AI for Enterprises: Case Studies and Trends for 2024

AI for Enterprises: Case Studies and Trends for 2024

AI in the enterprise is transforming the way businesses operate. From data management to cybersecurity and compliance, companies are integrating enterprise AI solutions into key business processes, all with the goals of automating processes, saving costs, introducing MVPs faster, and collecting insights into their operations.

Throughout this guide, we’ll discuss the rationale behind embracing enterprise AI strategy, the impact and challenges of data management for enterprises, and the current trends surrounding GenAI as well as its future trajectory. Additionally, to make our topic practical, we’ll close with two enterprise AI case studies we had at S-PRO that highlight how AI adds value to enterprises. 

Why Discuss AI & Enterprises?

In resource-constrained business environments, AI enables companies of all sizes to streamline processes, automate repetitive tasks, and improve operational efficiency. 

However, adopting this innovative technology has hurdles, especially for enterprises with large-scale operations, intricate organizational structures, and strict compliance requirements. 

But despite these challenges, AI continues to revolutionize the way enterprises work in several key ways, including:

  • Growing SaaS adoption.

For example, according to a recent study, 67% of enterprise infrastructure is now cloud-based. This represents an industry-wide shift towards SaaS-enabled delivery solutions.

  • GenAI hype.

Gen AI stands to have a profound impact on the pace at which businesses move to the cloud, pushing them to accept more modern infrastructure compatible with AI features that allow them to be more effective and competitive. 

This is important because even though companies can easily integrate chatbots, e.g., AI ChatGPT for enterprise, on their platforms, it is nearly impossible to achieve the transformative effect of intricate internal enterprise systems without reorganizing their core infrastructure.

  • Application of AI in new domains.

While artificial intelligence use cases include gaming and social industries, tightly regulated domains like governance, compliance, and data security are only beginning to implement GenAI into their products. 

That said, the potential for these products is immense. Previously, project managers had to undergo extensive training to understand the structure and execution of various standards. However, with AI copilots, the burden is significantly reduced, and the process of ensuring compliance is streamlined.

Data in Enterprises: Challenges and Solutions

Data is the lifeblood of every business. Without data, or more simply, input from users, you cannot build an algorithm for your decision-making processes. 

Yet, as critical as data is to enterprises, it also presents significant challenges, several of which we explore in the following section, along with solutions you can implement.


Some of the most common data management challenges enterprises face include:

Incompatibility with Legacy Systems

Most enterprise systems running today rely on custom but super obsolete systems like ERP solutions or databases, which are tedious to use, maintain, and update, especially when onboarding new people and approaches to your infrastructure.

Reliance on Manual Processes

Data cannot exist by itself, so you need mechanisms to extract, process, and send it to analytics. Too often, enterprises are held back because they rely on manual data entry and spreadsheets scattered across different Google Drives or Excel sheets, which are prone to getting outdated and are really hard to manage.

Difficulty Migrating to New and Innovative Technologies

It is difficult for enterprises to migrate to new solutions because the bigger they are, the harder it is to switch to a completely different approach.


So, how can you approach these problems for your enterprise? 

Data Management Platforms

First of all, you need to adopt a standardized and scalable means of managing your data. This centralized data management platform can ingest data from various sources, process it, and feed it through an analytics program to derive insights into your operations and then adapt those insights according to your needs — all in one place.

Data Governance Frameworks

Data governance involves policies that define how your employees work with data, identify controls and safeguards, and clearly define permissions and data ownership. By embracing proper data governance, you can ease compliance adherence and automate necessary elements like controls, permissions, and security.

However, you cannot simply deploy any program and expect it to work, bringing us to our next point, which is…

Employee Education and Awareness

In addition to adopting new technologies, your company or enterprise must ensure that employees understand how to use them, their roles, and how they can maximize their work time. In short, you need to prioritize education and open communication in your organization.

Cloud Deployments

Even though there are compliance requirements to consider and companies cannot deploy their entire systems in the cloud due to privacy and other reasons, it is a good idea to consider more efficient and scalable solutions, such as the cloud for parts of your system that aren’t subject to extensive compliance regulations.

