Artificial intelligence (AI), especially generative AI (GenAI), is on every IT leader’s mind.
GenAI is the fastest-growing discipline within the $308 million AI market and will reach $143 billion by 2027. IDC also forecasts that GenAI will add nearly $10 trillion to the global GDP.
Organizations must explore opportunities to incorporate GenAI into their operations to drive innovation and stay relevant. But amidst the fast-evolving technologies and uncertainties in this new frontier, how can you chart a clear path to optimize your investment and mitigate risks while shortening time to value?
Successful GenAI implementation isn’t just about acquiring the latest technology. You must also identify the best opportunities and support your business vision with the right solutions. Let’s explore the benefits of using GenAI in business, how to overcome the challenges of GenAI adoption, and how to combine the best technology and strategy to maximize your ROI.
What is GenAI?
GenAI is a subset of AI that focuses on creating new content, such as text, images, music, code, or video, based on patterns from existing data. It uses deep learning models like neural networks to generate novel content by predicting and synthesizing outputs that mimic human creativity.
While AI can process large amounts of data quickly and support a broad range of applications like language translation, data analytics, predictive modeling, and image recognition, GenAI has a much narrower focus. Yet, its creative and problem-solving capabilities will unlock unprecedented insights and productivity.
Although most businesses already use AI-powered tools to aid analytics, automation, and operations, GenAI implementation is more complex. It requires extensive efforts and a solution tailored to your organization and data infrastructure. So, is it worth the investment?
The Benefits of GenAI in Business
GenAI is already changing how organizations work, but we’re just scratching the surface. Here are the top reasons why companies are hopping onto the GenAI bandwagon:
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Enhance Efficiency: GenAI takes process automation to the next level, aiding content creation (e.g., reports, product descriptions, and code), data analysis, and customer support to save time and reduce errors while freeing human workers to focus on high-value, strategic tasks.
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Save Costs: Automation helps streamline workflow and lower labor expenses associated with repetitive tasks. Companies can also use GenAI to optimize logistics, supply chain operations, resource allocation, and other business processes to lower overheads.
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Improve Customer Experience: GenAI tools help you leverage customer data to deliver highly personalized experiences, such as tailored product recommendations and individualized marketing campaigns. Additionally, chatbots offer round-the-clock support to provide quick responses to customer inquiries.
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Accelerate Innovation: GenAI analyzes vast amounts of data and provides insights to guide decision-making. You can also use these tools to help brainstorm new product ideas, vet design concepts, and assist in research and development to shorten time to market.
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Enhance Cybersecurity: You may use GenAI tools to monitor for vulnerabilities and detect potential security threats in real-time. You may also automate incident response processes to minimize damage from a breach and ensure compliance with regulatory requirements.
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Augment Creativity: While GenAI can’t replace human creativity, it can assist in brainstorming, content creation, graphic design, and marketing campaigns — offering ideas and perspectives that its human counterpart might not have considered.
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Improve Forecasts: GenAI analyzes large datasets quickly and accurately to provide insights into market trends, consumer behavior, and operational efficiencies. You may deploy predictive models to provide accurate forecasts and support proactive decision-making.
How To Overcome the Challenges of GenAI Adoption
GenAI implementation is complex. It requires a robust data architecture and extensive computing capabilities. Here are the top challenges organizations face and how to overcome them with the right tools and strategy.
- Hybrid Complexity: GenAI relies on massive amounts of data spread across multiple platforms and environments, including cloud, on-premises, and edge systems. A poorly designed hybrid infrastructure may lead to data fragmentation, complex workflows, and compliance issues.
➡️ Overcome this challenge: Empower data users with applications and frameworks to access, process, and integrate data and insights while maintaining security, compliance, and control. For example, you may deploy data orchestration platforms and use multi-cloud management tools to provide unified access and support data synchronization. - Interoperability and Enterprise Support: Open-source tools are widely used to build and innovate GenAI models. While they offer flexibility, cost-efficiency, and community support, they may have interoperability issues with enterprise systems and limited support for large-scale production deployments.
➡️ Overcome this challenge: Work with vendors that offer best-of-breed enterprise-grade versions of open-source tools (e.g., HPE’s Machine Learning Development Environment Software) for better support. Also, enforce open-source governance policies to manage security, updates, and dependencies to ensure a secure and stable AI infrastructure. - AI Model Adaptation: GenAI models are highly specialized, and most organizations must fine-tune pre-trained ones to meet specific business needs. AI’s rapid evolution means these models require frequent updates, yet new ones or updates may not be compatible with existing infrastructure.
➡️ Overcome this challenge: Facilitate ongoing evolution by identifying the best model and framework for your long-term business strategies. Implement a Machine Learning Operations (MLOps) framework to manage the AI model lifecycle. You may also explore AI model marketplaces to accelerate deployment for industry-specific use cases.
Legacy Systems
Traditional IT infrastructures and legacy systems are not optimized to handle the high computational demands and sizable data flows GenAI applications require. Outdated hardware, storage, and networking setups often can’t support large-scale data pipelines for model training and real-time inference.
Learn more about our HPE partnership and schedule a conversation to jumpstart your GenAI journey.
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