The Path Forward for Intelligent Automation [2024 Trends]

Intelligent automation (IA) has fueled the technology revolution at an unprecedented level.

Businesses are increasingly utilizing IA solutions to accelerate their digital transformation journey. IA solutions offer limitless opportunities for businesses to use AI, GenAI, low-code process automation tools and other advanced automation tools in conjunction to achieve heightened impact.

This is not just limited to reduced operational costs, elimination of human errors and achieving accurate results to automation. Businesses are able to launch their products/services faster, accommodate increased volume of work without allocating more human capital, spend more time on decision making and innovation, and ultimately humanize their work environment for people.

An increasing number of businesses are keen to leverage the benefits offered by intelligent automation. For example, 80% of survey respondents from the retail sector expect their organizations to adopt IA by 2025. The same survey witnessed respondents from the Energy and Utilities sector reporting a 45% increase in customer leads after IA adoption.

Businesses are eagerly monitoring IA trends to anticipate technological advancements and changing consumer preferences.

Nividous outlines the top 5 key trends to watch for in intelligent automation technologies in 2024 and beyond:

1. Adoption of Holistic IA Solutions: Businesses are increasingly turning to IA solutions that embrace a holistic approach to automation. This shift stems from the limitations observed with stand-alone automation tools, which often struggle to move beyond siloed automation.

   By integrating complementary technologies such as AI, RPA, and workflow automation into a unified platform, businesses can achieve greater efficiency gains and competitive advantage.

   This holistic approach enables end-to-end process automation without the need for hefty investments in third-party integrations. Read a detailed blog post on how intelligent automation is different from RPA here.

2. Rise of Low-Code Process Automation Tools: Legacy applications that are time-consuming to deploy, require extensive employee training, and depend heavily on IT support are becoming obsolete in today’s fast-paced environment.

   Businesses are now seeking low-code process automation tools that empower non-technical users to create and deploy automation solutions rapidly and efficiently. With intuitive drag-and-drop features, these tools enable automation deployment across various departments with ease.

3. Integration of GenAI with IA Tools: GenAI, with its capabilities in predictive analytics and resource optimization, is becoming increasingly integral to the intelligent automation landscape. For example, in supply chain management, GenAI can analyze vast amounts of data to forecast demand accurately, while automation tools streamline processes like inventory management and logistics.

   Similarly, in customer-facing operations, GenAI-powered chatbots can provide personalized responses, complemented by automation tools handling routine tasks such as account inquiries.

4. Surge in Demand for Cloud-Based IA Platforms: The demand for cloud-based intelligent automation platforms is soaring, driven by factors such as agility, cost-effectiveness, and scalability. According to Gartner, investment in public cloud services is projected to exceed $1 trillion by 2027.

   Cloud-based solutions offer centralized platforms for deploying and managing automation initiatives, facilitating collaboration and integration across distributed teams and systems.

5. Emphasis on Ethical Business Practices: Organizations are increasingly prioritizing responsible AI deployment and ethical considerations in automation decision-making. This trend reflects a growing awareness of the societal impacts of automation technologies, including concerns about bias, privacy, and job displacement.

   Businesses are investing in ethical AI frameworks, transparency measures, and governance mechanisms to mitigate risks and ensure responsible automation practices. By aligning automation initiatives with ethical principles, organizations can build trust with stakeholders and foster sustainable growth in the long run.

Click here to learn more about Intelligent Automation.

Intelligent Automation Technologies that will rule the Automation Space in 2024 and Beyond

2024 promises to be a landmark year for IA. Let’s look at some of the Intelligent Automation trends that can take automation technology to newer heights:

1. Generative AI

Generative AI is a branch of AI that produces human-like responses to prompts. It studies patterns from exhaustive datasets and deploys this knowledge to create new, realistic, and actionable data for a wide range of industries.

This technology utilizes deep learning algorithms to generate novel and realistic outputs based on patterns and examples in the training data. With applications in art, design, entertainment, and content creation, generative AI unlocks new avenues for innovation and expression, empowering individuals and businesses to explore creativity in unprecedented ways.

For example, in airport security, Generative AI simplifies traveler authentication by producing definitive images from captured photographs.

