AI Implementation Readiness: When to Adopt AI/ML in Your Business

The news about "AI is reshaping the world and every industry" is creating a sense of urgency for businesses to adopt it. But as you consider integrating AI into your workflow, you may ask: Is my business ready? What will implementation cost? Will it deliver real ROI?

These concerns are valid because not all AI adoption yields positive results. In fact, a recent research by RAND shows the correlation between increased AI adoption rate and its failure. According to this research, AI project failures are due to a lack of strategic planning and proper readiness.

Therefore, before rushing into AI adoption due to market pressure, it's crucial you assess whether your business is equipped for a smooth transition. But often, what to assess can be another challenge.

In this article, we will discuss how to evaluate your AI readiness and understand your business's unique AI needs to ensure successful AI implementation.

Understand What AI Adoption Means for Your Business

AI adoption is a broad term; it doesn't mean the same thing for every business. The AI adoption priorities of a product-led business will differ from those of a service provider. Even within the same industry, two businesses may have entirely different AI adoption strategies.

For example, imagine running a call center, and your competitors use AI to improve customer interactions. Does this mean you should do the same? The answer is "not necessarily" because AI isn't a one-size-fits-all solution.

So, define your business-specific AI adoption rather than adopting AI based on competitors' insight or broad knowledge of what AI can do. Doing this can save you from wrongful AI implementation, which may lead to failed projects, wasted resources, and often, disappointment.

RAND research on AI project failure found that one of the primary reasons AI initiatives fail is that business leaders misunderstand or miscommunicate the actual problem AI is meant to solve. To avoid this pitfall, you should ask the right questions, such as

• What are the current challenges?
• What's working well, and where are the inefficiencies?
• What key objectives do we want to achieve with AI (e.g., improving efficiency, customer experience, reducing costs)?
• Do we have all it takes to adopt AI successfully?

Vijay Venkatesan, the CTO at a healthcare company in New Jersey, gave a typical scenario of how he would approach AI adoption in a regulated industry like healthcare.

According to him, the process starts with establishing a governing model, which involves asking questions such as,

• How do we define ethics, and where should we apply AI? And because we're a healthcare company, it's really about protecting the patients' data and ensuring we're not doing something we're not supposed to.
• What are the right use cases? Is this the right use case? Should we even use it this way?
• How do we evaluate the risk portion of these use cases and say, Is this a high risk? The high risk would mean the AI model could access the patient data. Have we taken all the protection and guardrails?

By combining the fundamental questions with Vijay's, you're on your way to validating whether you need AI immediately or later.

Review Your Operational Readiness (Culture, Finance & Structure)

Having specified what AI adoption looks like for your business, the next step is ensuring your organization is operationally ready. This means assessing factors like company culture, financial capability, and overall business strategy.

Successful AI implementation requires collaboration across teams and, in some cases, external expertise. If your internal team lacks the expertise to review the financial implications of what the AI project requires, consider an AI consultant.

To ensure a balanced operational readiness evaluation, consider these:

1. Set a Clear AI strategy

   First, develop a strategy document that outlines goals, timelines, and responsible teams for the AI project. Ensure this AI strategy process aligns with your business goals and values to foster long-term success.

2. Get Leadership Buy-in

   The second phase of your organization's readiness is aligning decision-makers with the adoption of AI to help them understand how it impacts return on investment. This stage can be tedious if leaders lack proper education about AI. However, you should dedicate extra resources and time to get their consent as they are often responsible for allocating budget and resources for business development and implementation.

3. Evaluate for Cultural Change Preparation

   AI adoption goes beyond technical shift. It mostly leads to behavioral changes, impacting how employees work, how leaders make decisions, and how you interact with customers. Without the right cultural alignment, AI projects are prone to failure. In a situation of misalignment in cultural change, Cisco recommends encouraging AI adoption across departments, incentivizing innovation, and rewarding successful AI initiatives.

