AI agency: from assistants to autonomous agents
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Artificial Intelligence AI-driven Business Process Automation
Support humans in critical processes and decision-making with AI.
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.
Boost efficiency and adaptability with AI orchestration
Integrate AI into end-to-end business processes and orchestrate multiple systems, humans, and automation technologies. Flowable integrates AI seamlessly into orchestration, enabling features such as predictive analytics and real-time decision-making support. AI optimizes task assignments and supports third-party sentiment analysis, managing requests and triggering flows. This ensures processes are efficient and adaptive.
Empower decision-making with human-assisted AI
In real-world scenarios, business processes are complex and dynamic. Flowable's dynamic case management empowers organizations to effectively handle unpredictable processes that require human decision-making. 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.
Accelerate process development with AI assistance
With a clear understanding of Generative AI's strengths and limitations, Flowable offers a suite of AI capabilities tailored for both developers and business teams engaged in business processes. Modelers can leverage Flowable’s contextual model generation for faster and more efficient model creation. Business teams can benefit from Flowable's AI-assisted document summarization, translation, sentiment analysis, and contextual queries, enhancing productivity and decision-making.
Unify technologies for optimal efficiency
Flowable’s flexible architecture empowers enterprises to integrate seamlessly with multiple technologies, including RPA, AI, chat applications, and enterprise systems such as Salesforce and SAP. This integration streamlines operations and boosts productivity by providing a single source of truth. Flowable connects effortlessly with various systems and data sources through Open API, OData, and an extensive range of out-of-the-box connectors, ensuring a unified and efficient workflow.
Maximize insights with advanced data aggregation
Flowable's process automation generates vast amounts of data, which it efficiently aggregates from various systems. This rich dataset can be utilized for multiple purposes, including training machine learning (ML) models and powering other AI initiatives. By leveraging this data, businesses can predict trends, automate decision-making, and significantly 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
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.
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