This is part 2/5 of our Ultimate Guide to AI Implementation- From Strategy to Execution.

Read the others here:

• [1/5] - Starting with Strategy: Building a Successful AI Game Plan for Your Business
• [2/5] - Data and Tech Readiness: Laying the Groundwork for AI Success
• [3/5] - Executing AI Projects: From Concept to Reality with Agility
• [4/5] - Deploying AI Solutions: Integrating into Business and Driving Adoption
• [5/5] - Scaling AI: From One Success to a Whole AI-Powered Organisation

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[2/5] Data and Tech Readiness: Laying the Groundwork for AI Success

After defining your AI strategy, the next step is to get your data and technology in shape. AI is only as good as its data and environment, and many AI initiatives stumble because their data wasn’t ready or the technology couldn’t support them. By putting in the work upfront to prepare your data and systems, you greatly improve your project’s chances of success.

• Assess your data landscape: identify what data you have (and need) for the AI use case, and note any gaps or silos‍
• Improve data quality: clean up errors and inconsistencies so your AI isn’t fed “garbage”‍
• Choose the right technology setup: decide on the right solution for your AI, the build vs. buy decision‍
• Ensure security and compliance: protect sensitive data and follow privacy regulations from the start‍
• Address bias and fairness: prepare data to avoid skewed or unethical AI outcomes

Assessing Your Data Landscape

AI runs on data, so start by auditing the data relevant to your use case. Identify all the data sources you might need and where they reside (databases, applications, spreadsheets, etc.). For each source, note who is responsible for it and how you can access it. Also, identify any gaps or silos - for example, important information locked in a system you don’t have access to, or historical data that’s too limited. You may need to integrate data from multiple systems or get permissions to use certain datasets. By the end of this step, you should know what data is available, what’s missing, and the plan to get it ready for AI.

Improving Data Quality

Once you know which data you’ll use, make sure it’s accurate and consistent. “Garbage in, garbage out” isn’t just a phrase people use, it’s the reality of AI. Examine the data for mistakes or inconsistencies: missing values, duplicates, or mislabeled entries. Fix these issues by cleaning the data - standardize formats, correct errors, merge duplicates - so the AI isn’t confused by dirty data. If data is incomplete or outdated, see if you can update it or gather additional data. The goal is a dataset that reliably represents the business scenario, giving the AI a solid ground to learn from.

Choosing the Right Technology Setup

Next, decide where your AI will live – in ServiceNow or on your other systems (or a mix of both). Using cloud-based AI services like ServiceNow can be a quick way to get started. Platforms like ServiceNow offer ready-made tools and scalable compute power, so you can prototype and deploy without buying new hardware. This is great for speed and flexibility, but remember your data will be moving “off-site”, which might be a concern for sensitive information or strict regulations.

Alternatively, running AI on-premises (on your own servers or private cloud) keeps data in-house and can reduce latency. However, it requires investment in infrastructure and technical know-how to set up and maintain. Many organizations start with a cloud solution to pilot the idea, then consider bringing the AI in-house later if needed for security reasons, or other purposes. In either case, ensure your IT environment can support the route you elect to go down.

Ensuring Security and Compliance

When working with data for AI, make security and privacy a top priority. Identify any sensitive data in your datasets (personal customer information, confidential business data, etc.) and take steps to protect it. This might mean anonymizing or masking personal identifiers so the AI doesn’t use actual identifiers. Use encryption for data in transit (especially if sending data across platforms or applications) and store data in secure, access-controlled locations. Limit access to the data and the AI system to the people who truly need it.

It’s also crucial to comply with data protection regulations and company policies. Check whether using the data for AI is allowed under laws like GDPR or industry-specific rules. If customers gave you data for one purpose, make sure you have the right to repurpose it for AI analysis. Engage your compliance or legal team early on if you’re unsure. Being proactive on compliance helps you avoid problems down the line and builds trust in your AI project.

Addressing Bias and Ensuring Fairness

Finally, be mindful of bias in your data. If the data reflects historical biases or is not representative of all cases, your AI could end up making unfair decisions. If your training data is heavily skewed toward one type of case, the AI might not perform well for others. To mitigate this, try to balance your dataset so it covers the diversity of scenarios your business encounters. You should also remove or avoid using attributes that shouldn’t influence outcomes (for example, excluding gender or ethnicity, unless it’s explicitly needed and lawful).

Plan to test your AI’s outputs for fairness during development. Have experts or stakeholders review sample results to see if anything looks biased or odd. If you notice the AI consistently under-serves a particular group or gives unexpected outcomes, adjust the data or how the model uses the data before deployment. Tackling these issues at the data preparation stage means you’ll deliver an AI that users find fair and trustworthy.

In short, getting your data and technology foundations right is not the flashiest or most exciting part of AI, but it is absolutely essential. By auditing and cleaning your data, picking the right infrastructure, securing sensitive information, and rooting out bias, you set the stage for an AI solution that works well and is trusted by your organization.

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Harnessing Artificial Intelligence with Crossfuze

At Crossfuze, we are passionate that Artificial Intelligence (AI) must be a strategic enabler that drives meaningful business outcomes. AI is not just about automation—it is about creating scalable, measurable solutions that enhance efficiency, improve decision-making, and unlock new opportunities for innovation.

Our role is to guide organisations through their AI journey, ensuring that every step—from inception to optimisation—is built on scalable, strategic, and measurable foundations.

Find out more about Crossfuze's AI offering.