Where is AI in enterprise software headed?

At the end of 2022, ChatGPT drove artificial intelligence (AI) into the public consciousness. Ever since then, the desire to take advantage of its power and might—especially generative AI (GenAI)—has reached a fever pitch. For example, I can’t open Adobe Acrobat without a pop-up asking me to try its AI assistant or log into my mobile banking app without an invitation to converse with a bot that can guide me through my accounts.

GenAI, machine learning (ML), large language models, and retrieval-augmented generative models are currently redefining enterprise software. In this blog, I take a look at how AI is currently changing enterprise software and where it’s headed next.

Enterprise software: The times, they are AI-changin’

The evolution of AI technology is paving the way for new business models and strategies. It is already enhancing efficiency and productivity, enabling smarter and more informed decisions, revolutionizing customer experiences, and significantly enhancing security measures. Here are some ways that it’s currently making its presence felt in enterprise software.

Integration of AI in software for business functions

AI is showing up in software for various business functions, including sales, marketing, HR, finance, and operations. For instance, AI integrated into analytics software is quickly providing real-time insights into market trends, customer behavior, and supply chain dynamics. In addition, natural language processing, ML, predictive analytics, and more now automate routine tasks, optimize business processes, and enhance decision-making.

An example of this is US regional bank KeyBank, which brought a hyper-automation strategy to life by combining AI, ML, intelligent documentation, and robotic process automation.

Refocusing work with AI

AI has been automating mundane tasks for years, but GenAI in software is now enabling employees to work faster and save time with prompts and generated content. This opens up opportunities for employees to focus on the more strategic and creative aspects of their work. Additionally, AI is facilitating the rise of a more flexible and adaptive workforce, with intelligent tools in enterprise technology supporting remote work and collaboration.

Scalability and agility brought to you by AI

AI systems in enterprises are handling much more work without a corresponding increase in costs. As a result, businesses are in the process of scaling their operations more efficiently. At the same time, AI is also enabling business agility. Businesses are better at navigating market changes, customer demands, and supply chain disruptions with real-time insights and the ability to generate responses that adapt over time.

Insurer Fidelidade has done just that. It built an application that uses machine learning models, AI, and AR to enable insurance customers to assess their car's existing damages remotely in less than five minutes using a smartphone.

Differentiation supported by AI

Enterprise software with AI capabilities is inspiring a host of new products, services, and business models. Enterprises that are using AI solutions like digital twinning or AI Agents can create unique value propositions that are difficult for competitors to replicate. This innovation can lead to new revenue and market leadership.

For example, Boston Scientific has introduced a differentiating product in the healthcare market. It includes algorithms that use patient data to automatically flag those who might need a special heart procedure.

Future trends: AI in enterprise software through a crystal ball

The integration of AI in enterprise ecosystems is not just about enhancing current processes. It’s also about reimagining the very fabric of how businesses operate and compete in an increasingly digital world. Picture a future painted with the intricate details of deep learning, the dynamic strokes of reinforcement learning, and the bold colors of quantum computing.

Deep learning promises a depth of analysis and decision-making that mirrors the complexities of the human mind. Reinforcement learning uses feedback to train AI and ML models to find the best action in a trial-and-error process. Quantum computing, through a whisper of a promise on the horizon, hints at a future where our current computing limitations are rendered obsolete.

What does this look like? My crystal ball is ready to show and tell.

AI-powered innovation and new business models

The transformative potential of AI extends to enabling entirely new business models and avenues for innovation. For example, AI added to pricing software could enable prices to adjust in real-time based on demand, competition, and customer profiles. In healthcare, AI could put the “personal” in personalized medicine, where treatments and medications are optimized based on an individual's genetic makeup, socioeconomic factors, and geographical location. In capital markets, the combination of AI and quantum computing has the potential to upend financial modeling by performing complex risk analysis and portfolio optimization.

Greater autonomy in business process management and operations

AI is on track to take autonomous business processes to a whole other level, where enterprise software can operate and make decisions with minimal human intervention. At this level, process automation applications evolve through continuous learning from data patterns and outcomes to automatically adjust workflows for greater efficiency. Integrating AI more fully into supply chain management could better predict disruptions, autonomously adjust orders, and optimize logistics without human input. In manufacturing, AI could control production lines, adjusting parameters in real-time for efficiency and quality.

Sustainability and social good—the AI way

As environmental and social challenges become more pressing, AI offers promising solutions. AI embedded in software will be able to analyze massive volumes of data to predict the effects of climate change, optimize energy consumption in industries, and contribute to sustainable urban planning. Additionally, healthcare, education, and humanitarian aid organizations could soon be using software powered by AI to improve accessibility, personalize learning experiences, and enhance disaster response efforts.

Amping up cybersecurity

The future of cybersecurity software and applications is increasingly intertwined with the advancements in AI. This will result in more dynamic, predictive, and effective software and endpoint defense mechanisms against cyber threats that seem to grow in sophistication and complexity daily. AI capabilities built into cybersecurity solutions could soon amp up current predictive threat detection, catching and mitigating potential attacks. Automated responses, enhanced phishing identification, and the use of deep learning to spot anomalies could significantly reduce the time between threat detection and response.

Embracing AI-powered enterprise software on the horizon

The future of AI in enterprise software is a landscape brimming with opportunities for transformation, innovation, and societal impact. The evolution of AI technology will redefine what is possible, pushing the boundaries of human creativity and ingenuity. By embracing a forward-thinking approach, enterprises can lead the way in creating a future where AI in enterprise software amplifies human potential and drives progress for all.

If you’re eager to explore how to use AI in your apps now, the OutSystems low-code platform can help. OutSystems is bringing the AI future into the present by making it easier to build enterprise software infused with AI. In fact, the platform itself offers AI-assisted development to help IT teams embed AI into their applications. Plus, OutSystems Mentor, a digital worker built into the platform and available on OutSystems Developer Cloud, can generate a fully functional app from a prompt or requirements document and then support iteration through suggestions and edits. Along with these GenAI features, Mentor also includes artificial intelligence capabilities that manage your app through its entire lifecycle, including DevOps, validation, deployment, updates, and more.

In all aspects of enterprise software, the future of AI is bright. To learn more, visit the OutSystems AI page.