What are Intelligent Applications?

Software has evolved, no longer requiring human assistance to execute complex tasks.

For instance, Wealthfront has redefined investing. By understanding a user’s risk tolerance and investment objectives, the application’s algorithms autonomously manage portfolios, from rebalancing to reinvesting dividends and applying tax-loss harvesting strategies, all without the user’s manual input.

Similarly, Rachio transforms garden management with its smart irrigation controller. By analyzing real-time weather data and soil moisture levels using connected sensors, it adjusts watering schedules to optimize plant health and water conservation. Users set their preferences once, and Rachio does the rest, ensuring efficient water use.

Superhuman offers a new take on email management, filtering out the noise to focus on what matters. By learning from your interactions and preferences, it highlights crucial messages, making inbox management not just smarter but truly intuitive.

These examples belong to a new class of software. Called intelligent applications (apps), they represent a departure from traditional software—where applications were once passive tools, these are proactive partners—where legacy applications required us to act; intelligent apps take actions on our behalf, improving autonomous responses over time.

What are intelligent applications in artificial intelligence?

At their core, intelligent apps leverage artificial intelligence (AI) to perform tasks autonomously, making decisions and taking actions that were once the sole domain of humans. These applications are data-driven, collecting and analyzing information from various sources in real-time to provide accurate results. By finely tuning AI models and integrating autonomous micro-agents, Intelligent Applications shift the software’s objective from assistance to action, affording them the ability to not only reason, learn, remember, perceive, and communicate, but modify their interactions with users or other systems. Data science makes this data accessible to everyone in the organization, allowing for more strategic business decisions at all levels.

Take, for instance, the journey from generic search engines to intelligent platforms like Perplexity. The intelligent app doesn’t just search; it understands. After processing a query, it engages in a dialogue to clarify intent, leveraging the conversation’s context to deliver precisely what the user needs. It’s not about answering questions anymore; it’s about understanding and addressing the user’s underlying needs.

And not all applications are user-facing. Intelligent Operations (IntelOps), an intelligent application that can automate a significant amount of developer and infrastructure operations, mostly exists behind the scenes. With IntelOps, a combination of AI agents, finely tuned models, and operational parameters work in coordination to address issues, conduct root cause analysis, spin up environments, monitor resourcing costs, and much more. While users may engage with IntelOps through a ChatGPT-like interface, most of the intelligent application operates behind the scenes, utilizing machine learning to process massive amounts of data and drive deeper insights into big data.

Intelligent applications vs. traditional applications in machine learning

The evolution of intelligent software applications isn’t just for show; it represents a substantial leap forward in how businesses operate and compete. The benefits and capabilities of Intelligent Applications afford software human-like capabilities, granting users new powers and transforming the digital workforce landscape.

Consider the legacy applications that often felt disconnected from the user experience, resulting in cumbersome digital interactions. A staggering 47% of digital workers have struggled to locate necessary information or data for their jobs. Intelligent applications also streamline workflows, automating tasks across infrastructure such as logistics and inventory management in supply chain operations. But with the advent of intelligent apps, this is changing. These apps deliver information in ways that resonate more naturally with human interaction, making users not just more satisfied but also more proficient at their tasks.

Case study: Intelligent applications in healthcare using predictive analytics

In the realm of biotechnology, where developing new cancer treatments demands precision and efficiency, one leading organization worked with Neudesic to build an intelligent app to change their approach to drug research forever. Predictive analytics plays a crucial role in these intelligent applications, offering personalized experiences and enabling the prediction of future outcomes and trends.

Faced with the challenge of time-consuming and subjective tumor core scoring by pathologists—a process that could extend up to four months—the custom computer vision solution emulates the expert analysis typically conducted by pathologists. The resulting evaluation process shrank from months to just three hours. In addition, the solution not only enhanced accuracy in identifying effective cancer treatments but also addressed the variability and scarcity of pathologist resources. The use of AI in healthcare extends to diagnosis and treatment planning, automating tasks and personalizing interactions to anticipate patient needs.

How? Using Databricks and integrating features like active learning and advanced data analytics, the intelligent app adapts over time, personalizing predictive insights and ensuring a continuously evolving and customized user experience. The collaborative model between AI and pathologists has not only elevated accuracy from 75% to nearly 99.99% but also fostered a symbiotic relationship where both entities learn and refine their analysis, paving the way for quicker, more reliable cancer treatment development.

Moreover, intelligent applications are also making a measurable impact in supply chain management by optimizing logistics and inventory management.

Conclusion: The future of intelligent applications in data-driven environments

The pace of development in intelligent applications is nothing short of astonishing. Intelligent applications are increasingly being used to optimize logistics and inventory management with predictive analytics. As these apps evolve, they promise to take on an increasingly diverse array of tasks, many of which have traditionally required a manual intervention. Pilot projects are essential in evaluating and adopting intelligent applications, particularly in the context of generative AI tools and their impact on businesses. Yet, as much as they automate the mundane, they also amplify our abilities in areas where the human element remains irreplaceable.

The future of intelligent applications lies in their ability to transcend current limitations, offering not just improvements in efficiency but also in how we conceive of software’s role in our lives and work. As we look forward to the next wave of advancements, it’s clear that intelligent applications will continue to redefine the boundaries of what’s possible, generating undeniable value across every facet of enterprise operation.

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