Plan for AI deployment success
AI is transforming businesses, but it also brings infrastructure and security hurdles. Ready to unlock its full potential for your organization? Let's dive into the key considerations to ensure you get the most value from your AI deployment.
Ready to deploy AI? Think ahead!
Discover how your organization can stay ahead by considering every aspect of AI deployment. Focus on building trust, establishing strong governance, fostering a data-driven culture, and assembling skilled teams.
How data-savvy is your decision making? A data-driven culture makes AI integration that much more successful.
Structure
Build a dream team, invest in top-notch tools, and keep the collaboration vibes strong to get the most out of your AI.
Stay ahead of the curve with continuous education and skill building for data management and hygiene, AI development, query engineering, and data science. And keep up with smart risk management and strategic planning to ensure your AI projects hit the mark and align with your goals.
Can your AI solutions grow and adapt? Make sure they can scale across different domains and handle changing requirements and workloads.
Consider the type of scenario that will work best for your organization. For instance, is cloud-hosted best or on-premises? Do you want a full-stack solution or a more specialized best-of-breed solution?
Audit your current infrastructure—servers, storage, switches, and more—to spot investment opportunities. Modernize your resources to meet your AI goals. Choose platforms that play nice with your existing systems and ensure you have the power to handle high computational demands.
Choose between cloud, on-premises, or hybrid solutions according to your data's sensitivity, cost, and scalability needs.
Protect your data with encryption and access controls, ensuring compliance with privacy regulations.
Set clear policies for data accessibility, usage, and ownership. Define roles and responsibilities for effective data management, including accountability for data hygiene and proper data formats.
Make sure all necessary data is available for AI model training and deployment, including historical data, real-time streams, and other relevant sources.
Smoothly transition to new systems or processes to minimize disruption and resistance.
Determine if your AI needs real-time or near-real-time processing. Check network bandwidth and latency to ensure quick and efficient data transmission. High latency can bottleneck distributed AI systems.
Get stakeholders involved at all levels to ensure systems and tools meet their needs and enhance skill sets, rather than limiting career opportunities.
Focus on finances: Balancing CapEx versus OpEx for maximum ROI
Capital expense
Capital expense
- Infrastructure costs: Hardware, software, and deployment expenses.
- Data transfer and migration: Address data locality issues and transfer fees.
- Storage costs: Significant in cloud environments where vast data volumes are needed for AI training and compliance.
Operational expense
Operational expense
- Integration and maintenance: Ongoing costs for energy and workforce.
- Regulatory compliance: Ensuring adherence to standards and security requirements.
- Professional services: Implementation, support, and consulting fees.
Getting the most for your money
Getting the most for your money
- Right-size for AI: Pay for exactly what you need, with room to grow.
- Modernize infrastructure: Sustainable upgrades can boost efficiency and lower costs.
Where should you deploy AI?
Maximize performance and business benefits by choosing the right deployment strategy. Weigh costs, performance, security, and regulatory needs to find the best fit for your organization.
Keeping it in-house
Deploying AI on-premises gives you greater control over hardware, data security, and compliance. This option requires significant investment in infrastructure and maintenance.
Sky's the limit
Cloud deployment offers scalability, flexibility, and cost-effectiveness, providing easy access to vast computational resources without the need for on-site infrastructure. This works well for organizations that don't need customization or specific types of data protection.
The best of both worlds
A hybrid model combines on-premises and cloud resources, offering a balance of control and scalability. Ideal for organizations with specific data privacy or latency requirements.
Closer to the action
Edge computing puts computing and storage closer to where resources are needed, reducing latency and enhancing privacy and reliability by processing data locally. Perfect for remote or mission-critical applications.