Designing the Digital Backbone
A Transformation Strategy for Taylor Inc.
95 Years of Growth.
A Defining Moment for Transformation.
Taylor Inc. has grown organically for decades into an enterprise-scale operation — over 300 employees and a global footprint across 40+ countries. This growth has been powered by processes built organically over time. Not through a unified system, but through the strength, experience, and adaptability of its people.
The question is no longer whether Taylor has succeeded — it’s what becomes possible when that capability is supported by the right digital foundation.
The experiential marketing industry is at an inflection point representing a $128B global market. The landscape is shifting: faster execution, shorter timelines, higher expectations. The companies that will thrive are those with smarter, more efficient systems — designed to enable consistent, scalable execution.
Taylor stands at the edge of a meaningful opportunity — to design the system that enables its next phase of growth.
From Technology Adoption
to System-Level Performance
We are in the midst of a new industrial shift — the most significant transformation in operating models since the digital and industrial revolutions. This shift is driven by the convergence of artificial intelligence, data, automation, and connected systems. But its impact is not primarily technological. It is organizational.
Most organizations are still approaching this shift through a Reactive Adoption* — introducing tools, piloting use cases, and enabling teams to experiment. The data reflects the limitations of this approach:
Only ~20 – 30% of AI initiatives move beyond pilot into scaled deployment. Most value remains trapped in isolated use cases, rather than embedded into core workflows across the business.
A different pattern is emerging among leading organizations. They are not focused on point solutions but on Designed Transformation** — reviewing workflows alongside technology adoption, investing in data integration and cross-functional visibility, and scaling solutions across the enterprise rather than within individual teams.
| Dimension | Reactive Adoption* | Designed Transformation** |
|---|---|---|
| Starting Point | Tools & use cases | End-to-end workflows |
| Focus | Task optimization | System redesign |
| Implementation | Pilots & experiments | Scaled transformation |
| AI Role | Assistant to individuals | Embedded in execution |
| Data | Fragmented across systems | Integrated across functions |
| Impact | Localized improvements | Enterprise-wide performance |
| Scalability | Limited | Built to scale |
| Outcome | More activity | Better performance |
Every quarter without a unified digital strategy compounds inefficiencies, increases cost, and widens the competitive gap.
The gap is no longer defined by access to technology. It is defined by how the operation itself is designed.
One connected operation
Taylor’s operation is structured across three core modes of value creation: Create, Build, and Execute. These modes define how ideas are developed, how they are produced, and how they are ultimately delivered.
They are supported by five foundational layers — People & Culture, Processes, Technology, Data, and Compliance — each playing a critical role in how the business runs.
The Digital Backbone connects these modes and layers into a single, coordinated system — enabling workflows to move end-to-end, data to flow in real time, and decisions to be made with full visibility.
The Digital Backbone
addresses this gap
It introduces a connected operational layer that links how work is created, built, and executed across the organization. Rather than adding new systems, it connects existing ones — enabling workflows to move end-to-end, data to flow in real time, and decisions to be made with full visibility.
It integrates the three modes of operation with the five foundational layers — turning them into a single, coordinated system.
Create
+More informed. Faster. Grounded in reality.
Ideas start from scratch, proposals assembled manually, institutional knowledge lives in people’s heads
Institutional memory searchable, proposals accelerated, creative ambition grounded in operational reality
Build
+More coordinated. Predictable. Reusable.
Sales sells blind, assets rebuilt because nobody knows they exist, each office runs its own way
Design-to-build continuity, searchable asset inventory, planned production, cost intelligence from history
Execute
+More visible. More coordinated. Learning over time.
Status via email chains, reactive fire-fighting, post-event learnings lost, no unified view
Real-time visibility, connected execution, organizational learning compounds over time
How Digital Backbone Works
The backbone is built on three core layers — each with a distinct role in how the operation runs.
How Work Moves
Automation connects systems and orchestrates workflows across Create, Build, and Execute.
Brief → proposal → project setup flows automatically
Handoffs triggered by status, not coordination
Systems remain synchronized across tools
Repetitive tasks (updates, routing, approvals) removed
→ Work moves end-to-end with only necessary manual intervention
What the business knows
Data creates a structured, shared foundation across all layers of the operation.
Projects, clients, assets, and costs captured in a unified model
Historical data reusable across proposals and planning
Real-time visibility across teams, locations, and projects
Reporting generated continuously, not manually
→ The organization operates on a single source of truth
How the system improves
AI operates across the backbone in different forms — each enhancing a specific part of the workflow.
