Divisional Artificial Intelligence; AI Adoption Manager
Listed on 2026-07-16
-
IT/Tech
AI Business & Operations, AI Engineer (Applied/Software), Change Management, Business Systems & Technology Analysis
Divisional Artificial Intelligence (AI) Adoption Manager
Job Category: MARKETING
Requisition Number: DIVIS
003465
- Posted:
July 8, 2026 - Full-Time
Tate Columbia MD
7001 Columbia Gateway Dr
Suite 500
Columbia, MD 21046, USA
Tate Columbia MD
7001 Columbia Gateway Dr
Suite 500
Columbia, MD 21046, USA
Tate Red Lion Plant 2
100 Redco Ave
Red Lion, PA 17356, USA
Tate Red Lion Plant 2
100 Redco Ave
Red Lion, PA 17356, USA
- Pay or shift range: $140,000 USD to $160,000 USD
Job Type: Exempt
Position: 1
Duration of role:
Permanent
Reporting to:
Divisional Head of Digital, AI and Insights
Location:
Remote (United States)
Kingspan Data Solutions originated in the traditional commercial office sector for raised access flooring and more recently, is supporting the accelerated growth in the data center industry. As one of the leading raised access flooring manufacturers, suppliers and installers across the US, Europe, Middle East and Australia, we have been involved in delivering a wide range of flooring solutions for some of the most prestigious buildings worldwide.
Our Tate brand of data center products has enabled us to support the leading technology and communication brands who rely on our team to innovate and deliver solutions. We continue to develop innovative products for use in commercial buildings and data centers, supported by our leading engineering, technical and specification team. Our commitment to sustainability is a key focus area for our business and one we continue to evolve as we grow and expand in line with our Planet Passionate Strategy.
Aboutthe Role
We are seeking an experienced AI Adoption Manager (Divisional) to play a critical role in shaping how AI drives efficiency and transformation across the business. This role will act as the bridge between business and technology, identifying and prioritizing high‑impact opportunities and translating them into scalable AI solutions delivered in partnership with engineering teams and third‑party vendors.
What You’ll Do AI Opportunity Identification & Pipeline Ownership- Work with functional leaders to surface high‑value ideas and convert them into structured use cases.
- Proactively identify opportunities to apply AI across business functions by analyzing workflows, inefficiencies and manual effort.
- Build and maintain a prioritized pipeline of AI initiatives based on impact, feasibility and ROI.
- Continuously generate new ideas by combining business insight with emerging AI capabilities.
- Act as the primary interface between business stakeholders and engineering teams.
- Translate business problems into clear AI requirements, use cases and solution briefs.
- Partner closely with AI engineers and third‑party vendors who design and deploy the solutions.
- Ensure alignment between intended business outcomes and technical delivery.
- Lead the end‑to‑end lifecycle of AI projects:
- Requirements gathering
- Pilot design and rollout
- Deployment coordination with engineering
- Own project governance
- Ensure delivery is focused on business value, not just implementation.
- Define success metrics upfront for every AI initiative (e.g., time saved, cost reduction, revenue uplift, accuracy improvements).
- Build robust frameworks to measure:
- Adoption and usage
- Productivity gains
- Track performance post‑deployment and ensure benefits are realized and sustained.
- Establish consistent reporting to demonstrate value and inform prioritization.
- Continuously refine use cases based on performance data.
- Drive adoption of AI solutions through structured rollout, training and engagement.
- Identify change impacts and ensure solutions are embedded into daily workflows.
- Develop playbooks, usage guidance and best‑practice frameworks.
- Support teams to integrate AI into how they work — not as a bolt‑on.
- Maintain a pipeline of pilots and rapid experiments with new AI tools and approaches.
- Work with engineers and third‑party vendors to test, validate and scale solutions quickly.
- Stay up to date on emerging AI technologies and assess practical business applications.
- Ensure a balance between experimentation and measurable outcomes.
- Ensure solutions are…
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