Infographic Best Strategies for Building AI Infrastructure
By Insight Editor / 13 Apr 2023 , Updated on 14 Jun 2024 / 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.
Accessibility note: The infographic is transcribed below the graphic.
Choose a strategic partner. Insight offers comprehensive support to build your AI infrastructure quickly, with less risk.
Infographic text included for screen readers:
From networking to compute to storage, Artificial Intelligence (AI) requires a different kind of infrastructure than traditional IT.
While 92% of IT decision-makers report their enterprises are leveraging or testing AI/Machine Learning (ML) technology, only 23% have optimized or automated it through the use of best 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 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.
Did you know? 40% of IT decision-makers rank cultural misalignment/disconnects between IT operations and development teams as the #1 challenge inhibiting innovation at their organizations.
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 models 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
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. (June 2024). The Path to Digital Transformation: Where IT Leaders Stand in 2024. Slide 6. Commissioned by Insight.
MarketPulse Research by Foundry Research Services. (June 2024). The Path to Digital Transformation: Where IT Leaders Stand in 2024. Slide 13. Commissioned by Insight.
