6-step AI implementation framework

We've already covered the three levels of AI implementation and the three most common mistakes companies make when adding it to their website in this blog: thinking AI is “set and forget”, treating AI as a siloed project, and focusing on tools and neglecting content. Now, let's turn to setting up your AI initiative for long-term success.

Our 6-step roadmap is not just about launching a new feature. It walks through how to embed AI into your broader digital strategy in a way that's sustainable, user-centric, and aligned with your organization's goals.

Before diving in, it's important to remember that successful AI adoption isn't just about functionality - it's also about responsibility. Ethical AI means maintaining transparency, fairness, and accountability in implementation and use. For detailed guidance on ethical AI deployment best practices, check out our DX Trends Report.

Skip ahead:

• Step 1: Understand the capacity for AI to solve your problems
• Step 2: Be responsible
• Step 3: Choose high-impact and low-risk pilots
• Step 4: Appointing an AI readiness lead/task force
• Step 5: Assessing digital assets for AI compatibility
• Step 6: Embedding continuous learning
• Bringing it all together on your website experience
• Your next steps

Before jumping into implementation, invest time in clarifying where AI genuinely fits within your business goals and user needs. This step lays the foundation for meaningful adoption.

Key questions you should answer at this stage:

• What are your organization's strategic priorities?
  Identify your key business objectives (e.g., improving customer satisfaction, increasing operational efficiency, reducing support costs) so AI initiatives directly align with measurable outcomes.
• Where can AI augment discovery, engagement, or operations?
  Map your customer and staff journeys to spot friction points below. These are your AI opportunity zones.
  • Are users struggling to find answers?
  • Are there high-volume, repetitive support requests?
  • Which content areas receive the most search queries?
  • Where do users abandon your digital experiences?

Here are some examples on how to analyze AI needs based on this assessment:

• What level of AI do you actually need?
  Clarify which of the three levels you require to avoid over-engineering or under-delivering:
  • Automation of internal tasks
  • Adaptation via tools that generate content or surface information, like conversational AI search
  • Action through AI agent systems that complete a task on behalf of a user, such as appointment scheduling
  • You can find more information on each of these AI levels here.
• Which use cases are low-risk but high-impact?
  Prioritize projects that allow you to test, learn, and demonstrate quick wins without exposing the organization to significant risk, like enhancing your Help Center with conversational AI search before scaling to product pages or sensitive information areas.
• Choose Leading metricsMaking a measurable impact on the business goal (the strategic priority) takes time. Identify leading metrics that are sensitive and actionable,  which can help you course-correct on the path to achieving your desired long-term result.

Before choosing where to implement AI, it’s important to evaluate the risk and complexity of each potential use case. This framework helps you assess whether a project is low, medium, or high risk - so you can prioritize wisely:

While you don't need to understand AI's internal mechanics in depth to use it effectively, you do need to be clear on its purpose, limitations, and where it fits your roadmap.

AI is a powerful tool that can deliver significant value, but it also carries risks that need to be carefully managed.

Consider the human impact

Evaluate the potential consequences of AI-driven decisions on your users. For example, while recommending a song that someone doesn't like is a minor inconvenience they can simply skip, telling someone they're not eligible for financial aid when they could actually qualify can have drastic impacts on their life. Understanding the gravity of these decisions helps you implement appropriate safeguards.

Ensure legal and moral rights to data

Before feeding data into AI models, confirm you have both legal and moral rights to use that information:

• Legal rights: Verify that data was collected legally and complies with current regulations
• Moral rights: Consider whether your use of this data aligns with how your community that provided it expects to be engaged with

All too often we see how terms and conditions changed regularly, and these changes are rarely in favor of the consumer. Strive for a better moral stance with your audience.

Build in explainability and escalation

Your AI system should be able to explain why it made specific decisions or recommendations. More importantly, establish clear escalation paths:

• Provide users with a way to flag incorrect or problematic AI responses
• Ensure there's a human at the back end who can review and address these issues
• Create transparent processes for users to understand how AI-driven decisions affect them

Address environmental and social impact

AI implementation has broader implications that need consideration. For example:

Environmental impact: AI models consume significant energy, contributing to climate change effects

Social impact: Many AI models are trained primarily on English content or built by specific demographic groups, which can introduce biases

Governance: Establish frameworks to monitor and mitigate these impacts from the start. It’s important that you don’t treat governance similarly to a document on SharePoint. Governance is built into all processes and allows you to continually monitor and improve your services. It is a key to engaging with your public and building trust.

Building responsibility into your AI implementation from the beginning, rather than adding it as an afterthought, helps ensure your AI initiatives create positive value while minimizing potential harm.

The goal is to secure early wins that demonstrate tangible improvements in discoverability and user satisfaction, while refining your internal processes and technical capabilities for broader rollout.

Now, you need clear ownership to stay on track. Select a dedicated lead or cross-functional task force to drive your AI initiative forward.

