As the name suggests, intelligent applications are applications powered by artificial intelligence (AI) and packed with data provided by transactions and other external sources. Similar to generative AI (GenAI), intelligent applications learn from interactions and gradually improve their autonomous responses over time, transforming the experience for customers, architects, developers, and other users.
Michelle McGuire Christian, a principal at Deloitte Consulting, said in an email interview that the biggest benefit of this technology is that it makes data science more accessible, takes data science out of the hands of data scientists, and makes it practical for everyone in the organization. He said that it is possible to provide significant insight. “This means companies can provide employees at all levels, all the way down to the C-suite, with the data they need to make more strategic business decisions.”
Intelligent applications quickly analyze large amounts of data and uncover patterns and insights that humans would not notice, Amazon Software Development Engineer Mayank Jindal explains in an email. “It allows businesses to react in real time to industry changes, increasing business flexibility.” Intelligent applications have the potential to reduce operational costs by automating routine tasks and resource allocation. There is also.
Most adopters believe that intelligent applications give them the time they need to perform complex operational tasks, access new insights, and build truly data-driven, scalable business models that empower all team members with valuable information. Christian says. “With this [technology], businesses can create new ideas, outcomes, and experiences for their customers, including better support and more personalized engagement. ”
Julien Moutte, CTO of infrastructure software engineering company Bentley Systems, said in an email interview that many organizations are under increasing pressure from accelerating digital transformation, talent shortages and sustainability requirements. I am. He said, “Intelligent applications can increase productivity, improve engineering quality, build more resilient infrastructure, and ensure sustainability requirements are met.”
gain intelligence
Intelligent applications offer enhanced data analysis capabilities and leverage machine learning to derive deeper insights into big data, Jindal said. “These types of applications can deliver personalized experiences based on consumer behavior and preferences, increasing customer engagement,” he explains. “It also supports risk management and decision-making by predicting future outcomes and trends.”
Using AI and ML capabilities in intelligent applications can bring significant value to organizations, says Moutet. “Applications with intelligent capabilities can automate tedious tasks and streamline workflows across the infrastructure.” For example, engineers and asset owners can use computer vision AI techniques to transform thousands of CAD drawings. He points out that objects can be quickly analyzed, detected and identified from
Intelligent applications are particularly useful for customer relationship management, Jindal says, allowing them to personalize interactions and anticipate customer needs. “In supply chain management, predictive analytics can be used to optimize logistics and inventory management.” He added that applying intelligent applications to fraud detection and personalized investment advice can also be measurable in financial services. He added that it could have a significant impact. “In healthcare, AI applications can help with diagnosis and treatment planning.”
When implementing intelligent applications, start by building a solid foundation using cloud-native tools and ensuring strong data hygiene is practiced. “Organizations need to create a set of data governance rules to integrate a single enterprise-wide data source and capture and store information in a way that eliminates duplicate sets,” Christian advises. . From there, adopters should consider their business challenges, objectives, and opportunities and focus on a small number of initial use cases.
first step
The easiest and quickest way to start using intelligent applications, regardless of industry or field, is to look at how data is organized, managed, used and disposed of, says Moutet. “Intelligent applications leverage data, so results can only come from good inputs,” he warns. “Companies that master data management can unlock the full potential of intelligent applications, digital twins, AI, and machine learning.”
Before moving to intelligent applications, companies need to identify the areas where such applications would be most beneficial. “Maintaining the right mix of technical and domain experts is essential when assembling a team,” says Jindal. It's important to invest in scalable AI infrastructure and tools that support your business goals. “Finally, pilot projects allow us to confirm the capabilities and limitations of the technology before full-scale deployment.”
assignment
Christian says he's seeing a lot of new hires trying to implement intelligent applications into everything all at once. “Our advice to clients is pragmatic: Don’t try to tackle everything at once and prioritize high-yield use cases. Don’t overlook the foundation and infrastructure needed to support intelligent applications. , she says, are common pitfalls. “Cloud-native tools, a single data source, and data governance are key to getting started.”
Robust data privacy and security practices and technology are essential, says Moutet. “This is especially true in the infrastructure sector,” she points out. “Engineering firms and owner-operators need a clear understanding of how their proprietary data and intellectual property is being used to train generative AI models.”
Prominent player
Current market players include leading technology companies such as IBM, Amazon Web Services (AWS), Google Cloud AI, and Microsoft Azure AI with their Watson platform. “Salesforce provides AI solutions for customer relationship management through the Einstein platform,” Jindal said. “Amazon Web Services (AWS) offers a wide range of AI options, including machine learning and data analytics capabilities,” he said, adding that smaller specialist companies such as Palantir and OpenAI are also contributing significantly to the market. Ta.
