Implementing AI in your organization is no longer a matter of if but when and how. AI has established itself as the must-have technology for achieving unprecedented levels of efficiency, and companies that fail to adopt and integrate it properly risk falling behind.
According to Gartner, by 2027, AI-driven innovation teams are expected to achieve project success rates up to 75% higher than traditional human teams, leading to faster value creation from applied innovations.
Our report, State of Process Orchestration and Automation in 2025, found that the majority of companies already use AI and plan to scale it in the coming years. Specifically,
- 88% of companies are looking to leverage AI to enhance their ability to analyze and improve processes, and
- 84% of organizations that already use AI are planning to expand their AI capabilities within the next three years.
In this blog post, we discuss why organizations need to operationalize AI to maintain control over it, ensuring compliance with regulations and long-term customer trust.
Why companies need to prioritize controlled, secure, and operationalized use of AI
With the surge in interest and proliferating use cases of AI in nearly all industries, it can be challenging to keep it under control, even in the most mature organizations. A Gartner report, Harness the Power of Process, AI and Data Orchestrators via Platform Software, highlights that the increasing use of AI and automation in business processes creates a greater need for orchestrating data, systems, and human and machine tasks, which now require more precise sequencing.
To achieve business goals, organizations must focus on not just adding new AI tools to their tech stack, but on aligning AI initiatives with overall business strategies, or in other words, operationalizing AI.
According to research by TSIA, “this alignment ensures that AI tools are implemented and integrated in ways that enhance business operations and drive innovation.” They go on to add that “technology leaders need to monitor and tweak AI systems regularly, develop a feedback loop, and educate teams about AI,” all of which is facilitated by the operationalization of AI.
Without properly operationalized AI, companies experience many challenges
While AI boosts organizational efficiency, it also brings increased complexity and compliance challenges as companies need to prepare for stricter regulations. A notable example is the EU Artificial Intelligence Act, a comprehensive framework designed to ensure the safe and ethical use of AI across industries. This act enforces strict requirements for transparency, accountability, and risk management, which can complicate compliance efforts for organizations.
The State of Process Orchestration and Automation 2025 report highlights organizations’ growing concerns around AI, transparency, and compliance:
- 84% of survey respondents report that a lack of transparency in AI applications and services leads to regulatory compliance issues.
- 93% recognize the need to orchestrate AI applications and services across their business processes.
- 90% agree that AI applications must be managed like any other endpoint within automated business processes to ensure regulatory compliance.
An additional challenge posed by AI is how it contributes to rising technical debt. Accenture’s report, Build your tech and balance your debt, stressed the fact that AI is a significant driver of this increase, with generative AI and enterprise applications being the primary contributors. A key factor is point solutions that appear to help by quickly applying AI to a single task but end up creating a value trap, ultimately making it harder to improve or even maintain a complete process from end to end.
Legal and ethical considerations of AI
Another common concern around AI is its legal and ethical implications. When AI is not properly operationalized, companies can face significant challenges.
Ensuring responsible use in business processes is crucial for many reasons. Data security is a key aspect, vital for both compliance and maintaining customer trust, as AI systems often handle sensitive information. Misuse can lead to legal and copyright issues, especially when AI generates content or uses data that may infringe on intellectual property rights.
AI systems can also produce inaccurate or misleading information, known as hallucinations, leading to incorrect decisions. Bias in AI models is another significant concern, as it can perpetuate existing prejudices and result in unfair outcomes. Additionally, immature AI models may not perform reliably, posing risks and inefficiencies.
Organizations must prioritize these considerations to ensure the responsible use of AI in business processes. Implementing robust encryption, access controls, and regular security audits is essential to protect sensitive data and comply with regulations like GDPR.
How can organizations implement AI with clear intent and purpose?
To achieve the full potential of AI and overcome associated challenges, effective integration through end-to-end process orchestration is crucial.
When integrating AI, it’s important to consider its potential to disrupt current workflows. To minimize these disruptions, we recommend a gradual approach of introducing AI solutions. To ensure a smooth transition and minimize resistance, it’s important to have strong change management practices and provide employees with proper training. Getting stakeholders involved and addressing their concerns can also make the integration process more successful.
Having clear internal guidelines and compliance frameworks is crucial for upholding regulatory standards and ethical practices. Additionally, human oversight in monitoring AI decisions provides an extra layer of accountability, ensuring that AI operates within set boundaries and aligns with your organization’s values.
Gain full visibility of processes in a centralized platform
Since one of the biggest concerns around AI is transparency, having a comprehensive overview is crucial for monitoring, optimizing, and safeguarding processes. A strong process orchestration tool is essential for connecting endpoints and giving full visibility into workflows. An enterprise-wide platform can bring together relevant data from different sources into one centralized location, ensuring accuracy and reliability for AI models while reducing errors.
Lead with unified modeling
To stay ahead of the competition, businesses need to increase productivity in both their IT and business departments. This starts with implementing a unified modeling language and utilizing sophisticated tools, such as Modeler’s Copilot and AI agents. These tools facilitate seamless end-to-end process modeling and expedite workflows through AI-assisted Form Builders. With AI-powered search functionalities, companies can quickly retrieve the necessary information, saving valuable hours or even days that would otherwise be spent searching.
Unify AI efforts for comprehensive integration
For a future-proof, scalable AI strategy, organizations need to shift from fragmented AI implementations to a unified, comprehensive approach.
The goal is to break down silos and seamlessly incorporate AI services and machine learning models into their operational processes. This integration enables swift adaptation to changing customer demands, market dynamics, and regulatory landscapes.
Utilize pre-built AI connectors
To accelerate their AI initiatives, companies can leverage prebuilt AI connectors to common AI systems, such as those provided by OpenAI and Hugging Face. These connectors, along with AI-enabled blueprints, offer a robust foundation for AI-driven processes. Businesses can also develop custom connectors and blueprints tailored to their specific requirements, which they can reuse across the organization.
Strategic AI implementation for business success
AI can help companies achieve incredible efficiency, but it’s crucial to operationalize it effectively to maintain control, ensure compliance, and build customer trust. As AI usage grows, there’s a greater need to orchestrate processes, systems, and tasks. Without proper operationalization, AI can lead to increased complexity, technical debt, and compliance challenges.
Effective integration through end-to-end process orchestration is key. Transparency is vital for monitoring and optimizing processes. A strong process orchestration tool provides full visibility and consolidates data, ensuring AI accuracy. Unified modeling and AI-powered tools boost productivity and streamline workflows.
Operationalizing AI through strategic process orchestration is essential for achieving business goals, maintaining compliance, and driving innovation. By focusing on controlled, secure, and orchestrated AI use, organizations can harness AI’s full potential while mitigating risks and ensuring sustainable growth.
Additional resources
AI-enabled process orchestration
Why AI Agents Need Orchestration
Operationalize AI by Blending Deterministic and Non-Deterministic Process Orchestration
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