The Undiscovered Potential of Generative AI for Enterprises

Generative artificial intelligence has been everywhere in the news since OpenAI released version 3.5 of their popular NLP-based chatbot for public use in November 2022. In March 2023, the “chatgpt” search query hit 100/100 points of global interest in Google trends. 

Despite the initial interest coming from consumers of AI tools, generative artificial intelligence has the potential to reinvent processes and operations on an enterprise scale.

According to a recent Generative AI in IT research, ​​33% of senior executives have put related initiatives on their digital agendas, and the other 67% plan to prioritize the technology for their business within the next 18 months.

While so far the most successful generative AI models are trained to produce content (text and images) and write code, Gartner researchers predict that by 2025 more than 30% of all new drugs and materials will be systematically discovered using generative AI techniques. With the pace AI companies have taken governing the technology, these assumptions are likely to come real. 

So, how can enterprises extract real value from implementing GenAI, and what’s the practical potential of these initiatives? Let’s figure out the promises and limitations of the technology for businesses below in this post.

What is generative AI for enterprises and how it works

Generative AI uses advanced machine learning models capable of creating new and original content based on the data they were trained on. This ability to generate unique new content makes this technology a real game-changer. Applied at an enterprise scale, generative AI can significantly improve the operational side of any business, bringing them a strong advantage against competitors that still hesitate to approach innovation.

When it comes to the practical implementation of generative AI among large enterprises, the majority of use cases boil down to utilizing one of the popular AI models (for example, GPT-4, Claude, or Gemini) as a basis and training it on specific data available in-house.

Current state of generative AI adoption

Being one of the key pillars of intelligent automation, generative AI is a pillar of intelligent automation and a driver for organizations leveraging transformation to bring more value to customers and get better business results. Today, this technology underlies the world's well-known software products.

• Google introduced GenAI-powered features to upgrade their digital services. Docs users can now use AI-based suggestions to write job descriptions, brainstorm, proofread, and rewrite texts, while Gmail can formalize notes into polished messages and summarize long email threads. Google Sheets, Slides, and Meet are the next to get smart updates. 
• Microsoft introduced Copilot, an AI-based intelligent assistant that provides developers with code suggestions in real-time. It’s also used to help Microsoft 365 suite users create emails, summarize discussions, create and edit text drafts, and analyze documents to find relevant content and add it to online presentations.

Among others, there are more key IT market players investing in researching the potential of generative AI to bring their businesses to the next level.

• AWS announced they are using generative AI under the hood of Bedrock, a service that allows AWS clients access pre-trained models to build their own apps capable of generating texts, images, and audio.  
• Meta is testing text-based AI tools for WhatsApp and Messenger and plans to use generative AI to create tailored ads for different audiences.
• IBM is holistically exploring the potential of foundational models for scientific discovery as a part of IBM Research division initiatives.

The potential of generative AI for enterprises has been proved by market giants integrating the technology to power up their products and services. Investments in this technology are already paying off, increasing the speed of development, boosting brainstorming processes, and transforming workplaces. It’s just a matter of time how soon it penetrates all fundamental tasks and workflows

Dmytro Ivanov, ML Engineer at Trinetix

What are the benefits of generative AI for enterprises?

Most enterprise leaders have already recognized that generative AI is a game-changer. Among the 500 executives taking part in the Salesforce survey, the majority mentioned that the technology is likely to improve customer service, help organizations take advantage of data, and improve operational efficiency. 

We asked Trinetix automation practitioners to extend and summarize the benefits enterprises can get by making generative AI a part of their digital transformation agenda. 

• Improve operational efficiency and eliminate the risk of human error by reducing manual and paperwork 
• Reduce development costs by accelerating the speed of engineering and content creation
• Get better ROI and achieve predictable results by leveraging data-driven decision-making
• Improve employee satisfaction by modernizing and digitizing legacy processes and documentation
• Enhance value proposition by stimulating brainstorming and ideation processes

Generative AI helps organizations establish document management processes integrity, which makes a huge part of success for enterprises. Combined with optical character recognition (OCR), machine learning algorithms can be used to extract data from different file formats (pdf, csv, png, jpeg) and generate new common-format data sources.

Dmytro Ivanov, ML Engineer at Trinetix

Let’s have a deeper look at the impact generative AI is having on different business areas.

How generative AI transforms businesses

Since we are still in the early artificial intelligence era, a number of generative AI applications are yet to be discovered. At the same time, GenAI technology has already made part of digital workplaces in key domains such as marketing, education, consulting, and even healthcare. However, there are more generative AI examples across industries.

As of 2024, there are some key functional business areas where generative AI has already proved to be effective and areas where the use of the technology is less predictable yet 100% worth looking at.

Improving customer service

Large language models (LLMs) that underlie generative artificial intelligence are used to train customer care agents, automate work in call centers, notify supervisors about cases that require special attention, or generate insights into consumer behavior.

Today a number of enterprise companies introduce AI-powered chatbots and virtual assistants that suggest responses to customer inquiries, provide agents with recommendations on the next steps, and help summarize cases with long customer history. 

