AI Strategy Workshop for a Digital Marketplace: From Experimentation to an Actionable AI Roadmap
Nextant partnered with a leading e-commerce organization in the flower industry, to help them move from early, disconnected AI experimentation to a clear, business-driven AI strategy.
While the company understood the importance of data and AI and had shown genuine interest in adopting these capabilities, its efforts were fragmented. Data initiatives were largely limited to isolated reporting efforts, and AI exploration was experimental, opportunistic, and not connected to a broader strategic vision.
Leadership recognized that, despite the motivation to leverage AI, the organization lacked a structured approach to prioritize initiatives, align stakeholders, and translate ideas into an executable roadmap tied to business goals.
Through Nextant’s AI Readiness and Use Case Prioritization Workshop, the client moved in just one week from a fragmented set of ideas to a prioritized portfolio of AI initiatives, a phased adoption roadmap, and a concrete execution plan. The engagement created immediate strategic clarity, stakeholder alignment, and a practical path to delivering measurable AI impact.
This accelerated format allowed the organization to rapidly explore AI opportunities and establish a strong strategic foundation without the time, cost, or disruption of a traditional multi-month consulting engagement. The project reflects Nextant’s broader mission to help organizations modernize operations, unify data, and adopt AI responsibly to drive better decisions, operational efficiency, and customer experience.
Move from AI experimentation to a clear, business-driven roadmap in weeks, not months. Schedule your AI Strategy Workshop.
The Challenge: Moving from Fragmented AI Experiments to a Structured AI Adoption Strategy
When the client began evaluating how to apply AI across its operations and strategic priorities, it faced a common challenge. There was strong interest in AI and many ideas across the organization, but no clear structure to assess readiness, prioritize initiatives, or define a realistic path forward.
While there was broad enthusiasm for AI across the business, leadership lacked a consistent framework to assess which initiatives were truly ready to pursue. The organization was still building its depth of understanding of AI, including the range of available solution patterns and what it takes to deliver solutions that are safe, reliable, and production-grade.
Teams struggled to evaluate potential use cases in the context of data and information maturity, the current landscape of systems and integrations, and practical constraints such as time, cost, and organizational capacity. At the same time, different stakeholders had different views on priorities, making alignment difficult and slowing decision-making.
Without a shared framework or a clear, phased timeline for AI adoption, the company found it challenging to distinguish high-impact, near-term opportunities from longer-term strategic bets and to build confidence around where to focus investment first.
The Solution: AI Readiness Assessment and Use Case Prioritization Workshop
To address these challenges, Nextant delivered its AI Readiness and Use Case Prioritization Workshop, a repeatable, scalable offering designed to give organizations a fast, structured entry point into AI strategy and execution.
The one-week engagement was structured into four highly focused working sessions:
Strategic Context and Business Objectives
Establishing a shared understanding of the business model, operating priorities, and strategic goals.
AI Maturity and Readiness Assessment
Assessing the organization’s current level of readiness for AI by evaluating core pillars such as data, technology, people, governance, and operating model.
AI Use Case Identification and Prioritization
Facilitating a structured discussion to identify relevant AI use cases aligned with business objectives, followed by prioritization of two to three high-potential initiatives.
Roadmap and Execution Design
Defining a clear, phased roadmap for the prioritized use cases, including scope, dependencies, sequencing, and key considerations for execution.
Between sessions, Nextant synthesized inputs, applied its deep experience with Microsoft’s AI and data platforms, and produced executive-ready materials to maintain momentum and strategic coherence.
Over the course of the week, the engagement delivered:
- A tailored set of AI use case candidates mapped to the client’s business and industry
- A prioritization and scoring framework balancing value, feasibility, and readiness
- A data and AI maturity assessment across key capability areas
- A set of initiative briefs and project charters for the top-priority use cases
- A phased adoption roadmap linking quick wins to longer-term strategic investments
This structured, collaborative approach created a shared language between business and technical leaders and enabled fast, confident decision-making.
The Outcome: A Prioritized AI Use Case Portfolio and Phased AI Adoption Roadmap
By the end of the engagement, the client had a clear, aligned, and actionable AI strategy grounded in real organizational readiness and business priorities. Leadership gained a practical framework to continuously identify, assess, and sequence AI initiatives over time.
In the short term, the organization identified concrete AI quick wins that were achievable with existing data, systems, and capabilities. In parallel, the workshop highlighted the need to modernize and unify core business data on an AI-ready platform to unlock more advanced and scalable AI solutions in the future.
By connecting near-term opportunities with longer-term strategic initiatives, the engagement established stakeholder alignment, a credible adoption timeline, and transformed AI from an abstract ambition into a concrete, execution-ready portfolio of initiatives.
Assess your AI readiness, prioritize high-impact use cases, and build a phased adoption plan aligned with your business goals. Request an AI Readiness Assessment.
Frequently Asked Questions
What is an AI strategy and why is it important?
An AI strategy is a structured plan that defines how an organization will use artificial intelligence to achieve specific business objectives. It connects AI initiatives to measurable outcomes such as revenue growth, operational efficiency, cost optimization, and improved customer experience.
Without a clear AI strategy, organizations often experiment with disconnected use cases that fail to scale. A well-defined AI strategy ensures alignment between business priorities, data capabilities, technology platforms, and governance requirements.
What does AI readiness mean?
AI readiness refers to an organization’s ability to successfully design, implement, and scale AI solutions. It includes evaluating key foundational elements such as data quality, system architecture, talent capabilities, governance models, and executive alignment.
An AI readiness assessment helps identify gaps that may prevent AI initiatives from delivering reliable, production-grade results.
What is an AI adoption roadmap?
An AI adoption roadmap is a phased implementation plan that outlines how AI initiatives will be prioritized, sequenced, and executed over time. It connects short-term quick wins with long-term strategic transformation efforts.
A structured roadmap reduces risk, improves stakeholder alignment, and ensures that AI investments are both practical and scalable.
How are AI use cases identified and prioritized?
AI use cases are identified by aligning business objectives with areas where data-driven insights or automation can create measurable impact.
Prioritization typically considers:
- Business value and strategic importance
- Data availability and quality
- Technical feasibility
- Organizational capacity
- Implementation complexity
This structured evaluation ensures organizations focus on high-impact, achievable initiatives first.
Why do AI initiatives fail?
AI initiatives often fail due to unclear business alignment, fragmented data environments, lack of governance, insufficient stakeholder buy-in, or unrealistic expectations about implementation complexity.
Successful AI adoption requires more than technology, it depends on strong data foundations, executive alignment, phased execution planning, and a clear understanding of organizational readiness.
What is the difference between AI experimentation and AI transformation?
AI experimentation involves isolated pilots or proof-of-concept projects that may not be integrated into core business processes.
AI transformation, by contrast, involves a coordinated strategy that embeds AI into operations, decision-making, and customer experiences through structured governance, scalable architecture, and long-term investment planning.
How long does it take to build an AI strategy?
While full-scale AI transformation can take years, an initial AI strategy and prioritization roadmap can be developed in a matter of weeks when using a structured workshop-based approach.
The key is balancing speed with strategic clarity, defining realistic, phased initiatives rather than attempting large-scale transformation all at once.
