The evolution of artificial intelligence can be divided into 2 phases: before GenAI and after GenAI. There is no denying that GenAI is the technology that has influenced various industries with practical and impactful applications. So, now, three-quarters of global enterprise CEOs consider generative AI implementation a key strategy for gaining a competitive edge in the market.
In this article, I will explore practical GenAI use cases and how enterprises can leverage this technology for business improvement.
Generative AI: definition, limitations, and benefits
Generative AI is a branch of artificial intelligence that utilizes unsupervised and semi-supervised machine learning to produce new content from existing data.It relies on neural networks, including Generative Adversarial Networks, Variational Autoencoders, and transformer architectures to perform specific tasks. It also includes LLMs in the transformer architecture to interpret human language inputs to generate fresh content.
To describe generative AI's potential for enterprise adoption as promising would be an understatement.
- The global GenAI market is projected to grow at a CAGR of 27.02%, reaching $118.06 billion by 2032.
- Statista predicts 46% AI adoption by 2025.
- Gartner expects 30% of large organizations' outbound marketing messages to involve generative AI by 2025.
However, enterprise-level adoption faces such challenges as:
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Security concerns. AI implementations present serious data security threats, particularly when dealing with data under non-disclosure agreements shared with GenAI providers in enterprises.
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Data collection hurdles. To fine-tune a selected Large Language Model (LLM), organizations must gather sufficient data. However, only 13% of companies have a practical data strategy in place.
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Data quality issues. Collected data often proves incomplete, low-quality, or irrelevant to ongoing business objectives, hindering the successful implementation of Generative AI in enterprises.
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GPU infrastructure costs. Resource-intensive generative AI models demand significant computing power and GPU enablement, which can be financially taxing for some enterprises.
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Shortage of qualified ML engineers. Implementing AI requires input from qualified ML engineers and Solution Architects who can integrate the technology into the company’s information architecture and tailor it to specific business needs.
However, as more enterprise leaders implement generative AI and the technology keeps evolving to meet growing business requirements, the benefits of adoption outweigh its pitfalls. This IBM surveydemonstrates how actively global CEOs are adopting AI right now.
5 generative AI Use Cases for Enterprises
As of 2023, the majority of generative AI use cases are found in marketing and advertising, technology, and consulting.
Streamlining document management
9 out of 10 large-scale businesses usually face challenges in their transformation plans due to inadequate data management strategies. It is often attributed to the extensive enterprise data stored in outdated systems and formats, complicating retrieval, management, and alignment with business objectives.
By combining Generative AI with computer vision, organizations can efficiently process and analyze various types of historical data, uncovering crucial dependencies. This streamlined approach enhances the way enterprise employees interact with data, resulting in quicker issue resolution and improved business integrity.
Stimulating data-driven demand forecasting
Generative AI significantly impacts global logistics and supply chains by utilizing LLMs to analyze historical data, predict future demand, address loading challenges, and optimize stock replenishment processes.
With Generative AI models continuously learning from dynamic datasets, companies in various industries, from retail to manufacturing, can leverage them for accurate demand forecasts, identifying upcoming market trends, and analyzing consumer behavior to enhance business performance and ensure customer satisfaction.
Enhancing workplace productivity
Leveraging generative AI can enhance operational efficiency and scale up employee productivity for companies. LLMs, learning from distinct business knowledge, enable intelligent assistance for employees handling corporate and customer information. These tools prove valuable for account executives and those managing tasks like processing invoices, tax reports, insurance quotes, and other industry-specific data.
Facilitating strategic planning
As generative AI capabilities advance, adopting the GenAI platform assists enterprises with decision-making, problem-solving, and strategic planning, ensuring comprehensive business enablement and letting market leaders build and maintain their competitive advantage.
Providing risk prediction
GenAI's adaptability in analyzing extensive datasets finds versatile applications in risk management. For instance, it made an impact by aiding financial professionals, optimizing healthcare strategies, enhancing cybersecurity, facilitating disaster preparedness, minimizing manufacturing downtime, managing energy grid stability, and improving road safety.
How to get started with generative AI?
The core of implementing generative AI at an enterprise scale involves a practical and strategic approach to its deployment and integration into the existing digital ecosystem.
Here are the key steps to kickstart the generative AI journey:
- Define objectives and form the product vision.
- Collect enterprise data needed to achieve these objectives.
- Enable secure cloud storage and data management.
- Evaluate service providers and choose an appropriate generative AI model.
- Adapt and fine-tune the model according to the objectives.
- Test and deploy the model in production.
- Measure the results using industry benchmarks.
Every successful generative AI use case begins with a comprehensive and technically grounded implementation roadmap that includes these steps. Therefore, executives and decision-makers who are only getting started with exploring GenAI adoption are recommended to cooperate with trusted digital partners who have vetted AI architects and experienced teams in their talent pool.
Also published here.