What the analyst says: Gen AI-based features will add up to $4.4 trillion annually to the global economy. In comparison, the UK’s GDP was $3.1 trillion in 2021. (McKinsey)^{1, 2}

What our expert says: Generative AI unlocks new techniques for performing existing automation tasks and also greatly expands the scope of tasks that can be automated. Despite these positive impacts, the greatest potential is in simplifying and expediting the creation and maintenance of automation.

2. Machine Learning (ML)

ML is a subfield of AI that focuses on developing algorithms and models capable of learning from data and making predictions or decisions without being explicitly programmed. By analyzing patterns and relationships within large datasets, ML algorithms can identify trends, classify information, and generate insights.

ML finds applications across diverse domains, such as image and speech recognition, recommendation systems, medical diagnosis, financial forecasting, and autonomous vehicles, driving innovation and efficiency in numerous industries.

What the analyst says: Top spheres for ML adoption are Business Analytics (33%), Security (25%), Sales & Marketing (16%), and Customer Service (10%). (A Statista survey taken by IT leaders)³

What our expert says: Machine learning will see massive leaps forward due to the capability of Generative AI to create and supplement datasets for model creation and retraining.

3. Natural Language Processing (NLP)

NLP is a branch of AI that focuses on the interaction between computers and human language. It involves the development of algorithms and models that enable computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.

NLP enables machines to comprehend and analyze text and speech data, allowing for tasks such as sentiment analysis, language translation, text summarization, and speech recognition. By leveraging techniques from linguistics, computer science, and machine learning, NLP plays a crucial role in various applications, including virtual assistants, chatbots, search engines, and language translation services.

What the analyst says: NLP will make UIs and GUIs obsolete, and this will make conversing with a machine as easy as conversing with a human. (Sisense)⁴

What our expert says: Not many people are aware that NLP has already become an integral part of our lives. Alexa, Siri and vehicular navigation systems are just a few examples of common NLP applications. However, there are still some barriers that prevent NLP from functioning at its best abilities. These include text with ambiguity, colloquialisms, sarcasm, synonyms and irony. With regular enhancements to NLP models, we are confident that these barriers can be swiftly broken down in the next few years.

4. Conversational AI

Conversational AI refers to AI technologies that enable natural, human-like interactions between computers and users through spoken or written language. It encompasses chatbots, virtual assistants, and voice-enabled devices capable of understanding and responding to user queries, commands, and requests.

Leveraging techniques from NLP, ML, and speech recognition, conversational AI systems can engage in meaningful conversations, provide assistance, and perform tasks autonomously. These systems are increasingly integrated into various applications, including customer service, healthcare, education, and smart home devices, to enhance user experiences and streamline interactions.

What the analyst says: Conversational AI deployments in contact centers will reduce labor costs by $80 billion. (Gartner. Inc.)⁵

What our expert says: Conversational AI has helped organizations use voice assistants to automate common queries and enhance customer interactions. There is still a long way to go because conversational AI tools struggle with understanding human sarcasm, emotions, and tone. We predict enhancements to Conversational AI models to become near-perfect in the next decade.

5. Hyperautomation

Hyperautomation is a strategic approach to automation that combines advanced technologies namely AI, ML, RPA, and low-code process automation tools to streamline and automate processes from start to finish.

The term was initially coined by Gartner, Inc., however it is now used interchangeably with Intelligent Automation, Digital Process Automation and Business Process Automation tools. Hyperautomation enables businesses to achieve greater efficiency, agility, and scalability while reducing operational costs and errors.

What the analyst says: Hyperautomation adoption is on the rise, with 34% of businesses having adopted hyperautomation to enhance employee productivity. (Analytics Insight)⁶

What our expert says: Hyperautomation has become a top priority for businesses. It helps them work faster and smarter. However, businesses find it difficult to find the right approach to adopt hyperautomation. Nividous recommends starting the automation journey by identifying low-hanging fruits – meaning identifying processes that are simple to automate and can deliver quick ROI. Automations then can be scaled up by introducing AI and other complementary technologies automating more tasks within the process and later expanding its use to the other departmental processes.

