AI Helper for SaaS: AI Integration Use Cases

AI helper, often formally called as AI assistant, is a tech solution that uses the means of artificial intelligence, such as large language models and natural language processing, machine learning, optical character recognition, etc., to automate numerous actions and business processes. Before the active AI development, those have been SaaS products that covered a large share of automation tasks. 

Software as a service solutions have become a game changer in automation and optimization of business processes across all industries. Everyone has heard about at least a few of the top SaaS companies, like Microsoft, Google, IBM, Salesforce, Adobe, SAP, Oracle. Most have used some of their SaaS products occasionally, many work with them on a daily basis.

Imagine the power of AI integration into a SaaS solution!

And we can assure you, there’s definitely way more than just a hype behind it. By creating AI software SaaS providers achieve multiple goals:

1. Provide SaaS clients with cutting-edge, effective and easy-to-use solutions. 
2. Increase customer satisfaction.
3. Outperform competitors and win / keep the market positions.

So what capabilities exactly do AI products provide their users with? Let’s peek into case studies of successful SaaS companies, including a few who entrusted their digitalization journey to Apiko’s SaaS app development services.

SAP AI use cases 

The world’s largest ERP software provider with about 437.000 clients in over 190 countries, SAP has powered up its vast functionality with AI solutions for nearly every aspect of business. The platform is highly customizable for company needs, so the yearly subscription cost varies from about $50k to over $1M, depending on the number of users and features included.

SAP has developed AI solutions for entire supply chain management, as well as such ones that cover its every single component on demand. Some of the SAP AI use cases are described below.

Procurement

For Unilever, SAP AI analyzes historical procurement data, vendor performance, and market trends to recommend the best suppliers. It also conducts spend analysis, identifies savings opportunities by consolidating purchase orders and optimizing contract terms.

Manufacturing and quality assurance

An air compressor manufacturer Kaeser Kompressoren uses IoT-enabled AI models to predict equipment failures. They analyze sensor data in real time. This allows proactive maintenance scheduling, reducing downtime and repair costs. 

Similarly, SAP AI models work for Bosch. They analyze production line data to identify defects, predict potential failures, and recommend process improvements in real time, contributing to quality assurance.

Demand planning and inventory management

SAP AI analyzes historical sales data, seasonality, and external factors like weather or economic trends to forecast demand. It ensures product availability, reduces waste and inventory costs.

Logistics and warehouse management

DHL uses SAP AI for route optimization, demand forecasting, and warehouse management. AI predicts delivery delays and automates sorting processes, enabling more efficient logistics operations.

Distribution

Many companies, including Adidas and Levi Strauss & Co, leverage SAP AI helpers to provide personalized customer experience. AI delivers tailored shopping experiences to millions of customers by analyzing their behavior, preferences, and purchase history. It also makes personalized product recommendations, and adjusts prices based on market demand and customer segmentation.

For Marks & Spencer (M&S) SAP AI analyzes store camera footage and sales data to predict when shelves will run out of stock and recommend restocking. It also tracks customer movements to optimize store layout. This improves supply chain efficiency through demand forecasting.

Sustainability and carbon footprint analysis

Shell and British Petroleum use SAP Sustainability Control Tower to measure and analyze the organization’s carbon footprint across its supply chain. AI also offers actionable insights to reduce emissions.

Recruitment and talent management

Siemens applies AI to improve hiring efficiency and reduce time-to-fill vacancies. AI is used to screen resumes, match candidate skills with job requirements, and predict candidate success in specific roles. It also provides personalized career path recommendations for employees.

Banking and finance management

SAP Business AI offers real-time transaction monitoring. It detects anomalies and fraudulent transactions by analyzing patterns in financial data. It also ensures compliance with industry regulations by flagging suspicious activities.

AI solutions provide predictive insights into cash flow, profitability, and expense management by processing real-time financial data. They also automate repetitive accounting tasks.

SaaS success “ingredients” 

You may think: “Ok, I’m not going to develop the next SAP or Microsoft, and, obviously, it takes a miracle to outperform them. So, even with AI integration, are there any chances for my SaaS success?”

A certain answer is yes. You need to 

1. Be an expert in your field.
2. See the way to directly resolve particular problems and inefficiencies that are common for this field / industry.
3. SaaS is a digital implementation of your solution. Clearly define its functional and non-functional requirements before reaching out to SaaS developers. You will sharpen the product vision during the discovery phase, yet thorough preparation is a must.
4. Come up with an effective SaaS marketing strategy.