Streamlining software engineering

Software developers make use of generative AI coding tools to automate some repetitive tasks like testing, get natural language prompts into coding suggestions, and review code in a more efficient way.

Such a change dramatically decreases the time for software development, and as a result, helps enterprises save their budgets. In addition, by using GenAI tools, junior engineers can learn faster as, in fact, they get a personal digital mentor.

Automating marketing communications

From ChatGPT and ​​DALL-E to a number of AI copywriting and tools used for creating tailored brand messages, generating images, and writing content briefs, marketing was one of the first business areas to adopt innovation.

Marketers use GenAI to craft product names, create email sequences to use for A/B testing, solve key SEO tasks like suggesting keyword variations or topic clusters, and create social media posts and content briefs—and these are just a few of the AI applications known so far.

Enhancing creativity with generative design AI

Design is another area empowered by AI. Together with deep learning models for image synthesis, artists can benefit from AI solving complex design tasks. The latter is a case of generative adversarial networks (GANs), specific neural network architectures that allow artists to translate sketches into immersive two- and three-dimensional images. 

Graphic designers are experimenting with deploying popular generative AI tools to help them diversify and simplify various creative processes. Adobe, for example, announced they leverage GenAI to help Photoshop users automate time-consuming design processes like compositing images into backgrounds.

Providing next-gen HR experiences

Generative AI has the potential to reimagine employee learning and onboarding by generating useful insights into a company’s data and processes. It also helps to automate some recruiting processes like reaching out to potential candidates and synthesizing questions for introductory calls & technical interviews. 

Some enterprise companies are already using AI-enabled chatbots that use generative models trained to provide information on a particular employee, their role, department, and work anniversary date. This allows users of HR management systems to save lots of time they would have to spend on manual searching.

Transforming logistics: AI predictive modeling for enterprises

In logistics and supply chain management, generative AI technology plays a crucial role in anticipating demand, optimizing operations, and mitigating risks. By analyzing historical data and market trends, generative AI models can predict future demand for products and materials, enabling companies to plan more accurately and efficiently. Additionally, generative AI algorithms can optimize transportation routes and schedules, minimize inventory levels, and identify potential disruptions in the supply chain, such as weather events or labor strikes.

As a result, companies can proactively adjust their supply chain strategies to ensure smooth operations and timely deliveries. The technology empowers businesses to innovate in their logistics processes, such as implementing dynamic pricing strategies or developing sustainable transportation solutions. This not only improves operational efficiency but also enhances customer satisfaction and strengthens competitive advantage in the marketplace.

Introducing smart finance management

Enterprise financial management is a pain point even in mature organizations. With lots of data still stored in paper format or spreadsheets, it takes hours to scan the information and come up with the necessary calculations and precise conclusions.

Generative AI allows organizations to leverage a data-driven approach and accelerate digital transformation. Training models to recognize any discrepancies in financial data can help businesses significantly decrease operational costs and contribute to efficient and modern digital workplaces.

Bringing sustainability compliance

Together with the functional changes generative AI brings to businesses, comes the switch to a more sustainable global economy. The total amount of paper consumed worldwide hit 408 million tons in 2021. With deforestation being one of the key drivers of climate change, sustainability concerns are definitely growing for a reason.

Enterprises should make a move to a greener future by building sustainable workplaces. Achieving ESG compliance has become a priority of top global employers, including our Fortune 500 clients. Generative AI tools used for brainstorming, accounting, and financial management help enterprises comply with related regulations by reducing paperwork and digitizing legacy records.

The future of enterprise generative AI

In the future, GenAI technology is likely to have even more impact on business, and the enterprise landscape in particular. Here are a few potential use cases we are likely to witness maybe even sooner than one can imagine:

• Healthcare imaging and diagnosis

AI-powered image generation and analysis tools can assist healthcare professionals in diagnosing medical conditions and interpreting medical images such as X-rays, MRIs, and CT scans.

• Automated video content creation 

Generative AI can be used to automatically generate video content for marketing campaigns, social media, and entertainment purposes. This includes creating personalized video ads, video summaries of articles, and even generating entire virtual scenes for movies or games.

• Virtual try-on and customization 

Generative AI can enable virtual try-on experiences for fashion and cosmetic products, allowing customers to see how items would look on them before making a purchase. It can also customize products based on individual preferences, such as personalized clothing designs or customized furniture.

Barriers to generative AI adoption for enterprises

Despite all the benefits and groundbreaking changes the technology brings to businesses, 99% of senior executives still have technical and ethical concerns about its adoption as of 2024. This is absolutely justified, as no innovation comes without barriers.

Among the limitations of generative AI adoption by enterprises, the below ones cause the majority of C-suite executives’ hesitations.

• AI implementations come with severe threats to data security. When it comes to enterprises, it’s often about the data under NDA to be shared with GenAI providers.
• Some generative AI models are large and resource hungry. Running them requires much computing power and GPU enablement, which are often unaffordable even for enterprises. 
• Having insufficient data collected will not allow organizations to get satisfactory results from training the model.
• Lack of digital dexterity among employees is likely to prevent AI adoption and use in the organization’s daily routines.
• Implementing AI is impossible without qualified ML Engineers and Solution Architects who can integrate the technology into the company’s current tech stack and tailor it to specific business needs.

