AI Automation: Carving New Paths to Cost Savings, Innovation, and Competitive Advantage

AI automation involves leveraging artificial intelligence (AI) to automate repetitive tasks within operational workflows, thereby enhancing efficiency and productivity. This not only streamlines routine tasks within core business processes but also enables companies to scale growth efficiently by integrating a broad range of digital tools such as robotic process automation, APIs, and software robots.

However, that kind of automation orchestration doesn't just stop at tech integration—it also means creating a mixed-autonomy scenario in which technology, data, and human workers complement each other to optimize business outcomes. Additionally, it means integrating intelligent algorithms and machine learning into routine workflows seamlessly, fundamentally reshaping how work gets done across the enterprise value chain.

Fueled by machine learning, AI replicates human cognitive processes, adapting and improving over time. This is where AI automation comes into play, infusing workflows with advanced cognitive capabilities. Traditional automation efficiently executes predefined tasks, while AI automation allows machines to think, learn, and make decisions autonomously (with human oversight).

Mixed-autonomy AI automation overview.
 

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AI automation is already affecting a wide array of industries, transforming business processes, altering the nature of work, and laying the foundation for a significant acceleration in business productivity. A Deloitte study recently found that over 50% of organizations planned on incorporating the use of AI and automation technologies in 2023. What’s more, AI software revenue is expected to surge to $307 billion by 2027, highlighting the race to carve new routes to innovation, efficiency, and competitive advantage.

Having established the transformative impact of AI automation on industries and business operations, let’s delve into its core components. The following sections will explore: 

• The four elements of AI automation.

• How it creates next-level productivity.

• Tips to actualize that productivity in your own processes.

• The benefits you’ll reap from using automation in AI.

• Trends to watch in AI automation.

Let’s dive in.

AI automation overview.

Harnessing the speed, power, and scalability of AI automation allows large organizations to sharpen their focus on value-added work by entrusting routine tasks to intelligent machines and software. For example, AI process automation streamlines document classification, routing and filtering for purchase orders, emails, invoices, and more, while ensuring documents and  information get routed to the right people or teams in a fraction of the time it would take a human. 

AI process automation platforms are table stakes for organizations seeking to stay ahead of the innovation curve. These platforms offer the speed and flexibility of low-code application development and also harness the transformative power of AI to address the complex challenges faced by data-intensive industries like banking and financial services. For example:

Predictive AI: One of the key applications of AI in the financial sector is predictive AI, which leverages advanced algorithms to analyze market data and provide valuable insights for investment decisions. By identifying patterns and trends in historical data, predictive AI enables financial institutions to furnish investment recommendations or autonomously manage investments based on projected market trajectories. This capability not only enhances the efficiency of investment strategies but also empowers institutions to capitalize on emerging opportunities in dynamic market environments.

Anomaly Detection AI: Amid the rise of sophisticated financial fraud, anomaly detection AI is a critical tool for banks and financial institutions to safeguard their operations. This type of AI uses machine learning techniques to detect deviations from normal patterns in transactional data, enabling early detection of suspicious activities such as money laundering and fraud. By serving as an alert system and signaling when human oversight is required, anomaly detection AI enhances the effectiveness of anti-money laundering (AML) and fraud detection, bolstering the integrity of financial systems.

Classification AI: Customer segmentation and risk assessment are pivotal for banks in tailoring their services and managing loan portfolios efficiently. Classification AI leverages sophisticated algorithms to analyze customer data and categorize individuals into distinct segments based on their transaction history, preferences, and behavior patterns. Moreover, classification AI helps banks do a better job of evaluating loan applications and categorizing risk by assessing factors such as credit history, income levels, and debt-to-income ratios.

AI and machine learning models classify documents and extract data from them.
 

The best AI automation platforms use state-of-the-art large language models (LLMs) to transform user interactions. For example, leading platforms offer plug-ins to quickly generate natural language answers to customers via chatbots, boosting customer engagement and reducing burdens on customer service staff.

[ Learn the differences of large language models vs. generative AI. ]

If you want to actually unlock next-level productivity, keep these three tips in mind.

Rapid growth based on a piecemeal approach to AI automation leads to islands of automation that defeat the point of adopting automation technology in the first place. Instead, take a strategic approach to avert the following problems:

• Higher IT maintenance costs.

• Gaps in skilled resources to manage various automation tools.

• Complex integrations to connect different automation technologies.

• Technology upgrades that conflict with each other.

