.
- Align AI with business strategy
For your AI initiative to really bear fruit, you need to assess every aspect of your business, and make sure it aligns with your AI goals. From people to data, processes to budget – make sure all of this aligns with your broader business objectives, goals, and governance structures.
- Ensure coordination between technical and business experts
Given that AI implementation is not just a business objective, but one that stands to impact every process, team, and member of the workforce, it is important to ensure coordination between technical and business experts. Make sure people from technical as well as business teams support the initiative through its lifecycle from design, implementation, management, through optimization.
- Maintain trust and transparency
Since resistance is a given with AI projects, it is important you maintain trust and transparency with your workforce throughout the implementation. Make sure to clearly communicate the benefits of the AI implementation, the challenges you are likely to face on the way, as well as the roles and responsibilities of people in achieving success. Curbing fears and building confidence is a sure shot to fostering adoption.
- Measure success beyond financial gains
With tangible benefits hard to measure, many enterprises seek immediate financial gains. But financial gains are not the only measure of a successful AI implementation; it is important to evaluate the project beyond monetary impact. If you are able to carry out business processes with increased efficiency, and unearth insights like never before, if your customers seem to be happier with you, and you’re able to maintain your competitive standing – you can proudly claim you have succeeded in your AI initiative.
Why will trends in BI require greater use of AI and Machine Learning
Since the inception of BI, businesses have long been leveraging the technology to carry out myriad tasks across reporting, workflow management, analytics, data visualization, self-service, and more. It is expected that the BI market will be work $17 billion by 2020. While the technology has been helping organizations to unearth critical business insights, and make better business decisions, the process is extremely complex and time-consuming. Businesses need to spend time and money in scouting for and hiring data scientists – who are difficult to find. The use of AI and Machine Learning in the BI landscape can result in far more exceptional outcomes; by allowing intelligent systems to perform labor-intensive and time-consuming tasks, organizations can make more accurate business decisions, faster. Here’s why trends in BI will require greater use of AI and Machine Learning:
- In the area of self-service, AI can make knowledge bases more optimized for users, making it easy for them to find the information they need. The more easily customers can find answers to questions, the more streamlined and efficient a customer service organization can be.
- Natural language processing (NLP) through AI bots can enable business users to talk to their data, making it easier for them to understand what they are seeing, and analyze data quickly by verbally asking questions.
- Cognitive computing can reach an entirely different level with AI; by ingesting vast amounts of structured and unstructured data, AI systems can mimic the human brain, and rearrange data into a highly manageable, easily consumable format.
- In the realm of business analytics, sifting through huge amounts of transactional and business data is a Herculean task. Using AI, data scientists can quicken the process of analysis, and improve the accuracy of predicting outcomes – without the need for complex programming.
- As democratization becomes increasingly important for businesses, AI can make intelligence accessible to every person within the organization. Such access can help every person achieve more, achieve better, and achieve faster – thereby improving business, and bottom-line stats.
- Live dashboards can enable business users to see what’s happening in real-time and receive alerts when something is out of the ordinary. Using AI, businesses can improve the ability to detect anomalies, and more easily identify trends and patterns.
How AI and Machine Learning can enable non-technical users to improve decision-making
BI has traditionally been about capable analysts scouting through massive amounts of data, and unearthing answers for people across the business. By providing sophisticated answers to questions through reports and dashboards, it has enabled enterprises to make data-driven, evidence-based decisions. However, the modern, empowered user does not want to depend on a handful of BI experts to find answers to everyday problems. The modern user wants to be able to look for answers on his own, without having to be technically competent. Here’s how AI and Machine Learning can enable non-technical users to find relevant data faster, gain insights, and make accurate decisions:
- AI systems can be trained to provide answers to questions users ask, without them having to do any coding or complex analysis.
- Intuitive search can change suggestions as questions people ask change, and provide users a list of similar queries, allowing them to get precise answers – in a matter of seconds.
- Business users with no technical background can choose the data source they want to analyze and create high-quality insights themselves.
- Instead of waiting for helpdesk to respond to a query, users can instantly communicate with bots, and get their queries resolved.
So, whether users are trying to estimate monthly sales, optimize the supply chain, or simply understand customer behavior, AI can provide actual answers instead of using models to predict those answers.
Best practices for applying AI and ML
With the capability to analyze massive quantities of data and deliver recommendations based on that data – with no human intervention – AI bridges the gap for business users with no technical knowledge, and makes analytics and big data insights accessible and understandable to the average user — not just data scientists. With trends in AI evolving every single day, here are some best practices and case examples for applying AI and Machine Learning to overcome BI challenges:
- Start by identifying areas of your business that will truly benefit from AI and ML implementation: user behavior analysis, customer service, workflow management, business insight, product improvement, and competitive position.
- No AI project can be successful without a robust data collection process in place. Since AI systems are only as good as the data that is fed into them, make sure to input large amounts of relevant data. Data that is consistent, updated, reliable and relevant is what will allow your AI systems to train properly, and deliver favorable results.
- Since AI systems are vulnerable to bias, the onus of ensuring they provide fair outcomes, without prejudice is entirely on you. In order to build trust in AI systems, you need to carry out responsible AI practices: start by identifying potential biases, and then make sure the systems are trained using unbiased data.
Are you ready?
As businesses get submerged under the sea of data, they are constantly on the lookout for tools and technologies that can help them emerge successful. While BI has long been enabling organizations to unearth insights, and make more informed decisions, AI is helping them consolidate and analyze massive volumes of data, and reshaping data into a format that is easy to understand, manage, and consume – without the need for complex analysis. Such automated and precise analysis allows businesses to understand customers better, keep pace with the market, and improve their operational efficiency. However, before you take the plunge, it’s important to understand where you stand in the AI readiness spectrum. Make sure you have the people, processes, and culture in place for widespread adoption. Since AI algorithms thrive on data, make sure to train them using data that is reliable and relevant. Without good quality data, the predictions and analyses AI does might be worthless; so, do your homework, and invest in AI only if it makes business sense.