Driving Enterprise Application Transformation and Modernization with Agentic AI

Agentic AI has matured to a point where companies are now trusting it not only to execute code, but also to plan and direct software evolution in the context of enterprise goals and aspirations. This is important as software drives so many of today’s business operations. Consider what impact agentic AI might have in the context of optimizing entire supply chains. Or creating and submitting complex financial compliance reports. Or assisting teams in managing workforce interactions from hiring to retiring. It is essential that application modernization is ensured through Agentic AI powered systems.  

Agentic AI technology has demonstrated the capacity to autonomously make real-time decisions, based on analytics and strong governance. Given secure and ethical boundaries, it can proactively enhance resource allocations, set workflow priorities, and forecast outcomes, among other tasks. No wonder businesses across a broad spectrum are now rapidly developing agentic AI applications – and often, using agentic AI to do it. 

Forbes recently stated that agentic AI is “no longer solving for individual use cases but creating contextualized agents that collaborate as though they’re part of a ‘real-world’ team.” Indeed, Grand View Research expects the autonomous AI and agents market will reach $70.53 billion by 2030, citing that context awareness, “a system’s ability to collect and process information about its environment and make autonomous decisions,” will accelerate the technological adoption. 

What this means for top executives, from finance and retail to healthcare and logistics, is that agentic AI solutions will increasingly be used to enhance productivity, giving birth to lean teams and standardized processes. This article will discuss the power of Agentic AI technology in transforming Application modernization.

The Case for Agentic AI

Beyond generative AI (GenAI), which can also create new content such as text, video, audio, and software code, agentic AI can make decisions and bring operational efficiency. Thus, AI-powered systems have the ability to drive full autonomy across end-to-end processes, such as procure-to-pay, order-to-fulfillment, and record-to-report, reducing errors, time and cost. Here are some examples of agentic AI use cases already in play:

• Human Resources: The freedom to trust broad, labor-intensive workflow restructuring to agentic AI allows valuable human personnel the ability to focus on more mission-critical activities. Organizations needn’t double-down on sourcing and recruiting high-paid talent to ensure enterprise application transformation. This is important in markets where labor shortages are already creating strains.
• Healthcare: Agentic AI can be integrated into enterprise applications, such as Salesforce, to reshape patient interactions and improve customer experience across the healthcare journey. It can also significantly ease administrative burdens by autonomously performing onboarding and credentialing tasks, verifying provider records, and generating comprehensive client summaries for lower cost, greater accuracy, and improved operating efficiency. 
• Telecom: By rolling out differentiated solutions at scale and speed, agentic AI technology is helping telcos compress time-to-market on new products and services, boosting revenue while also cutting cost. Likewise, customer experience is enhanced by preventing network disruptions and boosting service reliability through intelligent automation, real-time monitoring, and enhanced predictive maintenance. 
• Financial Services: Agentic AI has opened new vistas of opportunity across the financial services sector. Autonomous trading based on changing market conditions in real-time is now possible. Providing personalized financial advisory services based on individual client goals, risk tolerance, and tax-loss considerations, among other factors, can now occur autonomously. Even risk management is being enhanced by agentic AI powered systems capable of monitoring transactions, customer behavior, and the market all at once, and taking preventive actions when necessary.
• Technology: The ability to quickly develop and implement new business strategies using agentic AI templates and blueprints is breaking new ground in a variety of industries. Intelligent document processing, drug discovery, customer service, and claims processing head the list, but more are coming. Agentic AI is driving organizational transformation by creating new applications tailored to the needs of business.  

The technology, hosted on the cloud, enhances scalability, adapting to user needs, workloads, and emerging technological advancements on-the-fly. Lastly, agentic AI solutions can help organizations stay on the cutting edge by continuously monitoring enterprise applications in real-time and proactively suggesting updates and enhancements.

Simplify Modernization

Application modernization makes perfect use case for Agentic AI. It combines numerous AI technologies, such as unsupervised learning, natural language understanding, and semantic knowledge capabilities, to perform complex tasks with increasing proficiency. This makes it ideal for code conversions, testing, version upgrades, and debugging operations, where manual processes can be error-prone, costly, and time-consuming.  

When embedded into the application modernization process, agentic AI leverages the power of automation and AI learning systems to independently analyze, optimize, and enhance legacy systems. As a result, application modernization becomes more fluid and perpetual, as agentic AI can swiftly innovate and unlock competitive differentiation. In fact, multi-agent models can independently work together to identify and, in some cases, develop fresh opportunities. As Harvard Business Review puts it, “With their enhanced judgement and powers of execution, agentic AI systems are ideal for experimentation and innovation.”

