Feature
Businesses have been turning to automation to smooth workflows and perform repetitive tasks for years now. Intelligent automation takes it one step further. By combining automation tools with artificial intelligence (AI), intelligent automation delivers the same efficiency, but crucially, also has the ability to adapt to changing business circumstances.
Recent research conducted by Deep Analysis, with support from Hyland, sheds light on how some enterprises are successfully adopting intelligent automation and identifies the hurdles preventing other companies from doing the same.
The Rise of Intelligent Automation
The report, titled "Market Momentum Index: Intelligent Automation, Artificial Intelligence and Data," based its findings on a survey of 400 enterprises in the UK and the US with annual revenues of over $10 million. What researchers Alan Pelz-Sharpe and Matt Mullen found was that 88% of enterprises are either actively planning or plan to start their intelligent automation initiatives within the next six months.
The report highlights the growing momentum behind automation-driven transformation and identifies some of the drivers pushing organizations towards intelligent automation, including:
- Data quality and access: 63% of respondents indicated improving data quality as a key driver, while 58% emphasized better access to knowledge and data.
- Complex process automation: 77% of surveyed organizations directed their automation efforts toward automating their most complex processes.
- AI integration: 83% of respondents have AI projects in production or evaluation, with 84% of these projects now deployed in day-to-day operations.
- Enterprise system overhaul: 74% of respondents expressed willingness to replace their existing ERP system with an AI-enabled alternative, while 82% would consider replacing their ECM/document management system.
However, despite the enthusiasm for intelligent automation, data quality remains a significant hurdle. The survey found that 83% of respondents had to exclude at least one data source from their automation projects due to poor data quality. Furthermore, 59% faced issues across multiple systems, and 15% encountered problems with many of their data sources. Organizations are increasingly focused on data fitness and quality to ensure seamless AI integration.
Intelligent Automation Upends Traditional Workplace Technology
As intelligent automation gains traction, its impact on enterprise application ecosystems becomes more pronounced. The study found organizations are reevaluating their existing technology stacks, with nearly half planning to replace their ERP systems within the next three years. This would be a significant shift and reflects AI-powered solutions' role in shaping the future of the digital workplace.
It’s clear that intelligent automation isn't a passing trend. Miro head of product, AI Tony Beltramelli told us that for him, it's the next logical step in the evolution of traditional software-based automation into something much more dynamic and non-deterministic.
While robotic process automation (RPA) focuses on automating repetitive, rule-based tasks with a deterministic static workflow — things like data entry or simple workflows — intelligent automation (IA) brings cognitive capabilities into the mix through the integration with large language models, he said.
These AI agents of the IA workflow can make decisions, criticize and improve each other’s work, and complete a workflow in a more “organic” non-deterministic way. The deterministic vs. non-deterministic nature of workflow automations is the main difference. An RPA workflow is “deterministic”, meaning it follows clearly defined steps to execute a task in the same way every time.
By contrast, Beltramelli said, an IA workflow works towards predefined tasks and goals, but the system can execute the workflow in the way they deem most efficient at any point in time.
“IA is not just about following a script but adapting to complexity and context,” he said. “The real power of IA lies in its ability to extend beyond ‘if this, then that" logic. It turns processes into intelligent workflows that can adapt, optimize and even surprise you with creative solutions, a key differentiator when the goal isn’t just efficiency, but innovation."
Adoption Hurdles
At the heart of this is data. High-quality, diverse and unbiased data ensures that the algorithms driving IA can perform reliably and fairly. It’s not just about volume, but precision — having the right data for the right task.
Implementing IA is less about technology and more about mindset, trust and systems integration. Change management is critical here to allay people's fears and concerns around disruption or job displacement. Educating teams about how IA complements their work, rather than replaces it, is critical.
