Infographic Best Strategies for Building AI Infrastructure
By Insight Editor / 13 Apr 2023 / Topics: Data and AI
Whether you’re starting from scratch or with existing infrastructure, building Artificial Intelligence (AI) requires a different kind of framework than traditional IT. View this infographic for key insights into top concerns, common challenges and expert strategies for building AI infrastructure.
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Choose a strategic partner. Insight offers comprehensive support to build your AI infrastructure quickly, with less risk.
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From networking to compute to storage, Artificial Intelligence (AI) requires a different kind of infrastructure than traditional IT.
While 85% of companies are already using AI to drive business insights, 57% have only just started their journey or are looking to mature their practices.
Accelerate time to value with expert strategies for building AI infrastructure, whether you’re starting from scratch or with existing infrastructure.
How to: Build from scratch.
What’s the best front door?
Embedded AI services
Embedded AI services that are part of Platform as a Service (PaaS) or Software as a Service (SaaS) that you already have allow for easier add-ons because the infrastructure is already provided and supported by the service provider. Extending the capabilities that are provided via these services — or creating new ones that integrate with these services — is an easy way to take advantage of AI capabilities without a lot of business resource commitment.
Education & understanding
Missed expectations are one of the biggest reasons that AI adoption can fail. That is why it is imperative to develop a clear, data-driven understanding of what AI and Machine Learning (ML) can do to meet the needs of your organization — and how. With this understanding, you can leverage the transformative nature of AI solutions to develop an organizational culture that embraces innovation and adoption of impactful technology.
AutoML techniques
You don’t necessarily need to hire a team of data scientists to build your AI infrastructure. If you have your organization’s data and approach in order, then automated machine learning (AutoML) techniques can automate the tasks of applying ML to your business problems.
What do teams need to keep in mind?
- Don’t lose sight of the business objective: The business objective should drive the process and the interim decisions that are made.
- Take note of lessons learned as you go through the process and adapt accordingly.
- Set yourself up to be agile through things like domain-driven design, as a service and decoupled architectures.
- Remember: Building AI infrastructure is a journey, not a race.
- Cutting corners will only add to technical debt, while a principled approach with iterative releases will move the ball forward and provide momentum to your organization.
How to: Build with existing infrastructure.
What’s the best front door?
Measure your assets based on your business goals.
First, understand exactly what your business goals are and what/how AI can help your organization achieve those goals. Then, perform an inventory of your existing data assets and data infrastructure. From here you can determine any gaps between what you have and what you need, prioritize the gaps and apply the 7 R’s to your inventory with attention to your business goals.
What are the 7 R’s?
- Rehost
- Refactor
- Rebuild
- Replace
- Retire
- Rearchitect
- Restore
What do teams need to keep in mind?
- It takes time to uncover existing value in your data estate and infrastructure.
- Be principled in the approach, take your personal bias out of the equation and measure your assets based on your business goals.
- Follow the same design principles as if you were building from scratch.
- It's okay to retire, replace or refactor components of your environment if they are not providing value and/or the technical debt outweighs the value or the desire to make additional investments.
Common obstacles to building AI infrastructure
Although AI infrastructure can be incredibly beneficial, various roadblocks can make the building process challenging and even discourage organizations from completing their journey. At Insight, we have trained experts that collaborate with clients to solve these challenges and more:
- Identifying business goals, data estate value & data infrastructure
- Aligning best practices & efficiencies in the target environment
- Ability to support
- Identifying & locating data assets
- Security, compliance & governance
- Discipline to not create “new” technical debt
DID YOU KNOW? 86% of organizations have been impacted by technical debt over the past 12 months.
Choose a strategic partner that understands AI infrastructure.
Our experience spans the entire scope of industry technology challenges, empowering you to build your AI infrastructure more quickly, with less risk and with comprehensive support. Choose a strategic partner that understands AI infrastructure.
Sources:
MarketPulse Research by Foundry Research Services. (February 2023). The Path to Digital Transformation: Where Leaders Stand in 2023. Slide 11. Commissioned by Insight.
MarketPulse Research by Foundry Research Services. (February 2023). The Path to Digital Transformation: Where Leaders Stand in 2023. Slide 16. Commissioned by Insight.