Review Your Data Readiness

AI development is a complex process that relies on three fundamental components (business context, algorithm, and data). One is data, which is also becoming more complex as businesses continue to adapt AI for different use cases.

Data is the soul of AI algorithms that allow machine learning to function effectively.

Source: Deloitte

However, unavailability of data or availability of low-quality and inaccurate data may lead to AI system failures. This shows that data is the fuel that drives the effectiveness of any AI model. Therefore, organizations must invest in data engineering to guarantee clean data collection, cleaning, structuring, and formatting for AI model performance.

Before an organization can be considered AI-ready for a particular project, their data must have these characteristics:

• Data quality: A set of data is considered quality and useful for AI projects if it is free from errors and human manipulations. For example, a retail company has duplicated sales data sources that feed its AI model for demand forecasting. In that case, the AI model may predict unrealistic demand, leading to overstocking or stock shortages.

• Structure: While AI can handle structure and unstructured data, defining the easiest approach for an AI project is essential. Often, structured data is easier to work with as they are predefined.

• Data integrity: An established data governance must be in place to ensure data is secured from unauthorized access. Data must follow strict regulations and compliance to safeguard the integrity of AI models to be built.

• Relevance: Every AI project requires a different data set. Available data must be contextually relevant to the goals and objectives of the AI project. For example, customer data should include all relevant details like purchase history without missing or incorrect entries.

• Proper storage for consistency: Data format should be standardized to ensure uniformity, especially when working with data from different sources. A typical scenario is the collection of sales data in other currencies. This kind of data is not ready for an AI project as it needs to be in standardized currency formats before feeding into an AI-powered financial analytics tool.

To investigate the data readiness of an organization, understand the AI project scope, evaluate for AI data characteristics, and modify based on the outcome.

If your team lacks the technical expertise to conduct data readiness evaluation, work with AI/ML consulting experts like GAP to support evaluating and preparing your data. You may also perform a quick assessment to determine your organization's AI readiness.

Evaluate Employee Readiness

Just as a technological readiness check is important, evaluating your workforce's attitude and skills toward AI adoption is equally important. You must conduct an employee readiness evaluation at skill and behavioral levels to answer the question: is my workforce ready to adopt this innovation into their workflow?

According to Weforum's recent research, skill gaps are the biggest barrier to business transformation, especially as new technologies emerge. This emphasizes the need for organizations to adopt a people-first approach to AI adoption.

Since AI adoption is still in its earliest stages, upskilling and reskilling are some of the training methods to build an innovative AI workforce.

Technological Infrastructure Readiness

Your AI strategy isn't complete without analyzing your internal technological tools. Are you still relying on legacy tech tools? If so, integrating AI could be challenging, leading to inefficiencies and compatibility issues. Therefore, you need a modernization service intervention to ensure your technological tools are ready for AI.

You should also ask questions like: Are your systems cloud-based? Are you using software capable of handling AI workloads without any performance issues?

Additionally, scalability is a critical factor. Your infrastructure should be capable of growing with your AI needs to prevent future system failures and performance limitations.

Seek an AI Implementation Consultant for a Successful Adoption

With emerging technologies and increased AI adoption, AI readiness evaluation is becoming more complex. But you don't have to face the hurdles alone. At GAP, we focus on helping businesses across different industries assess their AI readiness and implement tailored solutions that drive real results.

Rather than spending countless hours on a trial and error approach, you can start with our AI calculator today for a quick and effective AI readiness evaluation. If you prefer a more personalized approach, GAP offers a free 1:1 consultation to align your AI adoption with your business goals.

Still thinking of how to incorporate AI into your business? Let's work together to make your AI implementation journey strategic and impactful. We've supported and helped thousands of companies, including big names like Thomson Reuters, NZero, Acxiom, and Nissan, to develop custom AI solutions. Contact us today, and let's begin the journey to successful AI adoption.