5 Profitable AI Businesses You Can Start Today

According to Moffat, these use cases have been pre-tested with customers. IFS asks whether customers would like certain AI features and if they would use them, but also if they are willing to pay for them. This chat window is on the right side of the screen, where people can ask questions to the IFS Copilot. Meanwhile, a recent Cisco study found that 92% of organizations have deployed two or more public cloud providers to host their workloads, and 34% are using more than four. Recent research from IBM and Cisco points to the need to help enterprise customers better control security across multiple systems.

It monitors who in an organization can access, modify, or configure AI models during training or in production. And, it can detect prompt injections or jailbreak attempts on AI chatbots deployed by the enterprise, IBM stated. Enterprises no longer need to rely on disparate point solutions or traditional SaaS tools that work in silos. Instead, vertical SaaS platforms can orchestrate data from across the organisation, unlocking new insights and automating workflows in ways that deliver real business outcomes. Most enterprises have spent years building a complex web of legacy systems, data repositories, and software platforms. The idea of ripping and replacing these systems for something new is not only unfeasible but also impractical.

And nearly all companies have AI roadmaps, with more than half planning to increase their infrastructure investments to meet the need for more AI workloads. But companies are looking beyond public clouds for their AI computing needs and the most popular option, used by 34% of large companies, are specialized GPU-as-a-service vendors. For the past two years, the potential of AI has been constantly being discussed. Now that the AI hype is slowly blowing over, solutions with AI features become available, and we can evaluate where everybody is at and what AI will mean for software and organizations.

Enjoy personalized recommendations, ad-lite browsing, and access to our exclusive newsletters. This level of end-to-end automation unlocks new efficiencies and drives faster decision-making, allowing enterprises to stay agile and competitive. Consider a finance team using a vertical SaaS platform that automates everything from invoice processing to fraud detection. The platform doesn’t just alert the team to potential issues rather it resolves them autonomously. In manufacturing, vertical SaaS platforms can monitor equipment, predict failures, and automatically schedule maintenance before a breakdown occurs. Linnes sees which new AI businesses are gaining fast traction, and knows which approaches are here to stay.

For example, an employee needing to create a new sales pitch while in, say, Salesforce, can press a button and relevant content from the company’s SharePoint repository would be retrieved and packaged up. “We knew we wanted to embed AI in our existing applications,” he adds. “Salesforce and others have an AI module you can add on, but we wanted to be more specific for our use case.” That meant that the company had to do some serious infrastructure work. It started, like most enterprise-grade AI projects do, with the data. “From advisor to active problem-solver, an orchestrated symphony of specialised agents can thoughtfully handle a large and growing percentage of daily requests and help employees do their jobs more effectively. Copilots also step in to assist the human agent, further automating tasks and workflows that run a business.

Offer AI marketing services

What’s replacing it is something far more powerful and targeted — AI-powered vertical SaaS. This new wave of SaaS focuses not on broad-based solutions but on highly specialised, industry-specific tools that deliver clear business outcomes. Mindbody, a vertical SaaS provider for fitness and wellness businesses, claims to grow revenue by an average of 36% within 6 months when moving from traditional SaaS. AI-driven content creation is a growing field, with businesses keen to produce high-quality content faster and at a lower cost.

AI has taken us by storm but is becoming available fast and setting new standards in various platforms and software companies. Competitors of IFS are investing hundreds of millions, or even billions, in AI. IFS currently works with the Azure AI service and primarily uses OpenAI models. However, it is exploring Hugging Face’s AI models and other large models to see how it can take advantage of them. De Caux also hopes to start offering AI to its on-premises customers in a few years. This is also dependent on the development of the so-called small language models (SLMs) from the big known parties.

On the training side, latency is less of an issue because these workloads aren’t time sensitive. Companies can do their training or fine-tuning ai chatbot saas in cheaper locations during off-hours. “We don’t have expectations for millisecond responses, and companies are more forgiving,” he says.