AI Copilots — Assist Work in Real Time
Embedded within tools to support day-to-day execution
Drafting proposals, briefs, and documentation
Assisting with planning, coordination, and communication
Providing contextual suggestions based on live data
→ Speeds up individual productivity
Generative AI — Create & Synthesize
Transforms existing data and knowledge into usable outputs
Generates concepts, content, and structured deliverables
Synthesizes past projects, reports, and learnings
Expands ideas and connects inputs across sources
→ Accelerates creation and knowledge reuse
Analytical AI — Understand & Predict
Turns operational data into foresight and decision support
Forecasts capacity, costs, and project risk
Identifies patterns across historical builds and outcomes
Enables proactive planning instead of reactive adjustment
→ Improves decision quality across the operation
Autonomous AI — Act & Orchestrate
Executes routine decisions and coordinates workflows independently
Routes tasks, triggers handoffs, and manages sequencing
Monitors live project status and flags exceptions
Reduces manual coordination across teams and tools
→ Accelerates creation and knowledge reuse
An Engineered
Transformation
The Digital Backbone is not deployed. It is built.
The transformation is not approached as a technology rollout — but as a redesign of how the organization operates.
It is structured, phase-based, and built to move from initial alignment to full system activation — ensuring that change is not introduced in isolation, but embedded across the business.
The approach follows a clear progression:
- Alignment before action
- Understanding before design
- Validation before transformation
- Execution based on evidence
- Stabilization & Optimization
Each phase builds on the previous one — reducing ambiguity, increasing precision, and enabling confident decision-making.
This framework:
- Moves from fragmented understanding to validated operational clarity
- Ensures decisions are grounded in evidence, not assumption
- Connects workflows, systems, data, and decision-making
- Enables transformation to be sequenced, not improvised
- Aligns leadership, teams, and execution under a shared system
The Phases
Each phase is designed to build on the previous one — reducing ambiguity, increasing precision, and enabling confident decision-making at every stage.
Pre-engagement
Alignment is established across scope, structure, and expectations. This phase defines how we will work together — and concludes with the formalization of the engagement.
Key Milestones
- Define Scope & Objectives
- Establish Governance & Ways of Working
- Confirm Team & Resourcing
- Finalize Commercials & Agreement
Outcome
Signed agreement and engagement ready to begin
See more detailsIntake
The Intake phase sets the foundation for Discovery. It focuses on collecting information, gaining access to systems, and building an initial understanding of how the business operates — before any on-site work begins.
Key Milestones
- Set up data room & information structure
- Collect core documentation (SOPs, org, systems)
- Gain access to tools, platforms, and workflows
- Review documentation and data flows
- Define Discovery plan (interviews, workflows, focus areas)
- Initiate leadership alignment & early engagement
Outcome
Structured understanding of the current operation and a fully defined Discovery plan
See more detailsDiscovery
The Discovery phase translates structured inputs into validated operational understanding. Through on-site observation, stakeholder interviews, and workflow validation, this phase identifies where value is created, where friction accumulates, and where transformation should be focused.
Key Milestones
- Conduct kickoff and align Discovery agenda
- Execute leadership and functional interviews
- Observe operations on-site (core workflows)
- Validate workflows, systems, and decision-making
- Identify gaps, redundancies, and breakdowns
- Consolidate findings and validate with leadership
Outcome
A validated, end-to-end diagnosis of workflows, systems, data, and decision-making. A prioritized and sequenced set of initiatives defining how the operation will be redesigned.
See more detailsImplementation
The Implementation phase activates the transformation roadmap defined during Discovery. At this stage, the Digital Backbone is built in practice — redesigning workflows, connecting systems, and embedding new ways of operating across the organization.
The exact scope, sequencing, and priorities of this phase are defined based on the validated findings and roadmap produced in Discovery.
Key Milestones
- Prioritized initiatives based on Discovery outputs
- Workflow redesign and system activation
- Integration across tools, data, and teams
- Rollout of new operating model
- Governance and performance tracking established
Outcome
Activated platforms, built automations, and trained champions.
Stabilization & Optimization
Workflows are implemented, systems are connected, and the Digital Backbone is embedded into day-to-day operations. The focus is not only on execution — but on ensuring that the new operating model performs consistently and improves over time.
Key Milestones
- Adoption monitoring
- Optimization recommendations
- Quarterly reviews
- Knowledge transfer
Change enablement
Transformation does not happen through design alone — it happens through adoption.
This layer runs across the entire engagement, ensuring alignment, clarity, and engagement at every stage. It supports how the transformation is understood, communicated, and embedded within the organization.
From early stakeholder alignment to ongoing communication and reinforcement, this phase ensures that changes are not only implemented — but sustained.
Key Milestones
- Leadership aligned on transformation intent and priorities
- Key stakeholders identified and engaged
- Adoption Path Defined
- Clear behaviors and ways of working outlined
- Communication structure established
- Change plan integrated into transformation roadmap
Defining the Path Forward
The Transformation Path is structured as a dual service model, enabling Taylor to move from Diagnosis to Execution with clarity and control. Each service is defined independently, with clear deliverables and a structured investment model.
or execution
optimization
Diagnosis Engagement Models
The following engagement models define how Taylor can access a full operational diagnosis — with or without change enablement.
