Searchability, GEO & AI Operating Model
Build the foundations that make search, self-service and AI work
Artificial Intelligence is changing how employees search for information, access knowledge and complete tasks.
Yet many organisations are discovering that investing in AI does not automatically improve employee experiences.
If content is difficult to find, information is duplicated, ownership is unclear or governance is weak, AI often amplifies existing problems rather than solving them.
ako labs helps organisations improve the quality of the information that powers search, self-service and AI. We also help organisations establish practical operating models for managing AI tools and identifying where AI can genuinely add business value.
Searchability, GEO and enterprise search
Most people are familiar with Search Engine Optimisation (SEO), but fewer realise that many of the same principles apply within intranets, digital workplaces and AI-powered search experiences.
Search Engine Optimisation (SEO)
Search Engine Optimisation is the practice of helping search engines identify the most relevant content for a user's search query.
Common SEO techniques include:
Using meaningful page titles and headings
Applying clear heading hierarchies
Using language that matches user search behaviour
Linking related content together
Reducing duplicate content
Keeping information current and accurate
The goal is simple
Help a search engine understand which content best answers a user's question.
Applying SEO principles to intranet search
The same principles apply inside organisations.
Whether an employee is searching an intranet, SharePoint environment, knowledge hub or digital workplace platform, the search engine still needs to determine which content is most relevant.
Search quality depends on factors such as:
Clear content structures
Consistent terminology
Metadata and taxonomy
Strong ownership and governance
Well-maintained content
Removal of outdated or duplicate information
For example, when an employee searches for "Parental leave", they should ideally find the current policy and supporting guidance.
Instead, many organisations return multiple versions of the policy, project communications from previous years, outdated PDF documents and department-specific interpretations of the same information.
The result is familiar - employees stop trusting search and start asking colleagues instead.
ako labs helps organisations improve searchability by strengthening content design, information architecture, governance and content lifecycle management.
Generative Engine Optimisation (GEO) for AI tools
Generative Engine Optimisation (GEO) applies similar thinking to AI-powered tools.
Rather than returning a list of search results, AI systems attempt to generate an answer.
To do that effectively, they need to determine:
Which information is relevant
Which sources are trustworthy
Which version of the content is correct
How different pieces of information relate to one another
Examples include Microsoft Copilot, enterprise AI assistants, knowledge discovery tools and AI-powered search experiences.
The goal is no longer “which page should I show?”, instead, the question becomes “what answer should I generate?”.
The same information challenges that affect search often affect AI:
Multiple versions of the same information
Conflicting guidance
Unclear ownership
Poor metadata
Inconsistent structures
Outdated content
Content buried in documents rather than designed for discovery
As a result, many AI initiatives quickly become information management challenges rather than technology challenges.
Improving AI readiness often begins by improving the same foundations that support effective search, including information architecture, governance, metadata, content quality and ownership.
AI strategy, readiness and governance
Technology is only one part of successful AI adoption.
Many organisations are investing heavily in AI technologies while giving less attention to the governance, ownership and decision-making processes required to manage them effectively.
Questions quickly emerge:
Which AI tools should employees use?
Who owns AI initiatives?
How should outputs be reviewed?
What information can be used safely?
How do we identify valuable opportunities for AI?
How do teams avoid duplicating effort?
These are operating model questions as much as technology questions.
What we help with
Search and AI readiness assessments
Activities may include:
Content audits
Governance reviews
Information architecture reviews
Metadata assessments
Search assessments
Content ownership reviews
AI governance design
We help organisations establish:
Governance structures
Roles and responsibilities
Decision-making forums
Approval processes
Standards and guardrails
Ways of working
RACI frameworks
AI opportunity assessment
Not every problem requires AI.
We help organisations:
Review existing business processes
Identify friction points
Assess automation opportunities
Evaluate AI use cases
Prioritise investment opportunities
The focus is always on solving the business problem first, then identifying the most appropriate solution.
How we work
At ako labs, we draw on a range of disciplines including content design, governance and operating model design, service design and change management.
Every client engagement requires drawing on each of these disciplines to get the job done right. However, our core end-to-end methodology is known as content design, and every employee experience we support is powered by our content design methodology.
Related work
Related insights
Looking to improve searchability or prepare for AI?
Whether you're improving enterprise search, preparing content for Microsoft Copilot, designing AI governance or identifying opportunities for automation, ako labs can help you build the foundations that successful search, self-service and AI initiatives depend upon.