And it’s not a gradual evolution, it’s a complete paradigm-change. The window of opportunity to establish expertise and build a brand is wide open. Start with a clear vision and choose the right AI business model to tap into this demand, positioning yourself at the forefront of a technology that’s transforming the way businesses operate.

AI-powered vertical SaaS doesn’t require enterprises to start from scratch. Instead, it seamlessly integrates with existing systems, orchestrating data and workflows to unlock business value. Enterprise decision-makers no longer care about the underlying technology itself—they care about what it delivers. They care about tangible outcomes like cost savings, operational efficiencies, and improved customer experiences. This shift in focus is causing companies to rethink their approach to enterprise software.

AI And Software Development

So it makes sense that 2023 was a year of AI pilots and proofs of concept, says Bharath Thota, partner in the digital and analytics practice at business consultancy, Kearney. And this year has been the year when companies have tried to scale these pilots up. “If your hyperscaler isn’t giving you enough at the right price point, there are alternatives,” he says. There are two major types of AI compute, says Naveen Sharma, SVP and global head of AI and analytics at Cognizant, and they have different challenges.

For companies who know they’re going to have a certain level of demand for AI compute, it makes long-term financial sense to bring some of that to your own data center, says Sharma, and move from on-demand to fixed pricing. As a remedy, there are regional deployment options, so, for example, an AWS data center in Singapore might support users in China. Moving data to a modern warehouse and implementing modern data pipelines was a huge step, but it didn’t resolve all of the company’s AI infrastructure challenges. Telecom testing firm Spirent was one of those companies that started out by just using a chatbot — specifically, the enterprise version of OpenAI’s ChatGPT, which promises protection of corporate data. Having a narrow focus on six industries and offering only a limited number of solutions has allowed IFS to compete successfully with those big players in recent years. However, the rise of AI, specifically generative AI, could throw a spanner in the works.

Despite AI’s relative infancy in the business space, when used smartly, it can empower our businesses to thrive in today’s increasingly competitive landscape. These are common for RAG, a type of gen AI strategy that improves accuracy and timeliness, and reduces hallucinations while avoiding the issue of having to train or fine-tune an AI on sensitive or proprietary data. Freddy AI Agents operate fully autonomously and are always active, providing human-like, multi-channel support, along with hyper-personalised and multilingual interaction. Additionally, it is built with strict privacy controls to meet enterprise-grade security and compliance standards.

• Instead, it leverages deep domain knowledge to understand the unique challenges faced by specific industries.
• He states that IFS’ customer portfolio is not eager to see many AI experiments, IFS’ customers want reliable AI functions and explainable results.
• However, competing with players such as Oracle, Salesforce, SAP, Microsoft, and a few others is not always easy.

“Almost all businesses have their standard operating procedures or product catalogues, which are either on the website or in the form of PDFs or documents. Our objective is to make sure that we can use this available content of knowledge and answer customer queries,” Parthasarathy added. SaaS giant Freshworkshas launched Freddy AI Agent, an easy-to-deploy autonomous service agent designed to enhance both customer experience (CX) and employee experience (EX). IFS states in its presentations that it is working on AI but does not want to chase every hype.

As software companies, personalized client experiences are at the forefront of our business, and the customer experience will always be a top priority. AI can be a major tool for efficiency, and when used intentionally, it can drive growth and innovation in an ever-changing landscape. Some of our clients have already expressed fatigue with AI-driven responses, especially in the form of live chat or chatbot features. Many customers prefer to interact with a real human when reaching out for information or troubleshooting and can be skeptical when they instead encounter a machine. It is important to strike a balance between leveraging AI tools to streamline customer service tasks and preserving the personal, human connection that customers increasingly value in today’s world.

IFS is an enterprise software vendor that focuses on several pillars with its cloud platform. The focus is on a combination of Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), and Field Service Management (FSM), but it is also specifically for six industries. IFS focuses on manufacturing, energy & utilities, construction & engineering, service industries, telecom, and aerospace & defense.

