part 2 of a 3-part blog series: gen AI technology
The race to AI — and especially gen AI — has started. Understanding your organization’s state of readiness for AI in terms of processes, technology, and people prior to starting any new projects is critical to success. In part two of this three-part blog series, I’ll lay out the AI-readiness dimensions of digital AI maturity that relate to the technologies needed to create and support AI applications. Be sure to read part one and part three of the blog series for the complete picture.
Early adopters of artificial intelligence (AI) and generative AI (gen AI) are destined to create a competitive edge in the market — but only if they proceed with a full understanding of the readiness of their people, technologies, and processes. While moving quickly is warranted, it’s important to take the time to assess your organization’s AI maturity and address any gaps before getting started. Being prepared allows you to start strong and scale new projects on a foundation of AI success.
A digital AI maturity framework is a helpful tool for assessing your organization’s AI readiness using five levels of maturity and 10 key dimensions. In the first blog in this series, I covered the assessment criteria for AI processes, which include your high-level AI strategy, target use cases, data management practices, planned performance metrics, and ethical considerations. In this blog, I’ll lay out the two dimensions that speak to your technological readiness: infrastructure and research and development (R&D).
technology infrastructure.
At the heart of AI readiness lies a robust technology infrastructure capable of supporting complex and compute-intensive data analytics and machine learning models. Use the maturity levels below to assess your current state, noting how closely your AI capabilities align with your AI aspirations.
emergent
- Infrastructure has little to no specific provisions for AI.
- Hardware, software, and data resources are limited or outdated.
- IT lacks cloud computing capabilities or advanced data processing tools.
experimental
- Essential AI tools and platforms have been added but are not fully integrated.
- Adoption of cloud services and data processing infrastructure are in their initial stages.
- IT is beginning to address the data storage and computing power needs for AI, but with limited scalability.
proficient
- Robust and reliable IT infrastructure is available to support AI applications.
- IT effectively uses scalable cloud computing, data storage, and processing resources.
- Integrated, AI-specific tools and platforms enable efficient development and deployment of AI solutions.
progressive
- Infrastructure includes advanced technologies with high scalability and flexibility.
- State-of-the-art AI platforms, tools, and frameworks are in place.
- There is a strong emphasis on security, data privacy, and ethical AI in the technology stack.
innovative
- Infrastructure is optimized for AI, including high-performance computing resources.
- IT is pioneering in the adoption of modern technologies that enhance AI capabilities such as quantum computing and edge AI.
- Infrastructure supports current AI and is adaptable for future advancements.
r&d.
Investment in AI R&D signifies a company's commitment to innovation, from basic experimentation to leading-edge advancements. Use the prompts below to evaluate your organization’s investment in AI R&D. This may include internal efforts and collaborations with external entities like universities or research institutes.
emergent
- There are little to no dedicated AI R&D efforts.
- Any AI development is ad hoc, without structured research processes or dedicated investment.
- The primary sources for AI advancements or applications are external.
experimental
- There has been some initial investment in AI R&D, but on a small scale and often exploratory.
- There may be some collaboration with external entities for AI research.
- R&D efforts are project based and not yet a continuous, integral part of the strategy.
proficient
- AI R&D processes are structured and have clear objectives and dedicated resources.
- Ongoing projects and experiments in AI have returned results contributing to business applications.
- Balancing internal R&D with strategic external partnerships for knowledge exchange and innovation is a priority.
progressive
- Significant investments are being made in AI R&D, with established teams and long-term projects.
- R&D efforts are closely aligned with business strategy, driving innovation and competitive advantage.
- Active engagement in the broader AI research community contributes to inclusion in the latest developments.
innovative
- Leading-edge AI R&D often sets trends and benchmarks in the industry.
- There is a strong culture of innovation, with substantial investment in groundbreaking AI research.
- Regular breakthroughs and advancements in AI contribute to the company’s success and the wider field of AI.
where are you on the AI maturity framework?
A full AI maturity framework can help you determine your organization’s readiness for adopting AI. An honest evaluation of your organization’s maturity will help you address any deficiencies, putting you on the path to unlocking the full potential of AI. Be sure to read the next blog post in the series, which covers the “people” dimension of AI maturity.
Where are you on the AI maturity framework? Get in touch to work through the framework with one of our seasoned experts.