GenAI: Current State and Future

This section will focus on the Generative AI market, particularly its current state and future outlook.

Current State

Presently, the GenAI industry is characterized by these trends:

Accelerated Growth and AI Adoption

LLM (Large Language Model) growth across industries leads to faster development cycles and reduced costs for engineering teams. Products that would take years to implement in 2021 and 2022 now emerge in a few months, meaning it is easier than ever to enter the market with early versions of your products to obtain user validation.

Rapid Improvements in Image and Video Generation

While videos that match humans’ performance are an ongoing challenge, tools like Midjourney or Sora from OpenAI show promise for broader video AI adoption. The same applies to audio. With models from Meta, MusicGen, and more, we now see that leveraging vast amounts of video and audio data through RAG (Retrieval Augmented Generation) allows these tools to produce more satisfactory results.

Companies and Individuals Want to Benefit from AI

While public awareness of AI is growing, understanding of the privacy and security of these systems lags behind. In response, companies like Google and Meta are creating model cards that contain findings about the models they get during research and development. This allows you to understand whether you can leverage an AI tool for your business, considering the data used during training and the biases it might have concerning different user types.


And now to the future of GenAI: 

Multimodal Generative AI 

Multimodal generative AI systems like Gemini and GPT-4 are setting the standard for what it takes to be considered a top LLM. These systems’ transformative feature is that they combine textual input with images, allowing for more versatile interactions and outputs. It isn’t a long shot to imagine solutions of the future capable of recording audio and video, speaking to us using text-to-speech, and processing audio via text-to-speech. 

Responsible AI 

Despite AI legislation being in its early stages, in a year or two, service providers working with data and AI will need to comply with many more responsible AI measures to operate in evolved markets, especially the US and European Union.

More GenAI Providers and State-of-the-art Systems

GenAI is seeing an unprecedented rise in both the number of providers and the “robustness” of solutions. There are a lot of models introduced by various companies or LLM providers. On one hand, open-source solutions outnumber what we’re seeing from big names like Google and Meta. On the other, state-of-the-art models like GPT4 cannot be deployed on basic GPUs, bringing us to the next trend of…

Compatibility with Everyday Devices 

There is a growing movement to compress AI models for deployment on smaller devices like mobile phones, laptops, smartwatches, and TVs. Microsoft's recent paper on Bitnet exemplifies this, where instead of using regular parameters such as floating point numbers, they decided to quantize those parameters to only three possible values: minus one, zero, and one. 


The emergence of LLMops is specific to GenAI and has already become a recognized job opportunity in the market. While the role is largely still being defined, job posts already list responsibilities such as prompt engineering, evaluation of results, LLM selection & deployment, and enterprise machine learning for applications. 

S-PRO Cases 

Now that we’ve explored GenAI trends, let us go over two real-world examples of how AI boosts enterprises regarding usefulness and value for clients.

Compliance Aspekte — AI copilot for GRC 

Compliance Aspekte, a leading GRC solution in Europe, empowers companies to manage documents, assess risks, and prepare for compliance certifications like GDPR, ISO 27001, and IT Grundschutz. Before partnering with us, the tool relied on rule-based algorithms and didn’t use AI, and its chatbot was limited in functionality. Our team addressed this by analyzing the firm’s IT architecture, system integrations, and user data, culminating in us developing a new “co-pilot” chatbot. This new version enables users to upload documents, automatically associates documents with compliance requirements, and provides relevant insights and instructions.

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TravelPlanBooker — AI solution for the travelling industry 

Another example of AI bettering enterprises is our work with TravelPlanBooker. In essence, TravelPlanBooker is the number one platform for stress-free trip planning. Before implementing AI, the platform featured a rule-based approach to receiving user inputs and generating suggestions, making it prone to conflict and scalability issues. To fix this, we implemented generative and conversational AI systems, enabling them to handle abstract queries and offer nuanced travel advice.

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For example, imagine you want to visit the Maldives for three weeks with friends and visit as many historical places as possible. You can simply feed this input into the tool. After that, TravelBookerPlanner will create an ideal daily itinerary for you, suggestions on how to best enjoy your time away, activities to do, etc. This AI platform also allows users to make real-time adjustments to their travels.

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