Five pillars for practical GenAI implementation
Is 2025 the year we seriously transition from GenAI hype to GenAI results? Recent research suggests so, especially for Britain, which could see an almost doubling of economic growth over the next fifteen years thanks to this advanced technology.
However, every technology leader knows that they cannot predict every advance on the horizon, even as they recognize their responsibility to plan for the future as much as possible. Across all industries, leaders are faced with the choice to take the leap and invest in technologies such as AI tools to future-proof their businesses. But without the right strategy and adoption plan, you can find yourself adrift without a clear idea of where you’re going.
Walking this tightrope requires a pragmatic approach, using the best available tools while maintaining flexibility and control. Practical GenAI implementation is not about rigidly following one path. Instead, it’s about creating an AI ecosystem that adapts and evolves with your business needs. That could mean choosing cross-platform solutions to avoid vendor lock-in, embracing open source to benefit from flexibility and transparency, adopting hybrid and multi-cloud strategies to create the best environment for your AI workloads. guarantee, or whether you should focus on tailoring your AI solutions. .
CTO at Dell Technologies UK.
Pillars for practical GenAI implementation
By working with technology providers, customers can ensure they can harness the power of AI – tackling the complexity, risks and costs of diving into and supporting AI now and in the future. By offering flexible consumption models, an end-to-end AI-optimized IT infrastructure portfolio, an open ecosystem of deep partnerships with other leading AI companies, and a commitment to open standards, they can support a GenAI implementation that aligns with the unique needs of a company. needs, risk tolerance and long-term vision. In short, they can help ensure a strategy that is not only groundbreaking, but also pragmatic and sustainable.
We can do that for our customers thanks to the lessons we’ve learned during our own AI journey. By implementing AI within our own operations, we have gained first-hand experience of its challenges and opportunities, giving us a deep understanding of what works and what doesn’t in practice. Our ‘customer zero’ approach, where we become our own first and best customer, ensures that our AI solutions are not just theoretical concepts, but are based on practical aspects, refined through real-world experience and ready to deliver tangible results for our customers to deliver.
From that practical side, we’ve developed these five guiding principles to help you more quickly and efficiently deploy AI technologies that will serve your business today and prepare you for your business tomorrow. These pillars for the practical implementation of GenAI are a testament to our own journey and our commitment to helping customers simplify complex technology.
1. Company data is your differentiator
Never lose sight of the fact that your data is a gold mine of insights, and unlike your competitors, you have exclusive access to it. You have a wealth of customer, operational and market data at your disposal; information that reflects your company’s unique journey and expertise. This data is the secret to success in the AI race.
By building on pre-trained models and customizing them with your proprietary data, your differentiator, you can gain a competitive advantage through deeper customer insights (AI can analyze your customer data to uncover hidden patterns and predict future behavior), proactively risk management (AI can detect fraudulent transactions in real time by analyzing customer patterns and flagging anomalies) and improved decision making (AI can analyze vast amounts of data to identify trends, predict demand and optimize pricing strategies – giving you the insights you need you have to smarter, faster decisions).
2. Respect the gravity of data
While data can be a treasure trove, it is never all found in one pot. Data is highly distributed, with most of it on-premises and more than 50% of enterprise data generated at the edge.
For data to be effective, it must be close to applications and services that depend on it for efficient processing and analysis. It’s better to give in to ‘data gravity’ and bring AI to the data (where the majority is on-premises) rather than moving enterprise data to available computing resources. Most organizations find it more effective and efficient to train and run AI models on-premises to minimize latency, reduce costs, and improve security. To turn data with AI into actionable insights, often in real time, a combination of on-premise, edge and cloud deployments is critical. For this reason, 66% of UK decision makers prefer an on-premise or hybrid approach to using and purchasing AI.
3. Right-size your AI infrastructure
There is no one-size-fits-all approach when it comes to AI. I’ve witnessed customers across multiple industries, in organizations of all sizes, deploy their AI in countless ways: from locally on devices and at the edge to massive hyperscale data centers. Not all models are large and not all AI workloads run in a data center. Or in the cloud. To avoid massive over- or under-supply, it is important to right-size the AI solutions you use for your use case and requirements. So analyze your use cases and goals to determine the most suitable infrastructure and model types.
4. Maintain an open, modular architecture
Equally important is the realization that the AI landscape is constantly evolving and that no one can predict its future course. This means that a rigid, closed system can quickly become outdated. Therefore, maintaining an open, modular architecture will be critical to helping companies adapt to the rapid changes in AI technologies and avoid being locked into outdated or inflexible architectures.
AI/GenAI workloads are a new class of workloads and require a new class of open, modern innovation that spans the entire AI domain: data layers and lakes, compute, networking, storage, data protection, and AI software applications. But it’s entirely plausible, if not likely, that new GPU infrastructure, algorithmic infrastructure, or inventions could emerge in the future that companies will have to adapt to. The biggest mistake you can make today is betting on and sticking with a closed, proprietary, one-dimensional AI system that is not flexible.
Open standards AI tools provide flexibility, transparency, and a vibrant community for support and innovation. By integrating open standards solutions into their AI strategy, companies can avoid dependency on a single vendor and tailor tools to their specific needs.
5. Create a thriving AI ecosystem
No single vendor can solve every AI challenge; collaboration is key. AI is a composite of many technologies, intellectual capabilities and services that companies must combine to succeed. Make sure you embrace vendors that enable an open ecosystem of partners, from major AI players like Microsoft to silicon vendors like NVIDIA and Intel to open source leaders like Hugging Face.
Open ecosystems provide equal opportunity across the technology ecosystem, supporting the creation of new GenAI breakthroughs and giving customers greater access to innovation and flexibility. Access to open models and technologies can accelerate progress and solve problems worldwide, fueling a global ‘innovation engine’ across all corners of the industry, from individual developers and startups to public sector and corporate organizations.
A real-world approach for real-world results
Successfully navigating a new landscape almost always requires a pragmatic approach that balances excitement with realism, preparation and careful execution. Capturing value from new technologies requires creating strategic roadmaps, and when it comes to AI, the preparation, quality and storage needs of the data that feed it are especially important. Don’t get carried away by the feeling that you have to transform into an AI powerhouse overnight. Start by identifying a specific, achievable goal that has the ability to generate business ROI, and strengthen the path to success with a clear vision and the right partnerships.
We’ve put together a list of the best free cloud storage.
This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro