The generative AI hype wave is slowly coming to an end. Instead, this technology is now widely recognized as a necessary addition for any enterprise that doesn't want to be left behind. 

While generative AI is still generating a lot of excitement, companies are starting to take a more balanced approach to weigh the potential benefits against concerns like cost, data security, and accuracy of answers (think about AI hallucinations).

The spotlight question now shifts from „Do we need to utilize generative AI in our workflow ?“ to „How do we do it in the most practical and cost-efficient way ?“

This sentiment arises from experience, as organizations have already faced various implementation challenges. Most generative AI initiatives are yet to be fully deployed and show a significant and tangible return on investment. 

Now that implementation challenges have become a primary concern, with a 14-fold increase in concerns about implementation costs, it's becoming apparent that the market needs practical and streamlined solutions for implementing generative AI.

This new focus on practicality, cost efficiency, and delivering fast and tangible ROI is driving the next wave of generative AI adoption.

Generative AI Implementation Challenges

As it turns out, simply using an LLM alone is not enough to satisfy most of the company's needs for generative AI. Implementing generative AI into a workflow brings with it a whole new set of challenges that need to be addressed. Most notable of them is figuring out how to safely feed generative AI with often sensitive business data and bring it within a workflow framework where employees can easily utilize it in tasks where they need it the most.

This is further emphasized by the fact that security concerns have more than doubled since 2023. Another statistic showcasing that the implementation of generative AI is not so smooth sailing is that only 25% of planned generative AI initiatives have not been fully deployed and are yet to reach their imagined potential.

Another big challenge that organizations face is figuring out what are exactly the best ways to use generative AI. Despite the fact that the number of possible applications for generative AI is growing, with many of them already being tried and tested, it's still quite challenging to fit it perfectly into existing working processes.

Response accuracy is becoming another major concern. Many of us encountered a situation when LLM suddenly decided to talk nonsense, which justifies increasing worries about the reliability of generative AI responses.

When all of the concerns are taken into account, companies now need to figure out how to safely feed their proprietary data into AI models and ensure the results are accurate and relevant.

Everyone wants to automate workflows and have generative AI as their right-hand assistant for creating summaries, data analysis, and more. However, setting up generative AI within an established workflow framework requires careful planning and expertise in order to avoid disruptions and maximize efficiency gains. This is the reason behind a major rise in the off-the-shelf solutions designed to provide solutions to most, if not all, of the above-mentioned problems.

The Rise of AI-Integrated Enterprise Software

As the challenges of implementing generative AI become more obvious, a new wave of off-the-shelf solutions is emerging to address these pain points.

Now that the need for practical and secure AI integration is being widely recognized, software companies are stepping up to fill this gap with solutions that provide businesses with a clear path to fast and easy implementation of generative AI.

The most popular solution for utilizing generative AI within a business framework comes in the form of enterprise search platforms coupled with LLMs. 

Every company has its own set of tools, databases, communication mechanics, and workflows, and enterprise search software offers the opportunity to quickly connect all of these diverse data sources. Once the integration is complete, these platforms can feed the relevant business data to large language models in a controlled and contextual manner. 

This essentially means that employees can now utilize generative AI with the data and tools they use every day and immediately see tangible benefits. Those benefits include creating summaries and reports with a single prompt, automating tasks that used to require significant manual labor, and using generative AI within the highly customized setting of a specific business environment.

A combination of generative AI and enterprise search is then proving to be fertile ground for generative AI adoption, and as such, it tackles one of the primary concerns that businesses have about creating the right framework in which their employees can make the most out of generative AI.

On top of that, many of these solutions are designed to address the security concerns related to feeding the generative AI with sensitive data. 

As they are not custom-trained LLMs and simply integrate with already established generative AI solutions, these platforms eliminate any potential risks of AI inadvertently revealing sensitive information because they can integrate with existing permission structures and allow employees to feed generative AI only the data they already have access to.

Another important feature of enterprise search software is that it is directed towards providing a user-friendly and accessible experience that enables employees across all levels of organization to use AI within the context of their specific tasks. This freedom gives everyone the opportunity to find the best use cases for generative AI and leaves a lot of room for custom automatizations and creative solutions.

Given the growing concerns about the implementation cost of generative AI, providing employees with instant access to generative AI and allowing them to automate and augment a big part of their workflow provides a tangible way to measure return on investment.

Moreover, many of the platforms offer machine learning built-in mechanisms designed to monitor and improve response accuracy over time and a number of interactions. This feature addresses worries around AI hallucination and response accuracy and relevance.

Akooda GenAI - Instant Enterprise AI Integration

Akooda offers a comprehensive platform that combines all the features that allow for a fast, secure, and effective introduction of generative AI in already established workflows.

Since it features around 50 pre-built connectors, as well as the ability to create custom APIs for any tools and apps, it allows for easy and thorough one-click integration.

In addition to this, Akooda takes care of all the important security protocols, adheres to SOC 2 standards, and integrates with any existing permission and authorization structures already existing within a business.

Akooda integrates all of the company data into a searchable unified repository and enables direct communication with large LLMs through APIs, which brings the power of generative AI directly to where you need it the most.

This enables instant, worry free, cost effective and practical solution to integrate all of company diverse tools and data sources and enable use of generative AI within the framework where employees can utilize the most generative AI has to offer.