As organizations look toward the future, AI-driven intelligent automation (IA) has emerged as a key enabler for digital transformation. But intelligent automation isn’t a one-size-fits-all solution—it’s a journey with distinct stages of maturity. For technology and business leaders in sectors like healthcare delivery, insurance, and government, understanding where they stand on this maturity model and creating a tailored roadmap for progression can make the difference between just keeping up and becoming an industry leader.
This maturity model, designed for AI-driven intelligent automation, provides a framework for assessing the current state, planning transformation, and laying out a path to future capabilities. As Dr. John Halamka, President of the Mayo Clinic Platform, aptly noted, “Healthcare, like many regulated sectors, must not only innovate but also find strategic pathways to scale these innovations responsibly.”
Let’s dive into the five stages of maturity in AI-driven intelligent automation and explore how leaders can approach each stage with practical, tailored strategies.
Stage 1: Initial – Exploring Automation
At this stage, organizations are just beginning to explore automation possibilities. Processes are largely manual, and automation is implemented on a small scale, often without a long-term strategy. Here, the focus is usually on simple, rule-based tasks rather than complex, AI-driven solutions.
In industries like healthcare, early automation often involves administrative tasks. Karen DeSalvo, Chief Health Officer at Google Health, notes, “Administrative burdens are one of the easiest areas to target for automation, freeing up healthcare professionals to focus on patient care.”
Guidance for Leaders:
- Current State Assessment: Identify repetitive tasks that are low-risk but high in volume, suitable for initial automation.
- Tailored Planning: Develop a short-term plan for automating select tasks using robotic process automation (RPA).
- Roadmap Forward: Start documenting outcomes from these small wins and use them as a foundation for justifying future, more complex automation projects.
Stage 2: Managed – Basic Automation with Limited AI
At the Managed stage, organizations have begun standardizing their automation initiatives, though they remain largely rule-based. AI integration is still limited, but there’s a growing recognition of the potential for AI-driven insights in more complex workflows.
In insurance, basic automation often tackles repetitive data processing. Dan Glaser, CEO of Marsh McLennan, points out, “Automation in insurance has been effective in streamlining claims processing and data entry—areas where consistency and accuracy are paramount.”
Guidance for Leaders:
- Current State Assessment: Evaluate existing automated workflows and identify bottlenecks where AI might add value, such as claims data categorization or anomaly detection.
- Tailored Planning: Implement machine learning models on a small scale to analyze repetitive data-driven tasks and improve decision-making.
- Roadmap Forward: Create a roadmap that transitions from rule-based RPA to AI-enhanced automation, focusing on workflows where data insights can add immediate value.
Stage 3: Defined – Integrated AI for Decision Support
At this maturity level, organizations are beginning to integrate AI to enable decision support in key processes. Rather than just automating repetitive tasks, the focus shifts toward using AI to augment human decision-making. This is a crucial shift, particularly in industries where regulatory compliance and accuracy are critical.
In healthcare, integrating AI for decision support often means applying AI to clinical workflows, diagnostics, or patient risk assessments. Dr. David Feinberg, former CEO of Google Health, noted that “AI’s potential in decision support is immense, especially in areas like diagnostics where precision is paramount.”
Guidance for Leaders:
- Current State Assessment: Map out decision-intensive workflows and identify which can be enhanced by AI-driven insights.
- Tailored Planning: Invest in predictive analytics and natural language processing (NLP) to support more informed decisions in real-time.
- Roadmap Forward: Develop an integration roadmap that aligns with regulatory requirements, ensuring any AI-driven insights are validated for compliance and reliability.
Stage 4: Quantitatively Managed – Predictive and Proactive Automation
At this stage, organizations are not only using AI to support decisions but are also leveraging predictive models to anticipate needs and automate more complex processes. In insurance and government, predictive models enable proactive risk management, fraud detection, and resource allocation.
“Being proactive with data insights transforms an organization’s ability to manage risks and allocate resources,” says Gina Raimondo, U.S. Secretary of Commerce. “For government agencies, AI-driven predictions can make public services more efficient and responsive.”
