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.
- Current state of generative AI adoption
- What are the benefits of generative AI for enterprises?
- How generative AI transforms businesses
- Barriers to generative AI adoption for enterprises
- How can enterprises approach generative AI?
Current state of generative AI adoption
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. It uses advanced machine learning models capable of creating new and original content based on the data they were trained on. 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
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.
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 2023, 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.
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.
Learn how ChatGPT can streamline document management for enterprises
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.
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 2023. 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.
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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.
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.
Get maximum value from generative AI with a tailored solution
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.
- Collecting enterprise data needed to achieve the objectives.
- Enabling secure cloud storage and management.
- Evaluating service providers & choosing an appropriate generative AI model.
- Adapting and fine-tuning the model according to the objectives.
- Testing and deploying the model in production.
- Measuring the results using industry benchmarks.
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.