6. Intelligent Document Processing (IDP)

IDP uses NLP, ML and Optical Character Recognition (OCR) to analyze and extract data from varied document formats such as contracts, invoices, emails, and purchase orders and from various sources. By eliminating manual data entry and streamlining document processing workflows, IDP enhances efficiency, accuracy, and compliance across various industries.

What the analyst says: The global IDP market size was valued at $1.45 billion in 2022. It is expected to show growth at a CAGR of 30.1% from the period 2023-2030 (Grand View Research)⁷

What our expert says: As the technology driving IDP improves, we will see more use cases emerge outside of the realm of traditional paper/scanned documents. Unstructured data locked in all formats will eventually be targeted.

7. Augmented Intelligence

Unlike full automation, augmented intelligence focuses on leveraging AI algorithms and machine learning models to assist human decision-making processes, providing insights, recommendations, and predictive analytics.

By combining the strengths of both human expertise and AI capabilities, augmented intelligence enables more informed and efficient decision-making across various domains.

What the analyst says: The augmented intelligence market is expected to reach $121.5 billion by 2030. (Allied Market Research)⁸

What our expert says: Augmented intelligence is complex because it needs a thorough understanding of expert knowledge within a challenge (e.g. car design or surgery). There is a need for more advanced ML models and data structures to analyze human performance and accelerate augmented intelligence.

Why is Intelligent Automation Important?

Intelligent Automation (IA) holds significant importance in today’s dynamic business landscape, offering numerous opportunities for organizations to excel amidst uncertainty and complexity. Here are several reasons why IA is essential in shaping the future of business:

• The Pursuit of Customer Excellence: Organizations prioritize customer satisfaction through various products and strategic decisions. IA plays a pivotal role in transforming customer experiences, with technologies like chatbots managing thousands of queries daily. Natural Language Processing (NLP) aids in assessing the urgency of support tickets, ensuring timely responses.
• Data Accuracy: Many organizations face transactional errors due to manual processes. Automation enhances data accuracy, facilitating informed decision-making and driving operational efficiency to unprecedented levels.
• End-to-End Automation: While some organizations have automated certain processes, there’s a growing need to automate entire end-to-end workflows. IA enables this transition, delivering highly agile processes that surpass business expectations.
• Data Reconciliation: With data scattered across multiple systems and files, aligning information from these sources is laborious and error-prone. IA streamlines data integration by harnessing information from various sources, thereby managing complex processes more efficiently.
• Fraud Prevention: Fraud detection traditionally consumes substantial manpower and time. IA utilizes real-time analytics to monitor transactions comprehensively, detecting fraudulent activities with unparalleled accuracy.

Intelligent Automation not only addresses current business challenges but also equips organizations with the tools needed to thrive in an increasingly competitive landscape.

What is the next big thing in Automation?

Several AI technologies, including the Internet of Things (IoT), are poised to impact global businesses profoundly. Many companies opt for AI-IoT integration to collect and leverage large amounts of data in real-time, empowering more innovative systems across domains like smart cities, manufacturing, and agriculture.

Generative Adversarial Network (GAN) is another AI technology with huge potential. The technology possesses superior capabilities to generate realistic data, enabling the creation of complex simulations to train AI systems in industries with scarce available data.

AI-powered personalized medicine will make significant strides shortly as it can analyze large amounts of clinical and genomic data to provide superior diagnoses, predict diseases, and recommend treatment plans. This will lead to more effective healthcare treatments tailored to the patient’s genetic makeup and medical history.

As these and other AI technologies advance, they have the potential to reshape industries, transform business models, and improve the quality of life for people worldwide.

Transform Your Organization with the Nividous IA Platform

The Nividous platform is a comprehensive intelligent automation platform designed to unleash the true potential of businesses’ workforces. The platform inherently incorporates all essential components, including RPA, AI, and Low-Code Process Automation, necessary for automating complex business processes end-to-end.

The combination of technologies enables businesses to reduce human involvement, allowing their workforce to concentrate on more productive tasks. Achieving automation goals with the Nividous platform requires no additional software, integrations, or licensing, streamlining the automation process effectively.

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