artificial intelligence Infrastructure refers to platforms that enable the development of intelligent applications that are predictive, self-healing, and require minimal human intervention. It plays a key role in the success of companies undergoing AI-driven transformation. As AI drives changes in workloads and operational goals, even the physical layer of the data stack requires changes in how raw data is managed.
AI is already influencing infrastructure design, including edge computing. In addition to the processor, the hardware is also optimized to support AI workloads. However, configuring hardware to handle AI effectively requires coordinated effort and a flexible approach. There is no one-size-fits-all solution for implementing AI infrastructure.
Aligning AI technology with business strategy
Technology alignment and AI business strategies play a key role in an organization’s success in an increasingly competitive global marketplace. Ensuring that technology choices and investments are aligned with strategic objectives is essential. This process involves aligning the organization’s vision, culture, processes and resources with the technical needs and opportunities of the market.
There is also the issue of “technical debt” to consider. Technical debt in software development refers to the cost incurred when choosing a simpler but less than optimal solution, resulting in additional rework and additional costs in the future.
A McKinsey survey of CIOs at financial services firms found that a significant portion (10-20%) of technology budgets allocated for new products is devoted to solving problems related to technical debt. It became clear. Additionally, CIOs estimate that technical debt accounts for 20-40% of the total pre-depreciation value of technology assets.
What is AI cleaning?
AI washing refers to marketing techniques in which vendors falsely claim that their products or services incorporate AI technology, even when the connection with AI is minimal or non-existent. This is akin to ‘greenwashing’ where companies make misleading or unsubstantiated claims about the sustainability of their products.
A study by London-based firm MMC Ventures, which surveyed 2,830 startups across Europe, found that 40% of those calling themselves “AI startups” had little or no AI capabilities. To avoid falling prey to AI washing, it can be helpful to consider the following:
- Does the vendor actually have the AI experience and resources needed for AI, such as data scientists, data architects, data engineers, etc.?
- Does the vendor have key data about their AI applications? Is this data coming from various sources?
- Can the vendor demonstrate that it has the right AI infrastructure, from the data center to the DevOps team?
- And finally, are the vendor’s AI capabilities aligned with the goals your organization is trying to solve?
Other questions
As innovation and technology advance, it is important to consider the following questions when making decisions: 1. Does this technology align with our mission and support our strategic direction? 2. Will this new technology integrate with our existing systems? 3. How much do we really need? 4. Will this technology fit my budget?
Addressing these questions will help organizations make an informed decision about adopting new technology that aligns with their mission, integrates with their existing systems, meets their needs, and provides a satisfactory ROI within budgetary constraints. can make decisions.
The value of AI infrastructure
An intelligent infrastructure is essential to harnessing the full potential of AI systems, especially as businesses recognize the value of integrating AI into their operations. AI infrastructure supports every stage of the machine learning workflow. By automating processes and leveraging AI, businesses can improve operational efficiency, reduce costs, and transform customer service, for example, through AI-driven chatbots.
According to reportlinker.com, the global AI infrastructure market is expected to reach $122.8 billion by 2028 with a market growth rate of 24.7% (compound annual). A survey conducted by IDC reveals that business leaders are increasingly recognizing the importance of AI-dedicated infrastructure to delivering meaningful value to their organizations.
Inadequate infrastructure has been identified as a leading factor in AI project failures, hindering progress in more than two-thirds of organizations surveyed. This highlights the critical role the right infrastructure plays in the success of AI initiatives.
When deciding on an AI platform infrastructure solution, companies need a clear understanding of the data life cycle in terms of AI models and the specific requirements of these models.
Before defining an AI infrastructure, here are some key data considerations that organizations need to know. Efficient management and storage of unstructured and semi-structured data. Challenges in collecting data from multiple sources. Complex processing for meaningful insights. High Performance Computing Platforms and Engines. High-performance computation of deep neural network models. Efficient storage and throughput.
Core components of AI infrastructure and technology requirements include well-organized data management and data architecture for collection, storage, transformation, distribution, and consumption. Ingestion involves collecting and labeling raw AI model data from static datasets or constantly changing sources.
Data storage needs to handle massive amounts of structured and unstructured data in scalable data lakes. Data preparation involves cleansing and transforming data in the data lake for AI model training. Data access ensures reliable access to AI systems’ structured data stores.
Next steps include feeding processed datasets to AI/ML tools, training and evaluating models using high-performance storage, and using a variety of ML models for specialized tasks. After training, AI models are used for scoring and prediction in production. Redundant data is stored on low-cost storage. Finally, inferences are made when the AI performs actions based on the analysis of the information received.
Aligning your business goals with the adoption of AI and related initiatives is critical. By doing so, companies can ensure that the technology they choose supports their overall goals and facilitates a smoother transition.
- The author, Prof. Mark Nasira, is the Chief Data and Analysis Officer for FNB’s Chief Risk Office.
- Read more articles by Mark Nasila on TechCentral
