The most important question in any AI conversation is whether the problem actually requires AI. Many challenges that get labeled as AI opportunities are better solved with advanced analytics, improved data quality, or better reporting. Pursuing AI when advanced analytics is the right answer wastes investment, creates complexity, and typically delivers worse results.
On Key helps organizations make that determination rigorously, then builds the requirements, readiness plan, and integration architecture around whatever the right answer turns out to be. If the path forward does involve AI, whether that means predictive analytics, LLM integration, MCP-enabled workflows, or agentic automation, we define precisely what has to be true before it can succeed.
Start a Conversation"The first question is never which AI tool to use. It is whether AI is the right answer at all."
The questions On Key works through before any AI engagement begins. These determine not just whether AI is appropriate, but which type of AI capability, if any, is actually the right fit for the problem at hand.
When AI is the right answer, On Key helps organizations understand which type of capability fits their problem, and what each one actually requires to succeed.
Statistical and machine learning models that forecast outcomes based on historical patterns. Appropriate when you have clean, structured historical data and a well-defined prediction target. Often the right answer before anything more complex is considered.
Large language models applied to business problems such as document analysis, structured data extraction, natural language querying of data, and content generation. Success depends heavily on data quality, prompt engineering discipline, and governance of model outputs.
Model Context Protocol and agentic AI frameworks that enable AI systems to interact with data sources, tools, and analytics environments autonomously. Qlik's native MCP capabilities fall into this category. Requires careful integration design and oversight architecture.
Statistical analysis, segmentation, anomaly detection, and optimization models that do not require AI infrastructure but deliver significant analytical value. Frequently the most practical and defensible solution, and the one that most organizations should pursue before AI.
On Key's AI engagements follow a deliberate sequence. Each phase produces a decision, not just a document. No phase commits the organization to the next until the output warrants it.
We define the business problem precisely and evaluate whether AI is the appropriate solution. Many engagements end here with a recommendation to solve the problem differently. That is a good outcome, not a failure.
We evaluate the data environment against what the identified use case actually requires: quality, completeness, structure, volume, labeling, and governance. We produce a clear gap analysis and remediation roadmap.
We identify and rank AI use cases by feasibility and business value, then design the integration architecture that connects AI outputs to the analytics environment the organization already uses.
We establish the governance framework covering model oversight, output validation, lineage, and risk, then develop the organizational transition plan that determines what has to change for adoption to succeed.
Clarity about scope is part of the value. On Key's AI practice stays in the advisory, assessment, and integration lane, where our data and analytics expertise is the differentiator.
Organizations running Qlik Cloud Analytics have a significant head start on AI readiness. Qlik's native AI capabilities, including predictive analytics, natural language querying, and AutoML, can extend what your environment already does. On Key designs AI readiness programs that build on your existing Qlik investment rather than bypassing it.
Start with a direct conversation about what you are trying to accomplish. On Key will give you an honest assessment of where you are and what actually needs to happen before AI can deliver.