How AI Extends the Shelf Life of Your Business Apps

Remember Lotus 1-2-3? WordPerfect? These once popular apps are classic examples of business software considered indispensable but that's since gone the way of the dinosaurs. It's a perennial challenge with no easy solution. As any IT professional can attest to, maintaining an enterprise tech stack can often feel like a game of Whac-A-Mole. You deploy software to solve a business problem, only to have that software become outdated or even obsolete when the next whiz-bang app comes along, sinking your hopes of a return on your investment. AI changes that equation.

Previously, developers built applications by collecting requirements and writing business logic (the rules and processes that govern how an app operates) to meet those needs. Today, using AI for app development, developers create smaller reusable components for specific functions. With apps that can learn on their own and AI copilots capable of performing tasks they were never programmed for, software obsolescence as we know it may soon become a thing of the past.

Future-proofing your technology with AI

AI extends the shelf life and usability of your apps by continuously learning and adapting new functionality to perform new types of tasks. So what does future-proofing look like in the artificial intelligence (AI) era? Consider this customer service scenario:

A company uses an AI chatbot to answer basic customer questions, like "where's my order?" or "how do I update my address?" As time goes on, the chatbot collects a trove of invaluable data. It learns the most commonly asked questions and which responses are considered most effective. It begins to learn what types of interactions lead to the highest customer satisfaction. The chatbot uses data, machine learning (ML), and natural language processing (NLP) to improve its understanding of customer sentiment. For example, it wouldn't thank you for your patience when the customer clearly expresses anger, and would offer instead to escalate the issue.

This deeper understanding helps it understand and resolve more complicated questions that it could not initially. The AI chatbot eventually becomes sophisticated enough to anticipate needs based on patterns in the data it has accumulated.

This represents an inflection point in application development, saving companies the time and money of building new capabilities from scratch, enabling them to innovate much faster. In addition to their ability to generate content, LLMs are capable of reasoning and orchestrating tasks. They are already solving problems that used to require custom applications to be built, or that were just not possible to solve with traditional applications.

AI helps you build new capabilities more efficiently

AI copilots reduce the need for frequent, expensive investments in new technology and workflows, helping you extend the usefulness of your existing technology.

Ajay Kumar Kambadkone Suresh, director of solution engineering at Salesforce, gives this example: A company has 10 application programming interfaces (APIs), which is a type of software that allows different programs to communicate with each other. The company wants to launch a new product and needs to develop two new APIs to do so, requiring development time to build and test.

"Today with a few customizations and some light engineering, you can tell your AI copilot that you're trying to build this product and you need a new front end, and connections to these four systems," he said. "What is the best way to do that without breaking anything I have?"

He said the copilot can evaluate existing APIs, and reuse the best bits from each, creating something new without investing time and money building something from scratch.

"Companies will be able to use what they already have to produce something brand new," he said.

AI's adaptability maximizes your software investments

Because AI copilots are designed to handle a broad array of tasks and can develop new capabilities, they can be repurposed for different business uses. This is due to their ability to learn and adapt through continuous data analysis and machine learning, enhancing their utility and, ultimately, your return on investment.

Picture this. An AI copilot is used to automate marketing tasks, analyzing customer data to personalize emails, segment audiences, recommend products, and more. That same copilot can be retrained using sales data, by feeding it information about leads, customer interactions, and revenue metrics. The AI learns to apply its capabilities to the new domain, understanding patterns and optimizing sales processes.

This helps maximize investments in AI across both sales and marketing, without the need for two separate AI systems. That's only possible when one AI copilot is natively integrated with all CRM apps, giving business users access to insights and recommendations across sales, marketing, commerce and service.
"That's where AI extends the life and usability of products," said Kambadkone Suresh.

Unify your data to open new CRM opportunities across the company

What makes this level of extensibility and reusability possible is a unified data backbone bridging CRM data across marketing, sales, service, and ecommerce activity, giving you a single view of everything your customers have done and are doing, regardless of where the data lives. Einstein Copilot, for example, delivers this out of the box, grounding every LLM prompt with up-to-date customer data from Salesforce Data Cloud.

By unifying customer data in one repository like Data Cloud, all departments and applications work with the same current data set, improving coordination and reducing redundancy. These platforms provide the foundational data you need for AI applications.

That's crucial as you look to squeeze as much value as possible out of your technology investments. No technology lasts forever, but with generative AI and the right data backbone, you can greatly extend an app's shelf life - maybe even reach the holy grail of "future proofing" it.

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