Guidance for Leaders:
- Current State Assessment: Identify areas where proactive automation could reduce risk or improve efficiency (e.g., fraud detection in insurance, patient monitoring in healthcare).
- Tailored Planning: Develop machine learning models that predict and respond to changing conditions, allowing for real-time adjustments.
- Roadmap Forward: Build a data governance framework to ensure that all predictive models comply with regulations, particularly around data privacy, and integrate seamlessly with existing systems.
Stage 5: Optimized – Fully Autonomous, Adaptive AI Systems
In the final stage of maturity, organizations operate with fully autonomous, adaptive AI systems that can dynamically adjust based on new data inputs. This level of maturity is ambitious and currently achievable only by a few, but it represents the ultimate goal for forward-thinking organizations.
In healthcare delivery, fully autonomous systems could revolutionize patient care by continuously adapting treatment protocols based on patient data. Karen Lynch, CEO of CVS Health, says, “We’re moving toward a future where AI-driven systems support continuous care, adapting to each patient’s needs in real time.”
Guidance for Leaders:
- Current State Assessment: Assess whether your current AI systems are capable of handling autonomous decision-making or if they require significant adaptation.
- Tailored Planning: Plan for a phased implementation of autonomous systems, starting with lower-risk areas, while monitoring system adaptability and accuracy.
- Roadmap Forward: Establish ongoing monitoring and governance to ensure autonomous systems remain safe, compliant, and aligned with organizational goals.
Conducting a Current State Assessment
Before embarking on any transformation journey, a comprehensive current state assessment is essential. This assessment should focus on:
- Process Mapping: Identify key workflows and their current automation levels.
- Data Maturity: Determine the availability, quality, and accessibility of data that AI models will require.
- Organizational Readiness: Assess whether teams have the skills and willingness to adopt AI-driven changes.
Healthcare Example: For healthcare organizations, assess data quality across EHR systems, understanding that fragmented data can hinder AI performance. “Data is the backbone of intelligent automation in healthcare,” says John Glaser, Executive-in-Residence at Harvard Medical School. “Without reliable data, AI systems can’t deliver actionable insights.”
Tailored Transformation Planning and Road mapping
Each stage of the maturity model requires a tailored transformation plan. This plan should outline:
- Short-Term Goals: Focus on immediate, achievable wins.
- Mid-Term Milestones: Build a bridge to more complex AI solutions.
- Long-Term Vision: Establish a vision for fully autonomous, adaptive systems.
Insurance Example: An insurance firm at the “Managed” stage might aim for short-term wins in automated claims processing, mid-term milestones in predictive fraud detection, and a long-term vision for adaptive underwriting systems. “Insurance companies must approach automation incrementally, building trust with each step forward,” remarks Mike LaRocco, CEO of State Auto Insurance.
The Road Ahead: Embracing the Future of AI-Driven Automation
The journey through the AI-driven intelligent automation maturity model is both ambitious and achievable. Each step offers opportunities to refine processes, boost efficiencies, and ultimately reimagine service delivery. For technology and business leaders, the path forward is a balance between visionary goals and practical constraints.
As Michael Kratsios, former U.S. CTO, aptly puts it, “The future belongs to those who can adapt rapidly, leverage AI responsibly, and transform thoughtfully.” By understanding their current state, defining a clear transformation plan, and setting realistic goals, organizations in regulated industries like healthcare, insurance, and government can unlock the full potential of intelligent automation.
Final Thoughts for Leaders: Embrace each stage of maturity as a milestone on the journey to true innovation. The rewards of intelligent automation are profound, offering not only operational benefits but also a transformative edge in a competitive world.
Contributor’s Summary:
Kishore Dharanikota is a seasoned Technology and Enterprise Architecture Strategy Consultant specializing in digital transformation and intelligent automation for Healthcare Delivery, Insurance, and Government agencies. With expertise in aligning technology and enterprise architecture with strategic goals, Kishore empowers organizations to navigate complex challenges, enhance operational efficiency, and drive sustainable innovation. Connect with Kishore on LinkedIn to learn more about his insights and career dedicated to optimizing enterprise architectures for long-term growth and compliance.