Data allows us to work efficiently, reducing unnecessary workload and helping us keep employees supported, engaged and motivated. In a time like today, where change is constant, it plays a major role in helping us acclimatize and recover.
However, to tap into existing data and make it work harder than ever before, it’s important for businesses to invest in the right kind of technology. AI and machine learning are areas that are still relatively new for most enterprises, yet tech leaders such as IBM have been conducting AI research for decades. And despite uncertainty brought on by the global health crisis, now is the time to begin unlocking the potential of artificial intelligence.
So how are organizations leveraging AI to make better decisions that will support the future of the business? Here are 7 expert tips for a smooth transition into digital transformation and Big Data analysis.
1. Make customer experience a priority
The customer journey is more important than ever. If you want to engage with your customers in the right way, knowing what motivates them is key. This means you’ll want to understand their needs and desires, their pain points and their areas of interest to map their behavior. Data can help you gain this level of insight into your customers and how they’ll move along the funnel.
By harnessing the power of AI, you can cut down the time it takes to source quality leads while simplifying the process of account selection. You can also turn data into valuable insight for the personalization of campaigns across every industry and sector, and you can boost efficiency and drive better results in your contact center. The opportunities with AI and customer data are endless, and by using this technology to get to know your audience, you’ll always be one step ahead in CX.
2. Redefine job roles with AI and automated tools
As you modernize your data platforms and simplify data collection, you have the opportunity to unlock the value of your data in a labor-efficient way. While there have been suggestions of replacing workers by deploying AI and automation, the reality is very different. This technology is about augmentation rather than replacement.
According to Gartner, AI augmentation will generate US$2.9 trillion of business value and recover 6.2 billion hours of productivity by 2021. So this presents a chance to reduce analyst workloads and redefine job roles within IT. This is hugely beneficial for companies that want to empower their workforce, nurture talent and reduce staff turnover.
3. Unify data management and break down silos
In order to maximize opportunities with AI, it’s important to have the whole picture when it comes to your data. If you want to prepare your data for an AI and hybrid cloud world, your first priority is to make it complete – and make it clean.
This begins with removing those invisible barriers across the organization. Collaboration is key, not just within teams, but across different departments, branches and international divisions.
By working together, you can avoid doubling up on the same workload and create actionable insights company-wide. Break down traditional silos and data can be shared to provide meaningful value for AI technology.
4. Understand the risk, and use AI to minimize risks
The rise of AI can be double-edged sword. Not only is AI a risk in itself, but it’s also a way to manage risk. As our amount of data grows, and the remote workforce becomes commonplace, the way we store and access our data will be paramount. Through machine learning you can make informed predictions, and AI-based analytics platforms can help you manage things like supplier risk. It can also be used to assist security analysts and significantly reduce the time it takes to investigate and remediate threats.
Like most new technologies, AI presents opportunities when applied well, and creates risks when applied badly. This is why it’s vital to never overestimate the capabilities of AI, and to have robust security measures in place.
5. Look at cost as a long-term plan
Cost savings is one of the biggest benefits of AI-powered data analytics. But any organization interested in lowering overheads should look at costs with a long-term view.
Rather than simply replacing human workers, the true value of machine learning and automation are things like productivity, improved customer experience, better customer and staff retention, enhanced online reputation, and having a competitive advantage in your field. All of these factors feed into the bottom line, and the real advantage when it comes to operational cost is found in efficiency.
6. Have a strategy to reach data maturity
When it comes to the data maturity curve, those businesses at the beginning will likely apply their data with an emphasis on cost reduction. But as their data maturity advances, there can be a shift from a cost focus to the purpose of business intelligence.
Data maturity matters because organizations that are data mature are at a competitive advantage. They will be able to leverage predictive analytics to spot opportunities in advance, anticipating future trends. Therefore, having a data strategy and mapping out the data maturity journey is key if you want scale insights for innovation and prepare for the AI revolution.
7. Use AI-powered tools as a part of your planning
Planning without analytics in place is planning without insight, so as your organization moves further along the data journey, it’s important to invest in AI-infused planning tools. Things such as automated model creation, natural language processing and cognitive help will make it quicker and easier than ever to build an accurate plan for the future.
When operations are complex, AI-powered planning analytics can make it simple to get an overview of financial performance, budgets and forecasts. So for businesses that want to be one step ahead, AI is a critical feature to adopt.
In terms of data-backed decision making, AI can make your business smarter and stronger. To find out more about how you can take advantage of AI, sign up for the ‘IBM Data and AI Emerging Smarter’ roundtable series.
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