AI Strategy & Readiness
Turning AI Potential Into Outcomes
Artificial Intelligence is continually evolving, increasing the need for organizations to move beyond experimentation and establish a thoughtful, scalable approach to AI adoption.
Partnering with companies to evaluate AI strategy, readiness, and governance is an increasingly important part of the work Kona Kai Corporation supports as organizations prepare to operationalize AI.
Our structured, practical approach to AI readiness and strategy helps organizations build confidence and clarity before scaling AI initiatives.
We begin with an in-depth assessment of your current AI landscape, using a business-first, “day-in-the-life” lens to understand how decisions are made, how data moves across systems, and where AI is already influencing work—formally or informally. This approach allows us to see how people, processes, platforms, and data interact today, identify points of friction or risk, and understand how AI, including emerging agent-driven capabilities, could realistically support outcomes across end-to-end workflows.
By embedding closely with your teams, we enable shared understanding and ownership throughout the process. This approach helps surface manual, fragmented, or low-value activities where AI may add value, builds internal knowledge and confidence, and ensures teams are active participants in shaping how AI is introduced. The result is not only a stronger AI foundation, but a level of readiness that supports long-term adoption as AI capabilities become more advanced and more autonomous.
Is AI creating friction instead of value?
Wasting Time?
Our teams are spending too much time experimenting, reworking outputs, or searching for data needed to make AI useful.
Wasting Money?
We are investing in AI tools and pilots without a clear roadmap, measurable ROI, or confidence in what to scale.
Wasting Resources?
AI outputs introduce risk or inconsistency, creating extra effort for reviews, corrections, and approvals to maintain quality and trust.
Our AI initiatives are slowed by readiness gaps.
1
AI adoption takes too long.
our solutions:
> Assess AI readiness across people, processes, platforms, and data to establish a clear baseline for adoption
> Map how decisions, data, and workflows operate today to identify dependencies that impact AI execution
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Work alongside your teams to surface manual, fragmented, or unclear practices that must be addressed before AI can scale or take on more autonomous roles
2
There are too many steps.
Our solutions:
> Analyze how AI fits into existing workflows and decision paths to simplify adoption
> Clarify roles, ownership, and decision points to reduce friction and ambiguity
> Iterate collaboratively with stakeholders to validate readiness assumptions and align on practical next steps
3
AI initiatives encounter bottlenecks.
OUR SOLUTIONS:
> Identify organizational and technical bottlenecks that prevent AI initiatives from moving forward
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Map how work, decisions, and data flow between teams to reduce reliance on informal processes such as emails, spreadsheets, or manual reviews
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Establish consistent handoffs and accountability to support predictable, scalable AI execution
Our AI investments are costing more than expected.
4
There is no clear way to measure success or readiness.
our solutions:
> Define readiness and success metrics tied to business outcomes, adoption, and risk
> Establish clear checkpoints that indicate when AI initiatives are ready to move from experimentation to execution
> Use readiness insights to inform prioritization, investment decisions, and future-state planning
5
AI reporting is inconsistent or incomplete.
Our solutions:
> Create consistent, business-aligned views into AI readiness and adoption
> Enable structured reporting that reflects how AI is used across workflows and teams
> Support end-to-end visibility that highlights constraints, dependencies, and improvement opportunities over time
6
AI growth plans are not scalable.
OUR SOLUTIONS:
> Identify repeatable patterns and foundational capabilities that support scalable AI adoption
> Reduce complexity by addressing readiness gaps before expanding AI use cases
> Support governance structures and cross-functional alignment to guide responsible growth
AI outputs introduce risk and inconsistency, tying up resources for review and correction.
7
AI outputs introduce risk and inconsistency.
our solutions:
> Design AI workflows that account for primary and exception paths, reducing the need for manual rework
> Introduce guardrails that limit where and how AI can act, ensuring outputs stay within defined parameters
> Focus on business results and risk tolerance rather than replicating current-state workarounds
8
AI decisions depend on undocumented knowledge.
Our solutions:
> Engage subject matter experts early to document decision logic and risk thresholds
> Translate tacit knowledge into repeatable rules and escalation criteria
> Use a day-in-the-life methodology to understand where human judgment is required and where AI can safely support decisions
> Align AI behavior to outcomes and quality standards, not personal preferences
9
AI output lacks a clear review and approval process.
OUR SOLUTIONS:
> Map end-to-end workflows that clearly define where AI generates output, where humans intervene, and where decisions are finalized
> Establish consistent review and approval mechanisms that balance speed with accountability
> Design supporting data and governance models to ensure AI outputs remain auditable, explainable, and trustworthy
