Before You Invest in AI, Evaluate These Four Areas
Tech giants are expected to pour $320 billion into AI development in 2025 alone. The industry’s value is climbing fast and will hit $1,811.75 billion by 2030. And according to Ernst & Young, 97% of leaders already investing in AI say they’re seeing positive returns.
However, in that same report, 83% of senior business leaders said AI adoption would be faster with a stronger data infrastructure in place, and 67% said their lack of infrastructure is actively hindering AI adoption.
Data readiness and technical infrastructure are two critical areas that need to be evaluated before you invest in an AI project. Along with strategy and culture, they form the four pillars of AI readiness.
Successfully building or scaling an AI initiative depends on your organization’s strength across all four pillars. This article examines each pillar.
What Is AI Readiness?
AI readiness refers to your organization’s ability to effectively implement and leverage artificial intelligence to achieve your business goals.
It consists of four pillars or areas of concern: strategy, data, technical infrastructure, and culture.
A company is AI-ready when it has the foundation in place to identify relevant AI use cases, integrate solutions effectively, and drive sustainable outcomes.
Many C-suite leaders think their organization is more prepared for AI than it really is, according to one Microsoft survey.
When asked to assess their company’s level of AI readiness, 34% placed their organizations in the highest two stages. However, when their responses were analyzed by a predictive AI model built to determine where organizations would actually fall, only 25% were included in the highest two stages.
To increase the chances for your AI initiative, it is vital to conduct an accurate AI readiness assessment.
Pillar 1: Strategic Readiness
Strategic readiness means the business knows why it’s investing in AI, what it expects to gain, and which problems it’s solving first. That sounds obvious, many initiatives stumble because the desire to do something with AI moves faster than the articulation of why and to what end.
Achieving success with your AI project requires a strict focus on, and a clear definition of, your business goals. Instead of looking at what AI can do, clearly define the most important problem you can solve with AI. Otherwise, it’s difficult to measure the value of an AI solution and understand its benefits and uses.
Foundational elements of this pillar include:
- The main problem to be solved
- Business priorities
- AI use cases
- Leadership buy-in
- AI Leadership expertise
- AI management processes
Pillar 2: Data Readiness
AI algorithms need to be trained on representative data. Every pattern, error, outlier, and unexpected occurrence helps train an AI model for a specific use case.
For this reason, data that’s considered “high-quality” may not be ready for use by your AI. The cleansed data used by data scientists and business analysts often has outliers removed and other tweaks. AI often uses messier data.
AI solutions also need centralized, easily accessible data repositories. When assessing data readiness, determine if your data is confined to departmental silos or consolidated in a single repository, such as a data warehouse or data lake.
Data privacy and security constraints are other factors that may hinder data flow between systems and slow AI adoption.
The data readiness pillar, therefore, depends on these elements:
- Data accessibility
- Data organization
- Data governance
- Data domains
- Data ownership
- Data definition
- Data optimization
- Data correction
- Data bias
Pillar 3: Team Readiness
Like any technology, AI solutions require team training and communication to deliver the best results. Preparing your staff for AI involves building their trust in the technology and giving them opportunities to improve their AI skills.
You might encounter employees who are hesitant to use AI or fear it will take their jobs. Communicating the benefits of AI can help foster greater acceptance and use of the new tool, especially if they see how it will make their jobs easier.
The cultural readiness pillar also includes:
- Team training
- In-house AI expertise
- Change management
- Continuous improvement
Pillar 4: Technical Infrastructure
Technical readiness means your existing systems can support what AI needs to function well. It includes the ability to handle and store large amounts of data, connect with business systems already in place, and stay responsive as the number of users or use cases grows.
Your path forward will depend on the AI functionality and your existing hardware and software architecture. Deciding factors include how much work it will take to integrate the new AI tool with your existing systems any AI tools already in use.
You’ll also need a dedicated cloud infrastructure that can run large AI models at scale. AI needs a cloud platform’s computing power, analytics capabilities, storage, reliability, security, and performance capabilities to generate enough value to move beyond the pilot stage.
The infrastructure pillar includes:
- Hardware readiness
- Software readiness
- Scalability
- Bandwidth needs
- Hosting requirements
AI Readiness Means AI Success
The success of any AI initiative depends less on the technology itself and more on the readiness of your business to adapt to it. When you score high in an AI readiness assessment, you’re better positioned for a successful AI implementation that delivers on your business goals and helps you realize a rapid return on investment.
To help you gauge your business’s AI readiness, Taazaa offers a free, online AI assessment. At the end of our AI Readiness Assessment, you’ll get a focused view of how your organization measures up across the four pillars: strategy, data, people, and technology. You’ll also receive your overall readiness score, key focus areas for improvements, a list of next steps tailored to your business, and an optional consultation offer.
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