Authored by Baker Tilly’s Chris Wagner, David Hickey
As organizations accelerate their adoption of artificial intelligence (AI), many are moving beyond experimentation and into execution. What began as an exploration of generative AI is rapidly evolving into a broader transformation of how work gets done. At the center of this shift is the convergence of robotic process automation (RPA), intelligent automation and agentic AI — technologies that are redefining both operational efficiency and workforce strategy.
The evolution from RPA to intelligent automation
RPA has long served as a foundational automation tool, enabling organizations to automate repetitive, rules-based tasks by mimicking human interactions with systems. These bots operate deterministically, following predefined logic to execute structured, high-volume processes such as data entry, reconciliation and system updates.
However, as business processes become more complex, the limitations of RPA have become more apparent. Traditional bots cannot interpret unstructured data or adapt to nuanced scenarios. Intelligent automation addresses this gap by layering AI capabilities, such as machine learning and natural language processing, onto RPA. This allows systems to interpret information, make decisions and manage exceptions, significantly expanding the scope of automation.
Understanding agentic AI
Generative AI has introduced powerful, prompt-based interactions that allow users to quickly access and synthesize information. However, these systems are inherently reactive and can lack consistency across interactions.
Agentic AI builds on this foundation by introducing structure and autonomy. Instead of one-off prompts, agents are configured with defined roles, responsibilities and guardrails. They operate within established parameters, leveraging business rules and contextual data to make decisions and execute tasks.
The importance of the process layer
While generative AI enhances how organizations access knowledge, the true transformation lies in the process layer — where work is executed across systems, applications and teams.
This is where intelligent automation delivers the most value. Bots handle deterministic tasks such as data validation and system updates, while agents address more complex scenarios requiring interpretation. Together, they enable seamless, end-to-end process execution while reducing friction and manual effort.
Orchestration as the control layer
As automation scales, organizations must manage an increasing number of bots and agents across multiple platforms. Without coordination, this can quickly become fragmented.
An orchestration layer provides centralized oversight, managing workflows as they move between bots, agents and people. It ensures processes run efficiently, provides visibility into performance and enables organizations to identify bottlenecks and optimization opportunities.
By analyzing process flows and outcomes, organizations can continuously refine operations and improve decision-making at scale.
A hybrid workforce model
Rather than replacing people, intelligent automation is reshaping how work is performed. The most effective organizations are adopting a hybrid model:
- Bots execute repetitive, rules-based tasks with speed and accuracy
- Agents think within defined parameters to manage complexity and exceptions
- People lead by focusing on strategy, relationships and innovation
This approach allows organizations to reduce inefficiencies while empowering their workforce to focus on higher-value activities that drive growth and differentiation.
Driving value through a strategic approach
Successful adoption of intelligent automation and agentic AI requires more than technology — it requires strategy. Organizations should start with targeted use cases that demonstrate clear value, building momentum through a “prove it” phase before scaling more broadly.
Equally important is evaluating where automation makes sense. Not every process is a candidate for automation; factors such as volume, complexity and return on investment must guide decision-making. A disciplined approach ensures sustainable, measurable outcomes.
Managing risk and building trust
As organizations implement AI-driven solutions, responsible deployment is critical. Systems should be tested, validated and governed with clear guardrails, particularly in customer-facing scenarios.
Human oversight remains essential to ensure accuracy, manage exceptions and maintain trust. A thoughtful, methodical approach helps organizations balance innovation with control while minimizing risk.
How Baker Tilly can help
Baker Tilly works with organizations at every stage of their automation journey, from early exploration to enterprise-scale transformation. Our approach combines deep industry experience with leading technology capabilities to help clients move from concept to execution with confidence.
Whether starting with a focused proof of concept or advancing a broader transformation initiative, Baker Tilly helps organizations unlock the full potential of intelligent automation and agentic AI; driving efficiency, enhancing decision-making and enabling long-term growth.