While some developers still prefer a manual process, the majority of our development team has an AI autocomplete tool built into their code editors for quick solutions to specific coding issues. In addition, IFS is building what they call an assistant framework. This framework is already partly used to determine how to read a query, what context and data are needed, and what AI feature should provide the output. An AI automation agency works directly with companies to integrate AI into their existing workflows. You’ll help clients automate repetitive tasks, improve decision-making with data-driven insights, and unlock new efficiency gains, cost savings, and productivity boosts.

The state of code security

In healthcare, for example, compliance with regulatory frameworks like HIPAA is non-negotiable. Vertical SaaS platforms can be designed with these regulations in mind, ensuring that data privacy and security are prioritised while enabling faster access to life-saving insights. In retail, these platforms can use AI to analyse consumer behavior trends and optimise inventory in real time. Solutions that don’t just provide tools, but real outcomes that align with the specific goals of each business. AI-powered vertical SaaS solutions in this space don’t just help manage product listings—they analyse customer behavior, predict purchasing trends, and even personalise shopping experiences based on real-time data.

• In part, this suits IFS just fine and can be seen as an excuse because it does not have the capabilities and resources to develop AI as fast as the competition.
• With real-time analytics, the value of Copperleaf becomes even more significant.
• IFS has about 60 AI use cases rolled out and about 300 in development.
• NetSuite is taking full advantage of Oracle technology to build out t…
• In retail, these platforms can use AI to analyse consumer behavior trends and optimise inventory in real time.

This year, Poka and Copperleaf, among others, were added to the portfolio. The global average cost of a network data breach has climbed to a record $4.88 million – which represents a 10% increase from 2023 and the largest spike since the pandemic, according to IBM’s 2024 Cost of a Data Breach Report. “IBM Guardium AI Security integrates with IBM watsonx and other generative AI SaaS providers. For example, IBM Guardium AI Security helps discover ‘shadow AI’ models and then shares them with IBM watsonx.governance, so they no longer elude governance,” IBM stated. Get the most out of your Inc42 experience by creating a free account.

During IFS’ various presentations, the company proudly showed how it competes with the world’s biggest IT companies in ERP, EAM, and FSM. However, competing with players such as Oracle, Salesforce, SAP, Microsoft, and a few others is not always easy. In a conversation with Bob de Caux, the AI leader at IFS, we asked about the AI roadmap and how far along IFS is. He states that IFS’ customer portfolio is not eager to see many AI experiments, IFS’ customers want reliable AI functions and explainable results. For now, IFS is working with prompt engineering and RAG on top of the Azure AI Service to add AI features. In recent years, IFS has experienced significant growth, on the one hand through increasing specialization in the six industries and, on the other hand, through strategic acquisitions.

In this way, enterprises move from simply managing operations to optimising them in real-time. You can foun additiona information about ai customer service and artificial intelligence and NLP. Enterprises are moving past the era of generic software in favor of comprehensive, tailored solutions that combine AI, automation, and deep domain knowledge to address their specific needs. This shift marks the beginning of a new era for enterprise technology, one where business results, not software features, are the primary concern. For the last 2 decade Software-as-a-Service (SaaS) dominated as the go-to model for businesses, offering scalable, accessible solutions for almost any operational need. The average number of SaaS applications used by an enterprise decreased by 14% in 2023. Take business process outsourcing company TaskUs, which is seeing the need for more infrastructure investment as it scales up its gen AI deployments.

Artificial intelligence is rapidly moving from novelty to necessity, with businesses worldwide racing to integrate AI into their operations. Right now, we’re still seeing early adopters take advantage, but this shift is about to go mainstream. Maria Korolov is an award-winning technology journalist covering AI and cybersecurity.

And there’s also the question of skills gaps or staffing shortages related to AI infrastructure management. Managing storage, networking, and compute resources while optimizing for cost and performance even as platforms and use cases all evolve rapidly is a concern, but as gen AI gets smarter, it might be a means to help companies. Also in the Flexential survey, 43% of companies are seeing bandwidth shortages, and 34% are having problems scaling data center space and power to meet AI workload requirements. Other reported problems include unreliable connections and excessive latency. Only 18% of companies report no issues with their AI applications or workloads over the past 12 months.