AI readiness lead responsibilities include:

Overseeing AI implementation end to end

• Ensure all AI projects are aligned with your broader digital strategy
• Maintain a unified roadmap from initial pilots to full rollouts
• Identify and manage dependencies between different work streams such as content, data, tech, and governance

Translating AI capabilities into business outcomes

• Understand what AI can (and can’t) do, and match tools to real user problems
• Identify suitable use cases by balancing ambition, feasibility, and risk
• Anticipate ethical challenges and avoid overreliance on untested tools

Ensuring ethical and inclusive design

• Ensure AI solutions are designed to meet the needs of diverse users
• Coordinate with legal and compliance teams to uphold data rights and privacy
• Promote transparency, fairness, and explainability in AI-generated responses

Coordinating collaboration across teams and promoting organizational readiness 

• Act as the central hub connecting content, UX, IT, compliance, and governance teams, ensuring consistent approaches across departments
• Facilitate cross-functional workshops and decision-making, resolving conflicts and prioritization challenges
• Raise AI literacy through internal education and workshops
• Act as a translator between technical and non-technical stakeholders
• Surface risks early, recommend mitigations, and flag dependencies
• Track progress, share learnings, and communicate wins internallys

Championing continuous improvement

• Keep AI readiness front of mind by monitoring performance
• Gather and analyze user feedback
• Drive ongoing refinement as technologies and user behaviors evolve
• Stay current with AI trends and best practices

Acting as the go-to resource for leadership

• Provide regular updates on progress and challenges
• Translate technical concepts for non-technical stakeholders
• Flag issues early and propose solutions
• Celebrate and communicate wins

This role can be a dedicated position or part of an existing digital strategy function, depending on your organization's size and structure.

Here's an example of an AI implementation stakeholder map. It includes stakeholders you might consider to be part of your AI readiness task force.

Before introducing new tools, you need to ensure your existing digital foundations are ready to support them. Conduct a thorough content audit across key areas.

Here is your content readiness checklist:

Content clarity, structure, and answerability

• Are your pages written clearly, well-structured, and easy for both users and AI to interpret?
• Do headings accurately describe the content that follows?
• Are questions and answers explicitly formatted as such?
• Is important information buried in images or non-text formats?
• How often do you review search query terms to see what users are looking for?

Here is an example of a content quality scorecard to help you visualize what goes into this assessment:

Search experience: Assess how easily users and AI models can locate relevant content.

• Is your internal search intuitive and effective?
• Are pages optimized for conversational discovery?
• How well does your website search handle synonym recognition and natural language queries?

Information architecture: Check whether your website's structure supports logical, seamless navigation and topic clustering.

• Are related topics properly linked and contextualized?
• Does your taxonomy support AI-based discovery?
• Is your content organized in ways that make sense to both humans and machines?

Governance workflows: Evaluate your processes for maintaining and updating digital assets.

• How quickly can you update content when inaccuracies are identified?
• Who is responsible for monitoring AI-driven interactions?
• What approval processes might slow down necessary adjustments?

Accessibility and mobile-friendliness: Ensure your content is well-marked-up, accessible, and mobile-optimized.

• Review compliance with accessibility standards and test usability across devices
• Ensure that screen readers and other assistive technologies can interpret your content

AI implementation isn't a one-and-done project. To keep your AI-driven experiences effective and evolving, establish a system for ongoing improvement.

A continuous improvement framework includes:

Monitoring

• Track search queries, conversational analytics, and user engagement patterns
• Key metrics to monitor regularly:
  • Top questions asked and successful answer rate
  • Conversation abandonment points
  • User feedback ratings
  • Follow-up questions (indicating incomplete answers)
  • Content gaps identified through AI interactions

Reviewing and iterating

• Establish a regular cadence for reviewing and updating your initial implementation based on user interactions and system performance:
  • Weekly: Review high-priority unanswered questions
  • Monthly: Update content based on query patterns
  • Quarterly: Assess overall performance and strategic alignment

Expanding

• Use data and feedback from reviews on your pilot (mentioned above) to also identify opportunities for safe, scalable expansion:
  • Prioritize new content areas where user demand is clear (e.g., top search queries not yet covered).
  • Reuse successful structures and formatting patterns from your pilot content.
  • Reassess risks, complexity, and readiness before scaling into more sensitive or dynamic sections of your site.

Sharing

• Create cross-functional feedback loops:
  • Regular insights reports for content creators
  • AI performance dashboards for leadership
  • Training resources based on common challenges
  • Success stories that reinforce the value of AI implementation

Governance integration

• Integrate AI considerations into your existing governance framework:
  • Add AI impact assessments to content creation workflows
  • Include AI readiness in content quality checks
  • Establish clear responsibility for AI performance
  • Create escalation paths for AI-related issues

The shift to AI-powered discovery and engagement is already here. Organizations that act early and wisely will build trust, relevance, and long-term visibility. Those who delay, risk falling behind as AI reshapes every touchpoint of digital engagement.

At Squiz, we're helping organizations get ahead of this curve with our Conversational AI Search feature, powered by Squiz Funnelback Search. This solution is designed to package the power of AI search into a conversational experience on your website.

Here's a glimpse of how this works in a real search scenario, such as financial aid inquiries:

By integrating Squiz Funnelback's trusted search engine, natural language interfaces, and content auditing tools, conversational AI search not only ensures your pages are AI-ready but also aligned to how people are discovering information today.

To help organizations maximize the success of this AI implementation, we offer a dedicated consultancy framework that supports content readiness, smooth implementation of conversational tools, and continuous improvement over time.

Ready to begin your AI implementation journey?