Finally, integrating IA into legacy systems can be daunting. These systems were rarely built with adaptability in mind, so businesses must navigate compatibility challenges. Yet this flexibility is one of their key advantages, Alan Jacobson, chief data and analytics officer at Alteryx told Reworked. The flexibility makes these systems less brittle to changes which could trip up traditional RPA.
Another challenge lies in the poor data quality found in many organizations. Two key pieces of data impact the results of intelligent automation solutions, Jacobson said. Many LLMs leverage data from the world wide web, including social media, blogs, new sites and a myriad of other sources. The reliance on such data can of course introduce errors or inappropriate responses in some use cases. The second type of critical data is the data feeding the process to be automated.
Jacobson views the new use cases IA opens up in comparison with traditional RPA as another key advantage, but he warns that involves a learning curve before you can determine which use cases are effective and which are not. Few team members, he points out, have experience working with unstructured data or understanding the techniques used by language models.
As a result, businesses often struggle with the types of errors and inaccuracies intelligent automation can produce. While there are methods to mitigate and detect these issues, developing a reliable and repeatable system with this technology remains an evolving field.
Don't Forget Responsible AI
Given the widespread public skepticism towards AI, business leaders must take a responsible approach to deploying intelligent automation, ABBYY director of AI strategy Max Vermeir said. Concerns often include bias and fairness, privacy and security, opaque decision-making and potential job losses. Companies must proactively address these risks and ensure compliance with ethical standards to build trust, he said.
While legislation will help regulate AI, businesses shouldn't wait for laws to determine ethical AI practices, Vermeir continued. Instead, they can follow emerging AI standards and implement responsible governance throughout the AI lifecycle. For instance, the National Institute of Standards and Technology (NIST) has introduced an AI Risk Management Framework, while ISO and IEC have developed ISO/IEC 23894, a global standard for AI risk management.
Trustworthy AI relies heavily on data integrity, he added. Generative AI, in particular, has raised concerns due to its reliance on large datasets, which can lead to inaccuracies or "hallucinations." A more reliable alternative is adopting Small Language Models (SLMs), which are designed for specific business needs and help mitigate compliance risks while delivering more accurate outcomes.
Ultimately, ensuring ethical AI requires a combination of best practices, risk management frameworks, voluntary AI ethics codes and well-targeted regulations to address key challenges, Vermeir said.
Any organization considering an investment in intelligent automation should start with a clear, data-driven approach, he continued. Ignore the AI hype and focus on real business needs. The first step involves understanding your current processes and workflows. Technologies like process intelligence can provide real-time insights into inefficiencies, helping business leaders pinpoint areas where IA could be used.
A strategic, informed approach will help organizations maximize the benefits of intelligent automation while ensuring efficiency, compliance and long-term success.
The Future of Intelligent Automation
Intelligent automation is evolving through AI specialization, multi-agent ecosystems and multimodal AI. Miro's Beltramelli predicts a shift toward industry-specific AI models and multi-agent systems that collaborate across platforms, enhancing automation efficiency. Alteryx's Jacobson foresees AI agents streamlining tasks like scheduling, travel booking and data analysis.
Multimodal AI, integrating text, vision and audio, will revolutionize sectors like finance by enabling AI-powered advisors to deliver personalized insights. Beltramelli advises organizations to start small but focus deeply on high-impact workflows, leveraging custom AI models as a key differentiator. “Successful adoption requires strong leadership, clear goals and workforce upskilling, ensuring businesses gain a competitive edge in the AI-driven future,” he concluded.
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Read up on other automation trends:
- What GenAI and LLMs Bring to Legacy Tech — You don't have to discard your legacy tech stack to modernize your company. Instead, try integrating GenAI and LLM to gain an upper hand.
- Market Trends – Predictions for 2025 — Legacy ERP systems will feel the heat, agentic AI's definition will be up for grabs and more predictions for the year ahead.
- Intelligent Process Automation Is Here - Where Are You? — Intelligent process automation is emerging as a digital workplace enabler by pushing the boundaries of RPA to unlock major productivity benefits.