Automation, artificial intelligence, machine learning—these terms dominate conversations around next-generation technologies and how organizations can continue to digitally transform. But what do these technologies really mean to a business, and what does it take to integrate them in productive ways? Intelligent business apps help business make better decisions at the point of transaction and provide more value to their customers. However, adopting intelligent applications is just part of a transformation. It also requires strong leadership to imagine how this technology can elevate all business processes.
In most instances, when we talk about any of these technologies, what we’re really referring to is real-time decision-making. Intelligent business applications serve as agile recommendation engines that allow business users to make decisions at the point and time of a transaction. For example, they enable businesses to personalize transactions by understanding a customer’s historical behavior, comparing it to the behavior of similar customers, and making recommendations about services or products that the customer is likely to appreciate. The key benefit is speed of decision-making.
Say a bank loan officer is sitting across from a loan applicant. Rather than the officer making a decision based on hunches about that person’s personality, presence, and the static data on a computer screen, a recommendation engine can instantly provide a decision about whether to accept or deny the application based on information gleaned from tens of thousands of other loans to customers with similar, pre-selected characteristics. Whether businesses are offering products or services, intelligent business applications allow users to receive recommendations in hyper-real time, thereby improving the targeting and reliability of their offering.
How do Organizations Adopt Automation and AI?
Employing intelligent business applications to the fullest means truly transforming the business from core to edge. From a technology standpoint, this means enhancing business processes, connecting existing systems, and streamlining adoption throughout the enterprise.
Breaking Down Data Silos
Most organizations have inadvertently siloed information. The first stage in the effective integration of AI and automation into business applications entails breaking down these silos. This means gathering all business stakeholders and asking them to inventory what data they collect, then asking how they can use that data to help them make better and more timely decisions. Then, IT leaders can determine how to backtrack to find potential roadblocks caused by siloed processes, policies, and information. Each business unit should be brought into the transformation process to talk about how shared data can improve their decision-making process and overall business performance. IT leaders are then responsible for determining how best to link pertinent data flows to the central data reservoir.
Making the Most of Data
The central data reservoir has to contain clean, standardized data. Dozens of analytics companies can set up a data management platform. However, data is not monolithic. It comes in all types of formats—text, numeric, multimedia, models, software languages—and from various sources (social media, sensors, emails, enterprise software applications). Making the most of data requires adopting technology that can cleanse and standardize information coming from these different sources so that it’s structurally identical. All reporting and insights should be drawn from a common data reservoir, so that everybody across the business is working from the same information.
Using Intuitive Dashboards
One important technology choice is user interface. Choosing intuitive dashboards lets business users easily understand what data they have on hand and uncover actionable insights that can help them increase their productivity. Effective dashboards allow users to generate the recommendations they need to increase their productivity. An effective dashboard should include content and a level of detail appropriate to the specific user. Based on the details on the dashboard, users should be able to tell stories that result in action.
Optimizing Edge Devices
Finally, edge devices must be optimized. Today, such devices include mobile phones, tablets, robotic systems, customer relationship management systems, and more—but whatever the point of interaction, devices and the user interface must be equipped to securely deliver the real-time recommendations being sent from the data management platform to the points of transaction. When it comes to edge devices, an organization’s security team and business leaders should be involved in purchasing decisions to ensure that these devices are secure and reliable. It’s also vitally important that the C-suite and IT decision makers discuss the anticipated needs of the enterprise going forward, so they can acquire technology that is scalable and future-ready.
How Can Businesses Ensure Successful Workplace Adoption?
Beyond the technology choices, transformation is also a people process. It must be driven by a strong, transformation-minded leader, and should entail sound change management strategies to ensure enthusiastic and efficient adoption by the workforce.
Consistent, proactive communications can help employees view their work through the automation lens. All stakeholders should be included early in the implementation process. Business and IT leaders must communicate the reality that automation will eliminate the dull, repetitive tasks that make work “work,” replacing those tasks with more purposeful work and the potential to transform employees’ careers.
That’s because intelligent business applications allow “blue-collar” technology jobs to become more like “white-collar” technology jobs. If engaging training programs are launched early in the process, employees will realize that they’re being equipped with new technological skills that will make them more marketable both inside the organization and out.
Developing a change management strategy should involve a collaborative approach to understanding and designing a personal and empowering experience for all stakeholders. People should be at the center of the process. Transformation will work when employees feel like their concerns are heard. Workers should understand why automation is happening, how it benefits the company and their careers in the long run, and what metrics will determine success.
When automation works, the advantages to the enterprise are far-reaching, extending beyond mere economic rewards to genuinely benefit employees and customers alike. Additionally, because automated systems grow more intelligent with time, continuously learning from data platforms that translate industry/market data and the internal operating model into actionable insights, users can make more informed decisions in the future.
Business leaders can learn which customers provide the most value to the organization and how to prioritize the needs of those customers—in an exchange that evolves every time they transact business.
Recommendation systems can also help decision makers prioritize product development. And, if an algorithm's criteria are carefully chosen, the right business decisions will be easier to understand not only for business users, but also for customers. People mistakenly think intelligent systems are opaque, but there are products on the market now that explain how algorithms make decisions on a case-by-case basis.
This explainability can even be integrated or scaled in the business. Automating a company is about much more than installing new, intelligent applications. Yes, it entails setting a transformation agenda with data and AI first, but it also involves governance to tackle a range of emerging concerns, from privacy and security, to accessibility, sustainability, and digital inclusion. This requires leadership from both the IT department and the C-suite to manage digital and organizational advances and change the mindset of the workforce.