What is AI business process automation?

AI business process automation (AI BPA) orchestrates an entire business process, including its rules, data, systems, and people, with AI handling specific tasks like classification, prediction, or content generation. It differs from “workflow automation with AI bolted on,” which adds AI to an existing tool; AI BPA puts orchestration first and treats AI as one governed component. This determines whether AI decisions leave an audit trail, and whether auditability is built in from the start.

How is AI business process automation different from traditional workflow automation?

Traditional workflow automation moves data between systems based on predefined rules, with no judgment involved. AI business process automation introduces AI at controlled points, such as document classification, prediction, or content generation, with human checkpoints and audit trails around each step. This extends what can be automated without weakening governance.

What processes benefit most from AI automation?

High-value processes with judgment-heavy decisions and audit obligations benefit most. Examples include insurance claims, onboarding, procurement, and exception handling, where errors are costly and auditability matters as much as speed. AI accelerates classification and triage while the orchestration layer records what happened and why. See the use cases above for more detail.

How does Flowable keep AI decisions auditable?

In Flowable, every AI task is a distinct process step, not a hidden call. The orchestration layer logs what the AI did, what data it used, what it decided, and who approved it, creating an audit trail for every decision. That auditability makes the decision defensible on review.

What role does human review play in AI-driven processes?

Human-in-the-loop review is intentional, not a fallback. High-stakes processes need judgment at specific points, so the orchestration layer makes those checkpoints first-class steps captured in the audit trail. For example, claims above a set threshold always route to a human reviewer before payout.

Does Flowable support open standards like BPMN, CMMN and DMN?

Yes. Flowable supports BPMN, CMMN, and DMN, all open standards for modeling processes, cases, and decisions. This keeps processes portable and inspectable rather than locked into a proprietary format. Enterprises evaluating an orchestration platform should ask whether it’s built on open standards or a closed one.

AI Business Process Automation Built on Process Orchestration

Govern AI inside high-stakes business processes - with auditability, human checkpoints and open standards built in.

50%

of enterprises will orchestrate AI by 2025.

2028

50% of organizations will use AI instead of time-consuming bottom-up forecasting.

70%

of enterprises will have operationalized AI by 2025.

What is AI-driven automation? What is intelligent automation?

AI-driven automation refers to the use of artificial intelligence (AI) in automation tasks and processes. AI tasks need to be orchestrated and choreographed as components of your business processes – just like any other traditional executable service or human task. The adoption of AI for task automation enables organizations to reallocate specialist staff resources to more strategic, specialist or creative endeavors, helping drive innovation and competitive advantage in today's rapidly evolving digital landscape.

Intelligent automation (IA) refers to the use of process automation technologies, such as artificial intelligence (AI) and machine learning (ML) in combination with business process management (BPM) to streamline and scale decision-making across organizations.

Therefore, intelligent automation embeds AI-driven automation. When it comes to automation, artificial intelligence can provide great support in many areas for both business teams and IT teams, ultimately supporting the overall automation goals of the enterprise.

The benefits of AI in automation

Organizations that successfully implement AI-based tasks can yield a wide range of benefits. A few of the main ones being:

Sustainable competitive advantage

AI-driven improvements in customer experience can lead to reduced costs, increased market share, and an improved market position and competitiveness through exploiting AI-driven decisioning and AI-generated insights.

New business models

Harnessing the transformative potential of AI to explore innovative approaches, tap into new revenue streams, and adapt swiftly to evolving market demands.

Increase in productivity

Leveraging AI to improve workflows, increase straight through processing, optimize resource allocation, and empower employees to focus on strategic initiatives, driving higher efficiency and output.

AI with Flowable

At Flowable, we provide full flexibility in choosing when and where to integrate AI. While we recognize AI's potential in automation, we understand it may not be suitable for all use cases. The decision to use AI should be based on the tangible benefits it offers to organizations according to its maturity level.

Orchestrate AI inside your business processes

Process orchestration is the layer that makes AI safe to run at scale. AI tasks are components of the process, not its centre of gravity. Guardrails matter, but process control is what actually makes it work.

blog_Empowering human decision-making: The incremental approach to AI in business_keyvisual

Build human judgement by design

In real-world scenarios, business processes are dynamic. Flowable's case management capabilities empower organizations to effectively handle unpredictable processes that require human decision-making by design. Use AI to support decision-making with securely stored data at any given point. Integrating AI into human workflows fosters adaptability, enabling real-time decisions and seamless collaboration.

blog_real-life ai applications with flowable

Built on open standards: BPMN, CMMN, DMN

Enterprises evaluating an AI process orchestration platform should ask whether it's built on open standards. Flowable supports BPMN, CMMN and DMN, meaning your processes, cases and decisions are portable, inspectable and not locked in.

Unify technologies for optimal efficiency

Flowable's flexible architecture empowers enterprises to integrate seamlessly with RPA, AI, chat applications, and enterprise systems, streamlining operations and boosting productivity through a single source of truth. Open API support and a broad range of out-of-the-box connectors ensure fast, unified connectivity across your existing systems and data sources.

AI that learns from your process data

Flowable's contextual model generation helps modelers create process models faster, drawing on AI to speed up and simplify development work. Beyond modeling, Flowable's process automation generates vast amounts of data, which it efficiently aggregates from various systems. This rich dataset can be used to train machine learning (ML) models and power other AI initiatives, helping businesses predict trends, automate decision-making, and enhance operational efficiency.

Generative AI, predictive AI and adaptive AI

Since its launch in 2022, ChatGPT and generative AI have been increasing in usefulness and capability. However, the AI landscape extends far beyond generative AI. Let's explore three distinct branches of AI that can significantly contribute to successful automation.

