By Ramon Mueller
Artificial intelligence (AI) and machine learning (ML), a key component and driving force of AI, are not new concepts in the tech world. And, despite the attention they receive, there still isn’t a clear consensus on the best way to apply these tools to improve business operations and make software applications more intelligent. The best way to achieve improved application intelligence is to embed AI and ML into software applications, which helps existing applications run more efficiently and effectively, and better inform behavior and business decisions. Organizations embrace this approach as they see the positive effects of automation on operational efficiency and improved customer experience.
Improving with AI
Three ways that AI with ML, infiltrates and improves software applications includes the streamlining of services, incorporating real-time updates, and consistent compatibility with the cloud.
When AI and ML are incorporated into new or existing software applications at an organization the business can immediately begin to see benefits, particularly the streamlining of previously separate services. AI assistance allows organizations to combine services and capabilities, which has produced positive results for marketing and sales teams that implement these technologies. For instance, prior to implementing AI technology, Epson reported low response rate from customers when following up on leads. According to Brad Power’s How AI Is Streamlining Marketing and Sales, published by the Harvard Business Review in 2017, after deploying an AI assistant, the company saw a 51 percent increase in response rates, representing a 240 percent increase from the baseline established prior to a study they did surrounding the combination of sales, conversions, and customer insights. Not only can AI assist with streamlining data from more than one source, it has the ability to filter out irrelevant information so that employees focus on the most current and actionable data.
While AI is key when it comes to managing logistics, it can also extend to content creation. Through the use of software, employees can streamline responsibilities and free up time to focus on clients’ needs and experiences instead of creating and maintaining thousands of pieces of content. As more cognitive skills such as voice, vision, and natural language (NLP) are added to AI, its knowledge and search capabilities will strengthen.
Incorporates Real-Time Updates
As businesses and opportunities evolve, so does AI. With continuous, real-time updates, AI ensures that content and marketing collateral accurately aligns with clients’ specific needs and wants in a client-centric way. This means employees no longer have the added burden of monitoring for updates. This is good news for everyone because when employees have more time to focus on the task at hand, they can do their job better. Consistent maintenance allows software updates in real-time, reducing and eliminating the hassle and stress associated with outdated or incompatible software and its data. This puts a company ahead of the game and raises the bar for competitors.
AI can also provide real-time insights into product lifecycles and the stages of development. This includes the ability for customers to track components of a product from manufacturing to creation to final delivery. It provides insight into errors or lags, identifying areas for improvement regarding customer experience, according to a post on AI Business’ website, Siemens CEO: Bridging Gap Between AI and IIoT Holds Key To UK’s Industrial Future.
Consistent Compatibility with Cloud
It’s no secret that the cloud isn’t going anywhere. In fact, in a Forbes’ website post in January 2019 by Louis Columbus, it is predicted that 83 percent of enterprise workloads will be in the cloud by 2020. According to Gartner, in 2019, 37 percent of CIOs plan to deploy or have already deployed AI, confirming the inevitability that AI and the cloud will soon be synonymous. In fact, Columbus notes a recent survey that predicts AI and ML will be the leading catalyst driving greater cloud computing adoption by 2020. Investing in AI and ML is an investment in the future of business and one that will continue to provide benefits as the transition to the cloud becomes more widespread.
In addition to these three core benefits of AI implementation, deep learning, neural networks, and predictive analytics using ML based on multi-layer patterns will push AI to the next level. Simulating human behaviors creates a deep learning evolution. Edge computing is pushing AI/ML capabilities to connected (i.e. IoT) devices and distributed systems, and ML provides the capability to process the data where it is produced.
AI and ML can successfully provide personalized smart applications to meet individual business needs in near real time. By using the same knowledge and preferences, automation can now become a part of the development process. It is inevitable that this process will be constantly evolving based on feedback and changing human behavior. This will give software providers the opportunity to be proactive rather than reactive. Furthermore, giving them the capability to have the solution known before the customers know that they need it.
Because the process is automated, the time to market will be reduced and will free up teams to focus on more time-sensitive emergencies like trouble shooting or other exceptional cases. Over time, the process will become standardized. These combinations will result in the reduced waste of useless features, reduced spamming and flooding because the offer has evolved to become personalized and matched. Applications need to leverage available data to trigger tasks based on predictive models. This furthers the notion of proactivity and the increased efficiency and effectiveness in comparison to reactive tasks. Having the ability to find a system that recognizes data input will ultimately result in the ability for direct actionable intelligence, detection of blind spots, and the correction of things automatically when possible. This extends into an investment in the future.
Being trapped in process or administrative work—which goes largely unnoticed—is becoming a thing of the past with the introduction of AI and ML to business processes. By embracing the ability of these applications to streamline services, provide real-time updates, and increase compatibility with the cloud, businesses have the ability to prepare and maximize the potential of software as it continues to evolve in anticipation of the future.
Ramon Mueller is a results-focused technology leader with more than 20 years of IT experience and a proven track of record in international implementations of ERP, CRM, web, and other business-process-supporting applications. In his role as CTO at SoftwareONE, Mueller is focused on leading the company’s technology initiatives to drive customer innovation and digital transformation. In addition, he is in charge of development and architecture for SoftwareONE’s PyraCloud platform, which enables organizations to manage and optimize their investments in cloud and software resources.
Aug2019, Software Magazine