Tad Travis, VP Analyst at Gartner, highlights how AI-driven autonomous business is revolutionizing enterprise applications, enabling intelligent systems that adapt, automate, and optimize decision-making while redefining workflows and employee roles.
Autonomous business represents the next wave of transformation, fundamentally reshaping the purpose, form and function of applications for employees. While digital transformation has been crucial in leveraging digital technologies to revolutionize organizational operations, deliver value to customers and enhance competitiveness, artificial intelligence is now driving the evolution towards autonomous business. This new style of business is partly governed and majority-operated by self-learning software agents that provide smart products and services to machine-customer-prevalent markets operating in a programmable economy.
Transformative Impact on Enterprise Applications
This trend is particularly relevant to CRM, digital workplace and ERP applications, which are experiencing a transformation in purpose, form and function not seen since the advent of SaaS applications 25 years ago. Applications are evolving from tools that merely facilitate processes, workflows and tasks into intelligent systems that operate on behalf of end users.
While generative AI is the primary catalyst for this transformation, it is not the only technology shaping the future. Several other developments are contributing to this shift, creating a more integrated and seamless experience for users. End users now expect applications to be as user-friendly as consumer apps available in app stores, driving a demand for more intuitive interfaces. Concurrently, the traditional lines between applications and advanced analytics tools have blurred, making these tools indistinguishable from one another. This convergence is further enhanced by algorithms, agents and bots that are increasingly performing tasks on behalf of end users, automating many work processes. Moreover, metadata about application usage and the work performed within applications has become as crucial to organizations as traditional transactional data, providing deeper insights and more informed decision-making. Finally, applications are evolving from monolithic structures to more modular and adaptable systems, allowing for greater flexibility and scalability. Together, these developments are reshaping the landscape of enterprise applications.
Implications for Employee Work and Application Deployment
The developments in the landscape of enterprise applications will permanently alter the nature of work that employees perform, leading to significant changes in the types of applications enterprises deploy to their workforce.
Accordingly, enterprise application leaders should also take a macro-level, long-term view of application capabilities and the type of work that applications will perform. Applications will change from supporting business process execution into intelligent applications that work on behalf of employees.
Intelligent Applications and Its Characteristics
An intelligent application acts appropriately and autonomously through learned adaptation, enhancing functionality, improving user experiences and elevating decision-making capabilities. These applications can learn from data, adapt to user behavior and make autonomous decisions to optimize performance and outcomes. Intelligent applications are differentiated from other applications by five characteristics outlined below.
Adaptive Experience
Adaptive experience refers to the user experience (UX) philosophy and design capabilities that enable dynamic, custom-made interactions for users. The goal is to reduce digital friction, making it easier for users to execute process steps and enter or retrieve data. Instead of static lowest-common-denominator interfaces, adaptive experiences tailor the interface to match how individual end users need to work in specific business moments. This results in synthesized, proactive and contextual user interfaces.
Embedded Intelligence
Embedded intelligence augments and automates decision-making, replacing the need for separate analytical and discovery work typically conducted in separate dedicated applications. It spans a spectrum of decision-making needs, determined by the degree of insights necessary to support decisions and the agency of those decisions. This integration creates more efficient workflows, improves decision quality and enhances user adoption of applications.
Autonomous Orchestration
Autonomous orchestration involves capabilities and a design philosophy aimed at introducing higher forms of AI process standardization and automation to business processes, especially those involving unstructured data. This enhances operational efficiency and metadata collection. It allows for business orchestration that encompasses diverse technologies, from low-code platforms to robotic process automation (RPA), traditional integration platforms and AI agents.
Connected Data
Connected data represents an advanced, integrated approach to managing and utilizing data across an organization. It leverages metadata, continuous analytics and robust integration technologies to create a cohesive and efficient data ecosystem. This drives autonomous orchestration, embedded intelligence and adaptive experience.
Composable Architecture
Composable architecture is a software design approach that emphasizes creating systems from modular, interchangeable services. This architecture allows for greater flexibility, scalability and adaptability, enabling organizations to respond quickly to changing business needs and technological advancements. Key principles include modularity, reusability and interoperability, facilitating a more responsive and resilient application environment.