You can build a tool from scratch or white-label an existing AI solution, rebranding it and marketing it to a niche audience. “AI content is a game-changer for scaling content marketing efforts,” says Linnes. With customizable content creation options, you support clients in producing high-quality material tailored to their audience, from blog posts to product descriptions.

In times of (cyber) war, you simply cannot depend on a cloud provider. IFS is experiencing substantial growth and is successful because of its particular focus. Three major solutions for six industries with a vast customer focus and investment in industry knowledge. Moffat is the former Chief Customer Officer ChatGPT App (CCO) at IFS and has more or less carried that role into his new role as CEO. During his keynote, he emphasized several times that they invest a lot in the relationship with the customer and what the customer desires. Two years ago, partly for this reason, there were almost exclusively customers on stage.

The last thing enterprises need is yet another platform that disrupts their existing operations. The beauty of AI-powered vertical SaaS is that it integrates seamlessly into what businesses already have. Enterprises are tired of dealing with massive data migrations and lengthy implementation cycles. What they need are solutions that enhance what they already use without introducing friction. By pulling data from multiple sources and applying advanced machine learning or deep learning models, these platforms can provide actionable insights at a speed and scale that manual systems can’t match. Today, there’s a relatively small number of gen AI use cases that have moved all the way from pilots to production, and many of those are deployed in stages.

Imagine a logistics company where AI quietly optimises delivery routes, predicts shipment delays, and reallocates resources in real time—all without requiring the intervention of managers. Or a healthcare system where AI automatically flags potential health risks for patients, allowing doctors to focus on patient care instead of data entry. When AI becomes invisible, it frees up employees to focus on high-value tasks that truly move the needle. By automating routine tasks, streamlining operations, and delivering real-time insights directly within existing workflows, these platforms make the enterprise more efficient without adding complexity. For example, an AI-driven vertical SaaS platform for retail might analyse sales trends from multiple regions, predict supply chain disruptions, and automatically optimise inventory levels—all without human intervention.

The days of enterprises investing in one-size-fits-all software are numbered. What businesses need now are tailored solutions that deliver real business outcomes—solutions built on deep domain expertise, powered by AI, and designed to integrate seamlessly into existing operations. An AI tool or software-as-a-service (SaaS) business lets you create a branded, scalable product used across industries. Whether it’s a tool for data analysis, workflow automation, or productivity enhancement, SaaS platforms provide continuous value to users while generating recurring revenue for you. An AI marketing agency elevates and enhances clients’ marketing strategies through artificial intelligence. You’ll help businesses automate content creation, sort lead generation, and fine-tune conversion tactics based on real-time data and insights.

IFS is a separate player in the enterprise software market because of its specializations. IFS focuses increasingly on the IFS Cloud platform on which their three leading solutions run. However, we should also mention that they still have a decent on-premises base that is not going away anytime soon. For example, IFS provides asset management on aircraft carriers, where a connection to the outside world is not an option.

Experienced employees can record how they perform their work or troubleshoot failures. Within Poka, other employees with less experience can learn from that. Organizations can even develop training for new or temporary employees to be more productive and efficient. The next generation of enterprise software ChatGPT won’t just solve problems; it will transform industries. As enterprises look to the future, those who embrace AI-powered vertical SaaS will be the ones who lead the charge in reshaping their industries and driving meaningful results. AI isn’t just disrupting SaaS, it’s building something entirely new.

With the right approach, you’ll optimize customer engagement and drive measurable results. While every industry has its own unique needs, AI is empowering software companies to boost productivity, upgrade customer service and stay ahead of the competition. Based on my company’s experiences, here are some of the practical ways software companies can implement different AI tools to create a positive impact across departments.

Some of this optimization can be done with machine learning, and already is. But the problem with ML is that provider offerings keep changing. Traditional analytics can handle the math and the simulations, while gen AI can be used to figure out the options and do the more involved analysis.