In reality, businesses can address each of these challenges by finding the right way to adopt innovation.

How can enterprises approach generative AI?

While the concerns about generative AI models being resource hungry and unsafe to us are true, the main mistake businesses can make approaching the implementation is to assume that simply taking a model and fine-tuning it using the data available is enough to get results.

Dmytro Ivanov, ML Engineer at Trinetix

Considering the implementation of generative AI to achieve better business results, it’s important to make it a part of your general transformation strategy. Only establishing an end-to-end approach and ramping up with skilled subject-matter experts can help you predict the outcomes and evaluate the impact of technology enablement.

At Trinetix, we have years of experience transforming enterprise businesses with the help of cutting-edge technologies at our back. Approaching AI solutions development, we use a strategic, discovery-first attitude and move “from simple to complex” to deliver predictable business-oriented results.

Defining objectives and forming the product vision

Before diving into generative AI adoption, companies should clearly define their objectives and form a cohesive product vision. This involves identifying specific business problems or opportunities that technology can address. For example, enhancing customer engagement with the help of personalized recommendations or optimizing operational efficiency through AI-enabled automation. This will allow organizations to ensure that their efforts contribute directly to strategic outcomes and ROI.

Collecting enterprise data needed to achieve the objectives

Successful generative AI implementation hinges on access to high-quality data. Enterprises should prioritize collecting and organizing relevant datasets that align with their defined objectives. This includes customer data, product information, market trends, and any other proprietary or external data sources necessary for training and refining AI models. To ensure robust and ethical AI deployment, data collection should emphasize quality, completeness, and compliance with data privacy regulations.

Enabling secure cloud storage and management

Generative AI applications require secure storage, management, and processing of data. Therefore, to make generative AI actionable, enterprises should invest in cloud solutions that offer robust security measures, data encryption, and compliance certifications (e.g., GDPR, HIPAA). Cloud platforms like AWS, Azure, or Google Cloud provide AI-specific services and infrastructure that facilitate seamless data storage, access, and integration across organizational functions.

Evaluating service providers & choosing an appropriate generative AI model

Selecting the right service provider and AI model is critical for the success of generative AI initiatives. That’s why before embarking on its implementation, companies should first evaluate potential AI vendors based on their expertise, industry experience, track record of successful deployments, and scalability of solutions. When choosing an AI model, we recommend minding the specific use case, compatibility with existing infrastructure, customization capabilities, and the ability to handle enterprise-scale data volumes and complexities.

Adapting and fine-tuning the model according to the objectives

Once an AI model is selected, it’s essential to focus on adapting and fine-tuning it to align with defined objectives and meet specific business requirements. This iterative process involves training the model with relevant datasets, refining algorithms based on feedback and performance metrics, and incorporating domain-specific knowledge to improve accuracy and effectiveness.

Testing and deploying the model in production 

Thorough testing is essential to validate the functionality, reliability, and performance of the generative AI model before deployment in production environments. Enterprises should conduct rigorous testing phases, including unit testing, integration testing, and user acceptance testing (UAT) to identify and resolve any potential issues or discrepancies. Once validated, deploying the AI model involves integrating it into existing workflows and systems while ensuring minimal disruption and maximum efficiency gains.

Measuring the results using industry benchmarks

Once the solution is deployed, we recommend establishing clear metrics and benchmarks to assess the impact and effectiveness of the generative AI solution. Key performance indicators (KPIs) may include ROI, customer satisfaction scores, operational efficiency improvements, and revenue growth attributed to AI-driven initiatives. Regular performance monitoring and analysis against industry benchmarks allow organizations to gauge success, identify areas for optimization, and justify ongoing investments in generative AI technology.

By following these comprehensive steps, enterprises can effectively navigate the complexities of generative AI adoption, maximize ROI, and achieve sustainable competitive advantages in their respective markets.

As a trusted technology partner of A-list enterprises, we know the real outcomes this approach brings in practice and would like to share some of our project highlights below.

Implementing AI for large enterprises: Trinetix experience

Our client, a strategic advisory firm and a Fortune 100 member, sought an intelligent approach to processing large volumes of crucial business-related information. The competitive corporate landscape as well as the necessity to mitigate major reputational risks at an enterprise scale required ultimate accuracy and time efficiency. 

To help the company streamline manual data processing and provide them with a strong competitive advantage, Trinetix developed and implemented an automated data engine capable of classifying information coming from internal sources, analyzing it, and extracting valuable business-related insights. Equipped with generative AI capabilities for enhanced precision, the system allowed our client to develop adaptability to market changes and revolutionize decision-making at scale.

Operationalizing intelligent solutions is a game-changing experience for enterprises. If you feel like bringing your business a competitive edge with generative AI, let’s chat about your objectives and challenges and define the right way to get started

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