Executing an AI strategy requires critical steps to achieve targeted business outcomes.
The next phase in AI’s sea change is at hand with several tech companies trumpeting themselves as “AI-first.” The allegiance to AI has kicked open the floodgates for a legion of fast followers.
Seventy-eight percent of organizations surveyed by Deloitte expect to increase their AI spending in the next fiscal year, with generative AI expanding its share of the overall AI budget.
AI is the latest in a line of technology trends, including shifts to cloud and mobile, that spurred the rise of digital services. Today AI runs on any digital device at virtually any location, from on-premises to the public cloud, out to the edge on industrial systems and AI PCs and workstations.
Whether your organization is racing to build out AI solutions or taking a more methodical approach, it needs a strategy and a process to help bolster employee productivity while better serving customers.
The following tips, adapted from this adoption guide created by Dell and NVIDIA, can help.
The AI Strategy Playbook
Setting the Strategy. Your AI strategy starts with establishing a North Star for AI goals that achieve business outcomes. Assess your current, “as-is” state, identifying gaps in skills and technologies required to drive AI, then build your roadmap for a desired “to-be” or future state. This roadmap must answer the question: What does good AI look like?
Then align business stakeholders, IT and functional leaders on use cases that address business goals. Successful GenAI requires partnerships between technology and people to ensure productivity while reducing repetitive tasks. As continuous stakeholder alignment is critical you’ll want to communicate expected outcomes early and often. Key to this is fostering experimentation, where employees view AI as a tool for growth and innovation.
Create Governance and Guardrails. Successful AI strategies address risks related to cybersecurity and data privacy, as well as ethical AI use. Adopt an AI governance framework that includes continuous risk assessment, as well as controls for data protection, cybersecurity, model transparency and bias detection. This will include the ability to monitor, manage and reconcile shadow AI, or unsanctioned tools that pose risks.
Right-size infrastructure. Your AI use cases require the right infrastructure to support them. Consider the hardware, including servers and AI workstations and PCs, to run the AI workloads that will fuel your use cases.
Factor in the resources required to operate different AI deployment stages and techniques (open-source models, retrieval augmented generation, fine-tuning and inferencing). Flexible, fit-for-purpose infrastructure serves as the foundation upon which AI can be safely and cost-effectively built.
Continuous Learning is Critical. Pervasive AI adoption requires a wash-rinse-repeat process of onboarding, education, training and skilling. This shouldn’t be a hard sell, as 47% of workers surveyed by Microsoft said that AI-specific skilling had become a priority for their organizations.
Install “AI champions” who will take the lead on training on new AI tools, using prompting tutorials, workshops and other training media. Curricula may vary by function (business stakeholders vs. technical users); the goal is to identify skills gaps, nurture new AI skills and cultivate lasting engagement. Solicit feedback and pivot off tools that don’t scale. As AI continues to grow and evolve, the only constant here is change.
Measure What You Manage. To nurture an AI strategy that evolves in lockstep with business demand and the pace of innovation, you need a rigorous approach to measurement. Create KPIs to track adoption rates, productivity gains and business outcomes tied to AI initiatives.
You’ll also establish regular review cadences to track progress and identify emerging needs. Then use feedback to improve training. Measuring your AI initiatives will also make it easier to tweak your AI strategy to align your business requirements with evolving technologies.
That Path to AI Innovation
To support this new era of AI running everywhere on everything all at once, there are many deployment options, from open-source LLMs running on premises to AI workstations and PCs at the edge.
No matter what your environment looks like, Dell and NVIDIA deliver the Dell AI Factory with NVIDIA, a modern solution suite and framework organizations can use to build and scale AI systems. The Dell AI Factory includes NVIDIA accelerated computing, software and networking technology, fueled by Dell servers and storage and professional services that provides a consultative approach to deploying AI.
Ultimately, being an early mover on AI is great, but it’s more important that you get your strategy right. Because AI best beats AI first.
Learn more about the Dell AI Factory with NVIDIA.