• Overlapping tools and misaligned governance.

AI automation is crucial for orchestrating core processes and implementing AI insights across the enterprise. However, not all implementation strategies are created equal. 

Unlike a platform approach, isolated task automation—such as via a combination of robotic process automation (RPA), AI, and other digital technologies—hinder your ability to effectively manage complex interactions across customer and employee journeys. Taking a strategic approach, supported by a robust data management architecture like a data fabric, enhances collaboration between the digital tools in your tech stack.  

AI models are trained on massive amounts of data. However, to make your data accessible to the AI, you need a strong semantic layer (one unified place to see and interact with your data). Without a semantic layer (like a data fabric), you won’t be able to create valuable AI models and get the most out of your AI investment. A data fabric is an architecture layer that connects data across the enterprise, providing a managed data pipeline to feed your AI models.

Your AI model outputs can then be incorporated into complex workflows and systems of record via process automation to boost operational efficiency and create seamless customer experiences. The more data you feed an AI (and the better that data is), the better the AI’s output will be. (And a private AI strategy ensures you can do this safely.) 

Appian provides multiple ways to use AI capabilities in your business applications.
 

[Learn how to reap the benefits of end-to-end process automation. Download the Process Automation Guide.]

1. Process optimization: Pairing AI and automation optimizes business processes by leveraging AI and machine learning technologies to enhance business process management, align teams with organizational strategies, and achieve operational goals. It includes everything from data analysis and automation of repetitive processes to aiding team members in making informed decisions by providing the right information at the right time. The overarching objective is to minimize errors, boost productivity, and elevate operational efficiency. 

 2. Operational resilience: AI automation unlocks your ability to anticipate and adapt to evolving circumstances and trends. Moreover, AI automation simplifies the smooth integration of cutting-edge technologies, allowing large organizations to swiftly respond to the insights provided by AI and adapt their operational processes to overcome the challenges of uncertainty and change.

 3. Cost savings: AI use cases are paying dividends as companies embrace automation to bolster their financial stability. For example, a recent survey of 500 global financial services professionals revealed that 36% achieved double digit reductions in annual costs through AI applications.

4. Enhanced decision-making: Decision-makers need strong data to make informed decisions. AI assists by providing predictive recommendations based on patterns within a given data set. These actionable insights enable business leaders to navigate challenges, identify opportunities, and stay ahead of competitors. 

5. Next-gen innovation: AI automation platforms are catalysts for innovation across products and services. In the healthcare industry, AI-powered automation promises personalized treatment strategies, predictive forecasting of disease outbreaks, and elevated diagnostic accuracy. 

6. Personalized customer experience: AI customizes interactions and automates routine tasks. For instance, AI-powered conversational chatbots deliver adaptive customer support based on real-time interactions. Analyzing behavior, preferences, and purchase history, AI algorithms provide personalized product recommendations, enriching customer engagement. Leveraging historical data, AI helps companies anticipate customer needs and enhance loyalty.

AI automation generates a user interface directly from a PDF in just a few clicks.
 

[For expert advice on navigating the AI economy, download the 2024 AI Outlook.]

Aligning AI automation with strategic business objectives is a powerful way to optimize business outcomes. The following AI automation trends are poised to propel business innovation and operational efficiency to new heights:

1. AI + RPA synergy: Integrating AI with RPA is transformative, automating cognitive and repetitive tasks and streamlining processes while enhancing team workflow efficiency. Using a robot to mimic human behavior is not always the most efficient way to automate. RPA is specifically designed for automating high-volume, repetitive tasks. However, for tasks involving complex decision-making processes, such as classifying emails or predicting fraud, AI is more suitable. The synergy combining AI and RPA optimizes workflows across an entire organization rather than just creating islands of automation.

2. Transparent decision-making: A recent survey underscores concern about the trustworthiness of AI automation among business leaders. Over 60% of CEOs identified trustworthiness as a primary concern associated with AI automation, indicating a growing need for explainable AI and transparent algorithms to drive automated decision-making processes.

3. Advanced conversational AI: Natural language processing (NLP) is advancing, paving the way for more sophisticated conversational AI and virtual assistants. A strategic automation trend drives seamless interactions with customers and internal teams. 

As the digital economy evolves, embracing a strategic approach to AI automation that is supported by robust data management and a platform mindset will help organizations unlock the full potential of this transformative technology and carve new paths to cost savings, innovation, and competitive advantage.

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