The following case studies are the proof that your solution can become not just successful, but rightfully take its place among the SaaS leaders. AI implementation provides additional momentum for bringing their efficiency and productivity to new heights.

Hive AI tools for project management

About 9 years ago Hive cofounders chose Apiko as a software development partner to bring their groundbreaking idea to life. Today, Hive is a leading project management SaaS designed to make teamwork as efficient and easy as it’s never been before. 

Full customization and software integrations with every single third-party tool or service required by a particular organisation are just a few things that contribute to making Hive the #1 choice in the world. A recently added AI integration has provided unsurpassed user experience, becoming an absolute blast in terms of customer satisfaction.

So, what are the Hive AI tools for project management? Let’s take a look. 

AI helper named Buzz

This AI assistant helps Hive users in multiple ways. One can create and manage projects, generate reports, and much more by simply talking to AI chatbot. And yes, at the first glance it does look like an ordinary support chat. So, when first introduced, most Hive customers closed the AI helper window within the first 5 seconds on the platform without investigating its capabilities. The thing is, this feature is powerful and can actually be very useful. 

Here you can see how Buzz creates a new project.

 

It also can add tasks within the project effortlessly, and provide their descriptions.

 

HiveMind AI assistant

This AI helper can create a project according to your requirements directly within the “New project” tab.

 

Moreover, within the “Workflows” tab, you can schedule your AI assistant’s activities, or define the triggers that will prompt the AI helper to complete a certain action. The convenient AI workflow management UI allows you to watch the template creation in real time. You can easily adjust the template by specifying your needs and preferences.

 

An AI email assistant regularly “reads” the inbox emails, and automatically archives the useless messages, if the user has connected their mailbox to the system. This provides considerable time savings, as a sufficient amount of such correspondence bypasses spam filters. If you still notice that some unimportant emails remain unarchived, you can inform the AI email assistant about more of your preferences by clicking the “Add memory” button.

 

It also generates respective reports on a daily basis.

 

The Hive AI assistant provides even more auxiliary functionality, like creating content, images, step-by-step plans or instructions.

 

The AI email assistant can also help you with generating the email topic and text, sufficiently cutting down the time spent on correspondence.

AI for fintech: Ageras case study

Ageras is a fintech platform winning the hearts of Europeans by its simplicity, clarity, and user-friendly approach to managing accounting, banking, and tax-related tasks. The founders aimed to enable every person to cope with their financial chores quickly, efficiently, and without the help of professionals, making it aka a fintech self-service app.

 

So, how does Ageras use AI for fintech? AI integration leverages the optical character recognition (OCR) to scrape the informative data (e.g. name, address, cost of the purchase, etc.) from bills and pay cheques, later autopasting it into the required forms, e.g. invoices or tax reports. This has increased the accuracy of error-sensitive data, which is crucial for the financial operations.

 

 

AI integration plan for SaaS products

It may seem like AI is a secret ingredient to make any SaaS great. However, you need to approach it wisely. There’s no need for AI integration in SaaS just for the sake of AI. You should have a clear vision of those business processes, covered by your SaaS, which could benefit from further automation with AI. 

Here’s the basic AI integration plan one should follow to get the best outcomes.

1. Identify use cases for AI in your SaaS product.
2. Research the tech stack: evaluate and select an AI solution or platform you could use for reaching your SaaS goals.
3. Run the proof of concept: it’s necessary to prove that such implementation is feasible, as well as to assess the risks of using the chosen technology.
4. Integrate AI capabilities into your existing SaaS platform.
5. Train and optimize the AI models for your specific use cases: AI accuracy cannot be neglected and you’ll have to work on it.
6. Test and validate the AI integration with real user data.
7. Monitor and update the AI models regularly to improve performance.

Let’s sum it up

AI helpers are excellent for processing and analysing large volumes of data. They can generate a concise, yet straight-to-the-point report based on a large flow of information, a great part of which is often unnecessary or even useless. The AI helper can easily speed up routine tasks, generate detailed project templates, etc. Almost certainly, every SaaS solution can benefit from AI integration.

Anyway, artificial intelligence solutions do have their limitations. 

• While AI enhances data-driven decision making, it is upon a human to double check the results of the actions suggested, and to take an action. 
• AI does not really understand the nature of the language on a high level. It is trained on the most frequently used word combinations, and a huge number of their possible use cases, which may give an illusion of understanding. It may be an obstacle in such areas where the word choice is crucial, e.g. law or medicine, although the large language models are constantly being improved.

AI is a vast area. If you’d like us to share our bit of expertise with you, don’t hesitate to reach out!