Generative AI

According to Forrester, 89% of AI decision-makers say their organization is expanding, experimenting with, or exploring the use of generative AI.

Generative AI refers to artificial intelligence systems designed to create new content, whether it be text, images, audio, or even complex data structures. These systems use language-based machine learning models, particularly generative adversarial networks (GANs) and transformer-based models like GPT, to produce content that mimics human creativity and cognition. Applications of GenAI include chatbots for system interactions, contextual email generation, art creation, music composition, coding, and more, providing powerful tools for innovation and automation in various creative industries.

By learning from vast amounts of data, generative AI can generate realistic and original outputs, pushing the boundaries of what machines can achieve in mimicking human-like creativity. Generative AI can significantly enhance automation in various sectors.

Example of GenAI for business teams:

Customer support

GenAI can be used to develop intelligent data-access chatbots and virtual assistants to handle customer inquiries, resolve issues, and provide personalized financial advice within certain constraints. This reduces the workload on human agents, ensures 24/7 customer support, and improves customer satisfaction through instant responses.

Loan processing and underwriting

Automating the evaluation and approval process for loans by analyzing applicants' financial data, credit history, and risk factors. This speeds up the loan approval process, reduces operational costs, and minimizes human error.

Example of GenAI for IT teams:

Code and model generation

Generative AI can assist in writing code snippets, scripts, or even entire modules, significantly speeding up the development process.

Software development lifecycle

Generative AI can provide test case generation and create documentation and APIs for services.

Predictive AI

Predictive AI, with its ability to forecast future events or outcomes, is beneficial for businesses that rely on data-driven decision-making. Industries such as finance, e-commerce, healthcare, and marketing can leverage predictive AI to optimize operational decisions, identify trends, and make accurate predictions.

Predictive AI encompasses algorithms and models that analyze historical data to make forecasts about future events or trends. This type of AI utilizes techniques such as machine learning, statistical modeling, predictive analytics and data mining to identify patterns and relationships within datasets.

By providing insights into potential future outcomes, predictive AI aids decision-making processes, enabling organizations to anticipate changes and strategically plan their actions to mitigate risks or capitalize on opportunities.

Example of predictive AI for business teams:

Risk management

With predictive AI enterprises can analyze transaction patterns and customer behavior to detect fraudulent activities, aiding in real-time fraud prevention and reducing losses.

Customer lifetime value prediction

With predictive AI, enterprises can better estimate the lifetime value of customers, assists in identifying high-value clients, enabling personalized customer service strategies for enhanced retention and profitability.

Example of predictive AI for IT teams:

User behavior analytics

Predictive AI can detect anomalies in user interactions with automated systems, allowing for early identification of potential security breaches or misuse, thereby ensuring the integrity and security of automated processes.

Incident management and resolution

Predictive models can forecast the likelihood of incidents in automated processes based on historical data, empowering proactive response strategies to minimize disruptions and streamline incident resolution for optimized automation performance.

Adaptive AI

According to Gartner, adaptive AI systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise. These systems aim to continuously learn based on new data at runtime to adapt more quickly to changes in real-world circumstances.” (Gartner: Gartner glossary, Adaptive AI.)

Adaptive AI focuses on enhancing human capabilities by providing decision support or adapting to individual needs and contexts. This includes technologies like virtual assistants, recommendation systems, and adaptive learning platforms that personalize educational content based on student performance. Adaptive AI aims to improve accessibility, productivity, and personalization in various domains.

Example of adaptive AI for business teams:

Hyper-personalized services

Adaptive AI can create hyper-personalized services like content and product recommendations by using real-time customer data.

Customer service

Adaptive AI can improve customer service chatbots by allowing them to learn from interactions, improving their response accuracy and effectiveness.

Example of adaptive AI for IT teams:

Smart task automation and workflow optimization

With assistive AI IT teams can automate routine tasks and optimize workflows. For instance, an AI-driven virtual assistant can help manage repetitive administrative tasks such as scheduling maintenance windows, sending reminders, or generating status reports.

Identifying the biggest AI risks

While AI offers many potential benefits, enterprises also face significant risks associated with its adoption. McKinsey research has highlighted the primary AI-related risks that enterprises find most relevant. These include:

Privacy and data

AI systems can pose significant privacy risks by collecting, storing, and analyzing vast amounts of personal data without sufficient safeguards or user consent.

Security & accuracy

Inadequately secured AI training processes can be vulnerable to adversarial attacks, leading to compromised model accuracy and reliability.

Fairness

The deployment of AI systems on biased infrastructure can perpetuate and exacerbate existing inequalities, resulting in unfair outcomes for marginalized groups.

Transparency and explainability

AI systems often lack transparency and explainability, making it difficult for users and stakeholders to understand and trust the decision-making processes.

Safety and performance

Ensuring the safety and performance of AI systems is crucial, as errors or malfunctions can lead to significant harm and unintended consequences.

Costs

Developing, deploying, and maintaining AI systems can be expensive, potentially limiting access to only well-funded organizations and exacerbating digital divides.

The biggest challenges when adopting AI

McKinsey highlights the biggest challenges to AI adoption for both, AI high performers and others.

Despite their advanced capabilities, AI high performers still encounter significant challenges in capturing value from AI, which tend to be related to their advanced stage of AI maturity.

These high performers often struggle with operational issues like monitoring and retraining models, whereas others face more basic strategic challenges, such as defining a clear AI vision tied to business value or securing adequate resources. The findings indicate that even top performers have not fully mastered AI adoption best practices, such as MLOps, although they are closer to achieving this compared to others.

Further AI reads