In one corner, there’s plenty of evidence that autonomous intelligence is the future of technology. From self-running cars and warehouse robots to auto farming solutions, autonomous technology has brought extraordinary decision-making capabilities across industries. They manage tasks, intuitively interact with the environment, and drive business outcomes with little to zero human involvement. The enterprise world is keener on having the firepower to combat operational inefficiencies and unexpected security threats in the other corner. Moreover, with the adoption of AI-driven automation gradually increasing, the more successful players have begun using autonomous enterprise software to make the whole process more cognitive and intelligent.
According to a recent survey conducted by a research giant, last year saw a reduction in global IT spending in the face of unexpected contingency plans and budget cuts – but it also witnessed a rise in the adoption of cloud-based SaaS applications. Their findings also showed that 57% of businesses have already adopted IoT and real-time analytics. Weighing in the estimation that a 10x growth in data is expected through 2025, there will be a gradual move towards self-driven enterprise applications.
But today, it isn’t just about accelerating the completion of specific use cases that involve heavy-duty manual tasks. Instead, it is about equipping the enterprise to focus on growth by taking decisions more intelligently, cost-effectively, and quickly. And it isn’t as though they have a choice, given the rapidly changing expectations of users regarding what constitutes a good application experience.
Battling application woes and encouraging user adoption
Lack of seamless and synchronized record-keeping:
More enterprises are adopting information-centric applications, which means that a lot of data gets processed every day, and sometimes, it occurs throughout disparate channels. Incubating an application culture of autonomy can help in easily handling hefty workloads while keeping any security threats at bay. In addition, it can provide cloud-based access to a continuous flow of information to widen the scope for automation.
Little to no data insights to deliver great user experiences:
SaaS business applications possess the ability to monitor and manage processes in real-time analysis, but they may lack the predictive capabilities to make further things easier for users. The enterprise can stitch together data from different touch-points with autonomous systems to develop unified customer personas that continuously improve user experiences.
Business impact of autonomous enterprise-class applications
Autonomous enterprise applications leverage AI technologies like Machine Learning (ML) and Natural Language Processing (NLP) to become a more self-sustaining system. Furthermore, with the growing demand for more intelligence in ERP, CRM, and HRMS, etc., there is a need for these systems to think and act like humans without getting bogged down by associated flaws. Hence, ML-driven algorithms are used to extract a tremendous amount of structured and unstructured data before and interpreting them to provide valuable insights.
With autonomous technology, the application can significantly reduce data entry-related errors while offering reusability and scalability as future-ready advantages. Since most enterprise applications rely on a centralized database, infusing autonomous intelligence ensures more accurate and thorough metric reporting and speedier execution of business-critical tasks that affect day-to-day operations. It also adds a thick layer of protection to the application by identifying breaches and eliminating disruptive delays.
In addition, the enterprise can add data parameters based on language technology or the emotional intelligence of users to improve the relationship between employees and applications.
Some of the other benefits of autonomy that cuts across several other enterprise business applications include:
- Improve employee productivity by automating cross-functional interactions and eliminating process redundancies – leading to better utilization of daily work hours
- Leverage a unified data repository, integrated with smooth machine-to-machine communication, to increase the speed of data processing
- Understand patterns and anomalies through actionable insights on key application metrics
- Identify more scope for automation within the technology ecosystem
Questions to ask before considering autonomous applications
Can the application be integrated into the current technology architecture to allow dynamic data management (Or does it require a complete overhaul of the ecosystem?
- Will the application provide opportunities for cross-functional teams to securely and seamlessly interact? (Or does it come with limited collaborative features?
- Will the application reduce the cost and effort involved in maintenance and support? (Or would it lead to ‘higher than before’ recurring costs?
- Is the application equipped to handle end-to-end processes without any manual intervention? (Or would it only take care of specific digitally-enabled processes?
Autonomous solutions can be harnessed, from HR and finance to planning, procurement, and legal, to improve daily operations. Their self-running, self-protecting, and self-repairing workflows can help save a ton of time, money, and effort. More importantly, they open the floodgates of innovation to improve workforce productivity and boost application performance.
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