Activities to achieve application transformation include:

• Automated Code Refactoring: Examining current codebases and upgrading them to contemporary architectures.
• Intelligent Workload Placement: Using AI-driven insights to identify which parts of an application should migrate to the cloud or remain on-premises.
• Streamlining Workflows: Automating repetitive tasks, such as testing, deployment, and monitoring for faster, error-free updates.

Specific examples of agentic AI in application modernization include: 

• Legacy System Upgrades: Identifying redundant code, optimizing application logic, and modernizing outdated technologies. 
• Cloud Migration: Automating the re-hosting, re-platforming, and re-architecting of applications for cloud environments at reduced time and cost.
• Predictive Maintenance: Integrating predictive analytics into enterprise applications to preempt failures and ensure smoother operations.
• Personalized User Experiences: Enhancing customer-facing applications by delivering personalized recommendations or adaptive interfaces.
• DevOps Automation: Streamlining continuous integration/continuous delivery pipelines, automating quality assurance, and accelerating application deployments.

Overcoming the Hurdles

For all that agentic AI brings to the table, it will require organizations to make several transformational changes before realizing a return in the form of application transformation. For starters, system designers must be able to explain the reasoning behind agentic decisions (aka, explainable AI), before trust on the technology is established. This is essentially a data engineering question that must be addressed up front. 

By defining the underlying rationale in an understandable way, for instance, what data sources were queried, the rules that govern a specific decision, how these decisions are justified within the model, etc., system designers can prevent unpredictable agentic behavior and establish credibility leading to trust.

To this point, taking a human-in-the-loop approach can go a long way toward building confidence in AI systems and facilitating application modernization. This can be particularly valuable within regulatory frameworks, where accountability is paramount. By keeping a human-in-the-loop, autonomy may be balanced with human judgment, ensuring a trustworthy decision chain, while taking advantage of the efficiencies inherent in AI-based systems.

Some processes may also lend themselves to a maker-checker model of governance. Here, tasks performed by one agentic AI system can be cross-checked by another, using a rule-based decisioning framework.

As always, structured, accurate data must be in place to inform agentic decisions. In some cases, this will require organizations to address long-neglected data management issues involving siloed information, disparate systems and platforms, and moving data to the cloud where AI agents can access it readily. Having a centralized data lake on the cloud serves these purposes well.

By itself, process standardization will improve the efficiency, productivity, and scalability of any enterprise seeking an edge. But, when coupled with the integration of agentic AI, true business logic can be established, hardened, and enforced, enhancing outcomes for users, customers, and the brand as a whole.

Choose your Starting point and Begin

With a sound data foundation, clear business logic, and an explainable model to start, launching a pilot program is the best way to prove the value of agentic AI technology and form a basis for program expansion. Be sure to set measurable goals going in, to check the efficacy of each agentic system, and begin building a track record. As you learn from the experience, you will begin to see benefits from the integration of agentic AI in your various modernization efforts. Based on your industry, here are some of the benefits you can expect:

• Finance and Banking firms may find it easier to streamline legacy systems, enhance security, or introduce new, AI-driven customer experiences. Finance and Banking firms may find it easier to streamline legacy systems, enhance security, or introduce new, AI-driven customer experiences. 
• Healthcare organizations may begin to improve patient care, data management, and compliance in the face of evolving regulations.Healthcare organizations may begin to improve patient care, data management, and compliance in the face of evolving regulations.
• Retail and E-commerce businesses will be able to efficiently optimize supply chain operations, while personalizing customer interactions and scaling for peak seasons.Retail and E-commerce businesses will be able to efficiently optimize supply chain operations, while personalizing customer interactions and scaling for peak seasons.
• Manufacturers may choose to enable smart factories, integrating IoT data with modernized apps for real-time decision-making.
• Public Sector organizations will be able to enhance service delivery, data transparency, and cybersecurity by upgrading outdated systems.

Agentic AI, for all its specificity, granularity, and co-operative qualities, when working with humans or other agents behind the scenes, has the power to change the way organizations collaborate, serve, and succeed. The technology’s promise of independent, automated, and intelligent reasoning is now being realized in industries worldwide. First movers in this space will reap the greatest rewards. Now is the time to consider a test run—and prove the viability of agentic AI in your organization. 

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