Core Diagnosis
Full operational visibility across Create, Build, and Execute
What Taylor receives
- Operational X-Ray across Create / Build / Execute workflows
- Quantified breakdown of inefficiencies, friction points, and lost value
- End-to-end System Map (workflows, systems, data flows, decision layers)
- Prioritized Transformation Roadmap (what to change, in what order, and why)
- Investment Logic to support strategic and operational decisions
How it’s built
- Structured Intake (remote)
- 3-week on-site operational immersion
- Leadership & functional interviews
- Workflow & systems assessment (end-to-end)
- Data & tools review (integration, usage, gaps)
- Validation sessions with leadership
- Analysis, synthesis & roadmap design
Diagnosis + Change Enablement
Everything in Core Diagnosis, plus the strategy to make change stick
What Taylor receives
- Everything included in Core Diagnosis
- Communication & Change Strategy aligned to the roadmap
- Leadership Alignment & Messaging framework
- Stakeholder Engagement structure (who, when, how)
- Adoption Plan (behaviors, tracking, reinforcement)
- Ongoing Communication Cadence across teams
How it’s built
- Integrated alongside Diagnosis (not post-phase)
- Leadership alignment sessions
- Stakeholder mapping & engagement design
- Communication planning & narrative development
- Adoption & behavior definition
- Reinforcement mechanisms design
What Initial Interviews Revealed
The new paradigm is not theoretical. It is already playing out across Taylor’s day-to-day operations.
Initial conversations point to a clear pattern: Not a lack of capability — but a lack of system-level connection.
High Impact
“We probably have somewhere between $20 and $30 million worth of assets in our warehouses. And honestly, I couldn’t tell you exactly what we have.”
Without centralized asset tracking, teams rebuild what already exists across three warehouses and 40+ countries.
Estimated 5 – 10% annual inefficiency from lost, unused, or duplicated assets. Additional costs from redundant fabrication and logistics.
“Sales will sell something and then production finds out and goes, ‘Wait, we can’t do that in that timeline.’”
Sales operates without visibility into production capacity, leading to over-promises and margin erosion.
Based on observed workflow patterns and typical cost structures in custom build environments:
- 15 – 30% of projects are impacted by sales ↔ production misalignment
- Each affected project incurs $10K–$25K in combined impact (redesign, rush production and logistics, margin compression)
- Based on an estimated 40 – 80 projects per quarter
Medium Impact
“I get 400 to 500 emails a day. I’d say 70% are CCs where I don’t really need to be included.”
Senior leadership time is consumed by low-value communication — reducing focus on strategic decisions and operational alignment.
400 – 500 emails/day with ~70% low-value or unnecessary. Estimated 2 – 3 hours/day of executive time lost. Based on average executive cost of $150–$250/hour.
“We’re paying for ClickUp Brain but nobody’s really using it.”
Technology investments are already in place — but without adoption, integration, and shared workflows, their value erodes over time.
~$60K–$130K annual investment across core tools (ClickUp, M365, AI tools). Estimated 40 – 70% underutilization due to fragmented adoption and lack of integration. Lost productivity and coordination value typically 2 – 3x tool cost.
Strategic
“The younger employees pick things up instantly. Some of our senior people… it’s a harder conversation.”
Technology adoption is inconsistent across the organization. Junior and technical teams adopt new tools quickly. But adoption at the management layer lags — creating bottlenecks in approvals, decision-making, and execution flow.
~20 – 30 managers involved in approvals / decision layers. 5 – 10 hours/week lost per manager due to friction (tool gaps, back-and-forth, manual processes). Avg loaded cost: ~$120 – 150/hour.
“We don’t have a CTO. We’ve never had someone whose job is to look at all of this holistically.”
Technology decisions are made in isolation — by function, by need, and in real time. Without a central owner, tools, data, and workflows evolve independently rather than as a system.
Direct Waste: $15K–$60K. Workarounds: $90K–$300K. Lost leverage: $100K–$400K.
* Findings are based on initial conversations and directional estimates. Actual impact may vary — figures will be validated and refined as part of the diagnostic phase.
Pre-Engagement + Intake + Discovery
Each phase is designed with a clear boundary: defined inputs, a structured operating model, and explicit exclusions. Nothing begins without the previous phase being complete.
This section details the three initial phases — what each one does, how it operates, and what it deliberately does not do.
Pre-Engagement
+Alignment Becomes Commitment
This phase formalizes the foundation on which the transformation will operate. It translates initial conversations into a structured engagement model — defining scope, governance, resourcing, and ways of working. Rather than beginning work prematurely, it reduces ambiguity across strategic, operational, and contractual layers.
Operating Model
- Executive alignment across transformation objectives and scope
- Definition of governance structure (Sponsor, SteerCo, decision model)
- Establishment of communication cadence and reporting structure
- Alignment on tools, collaboration model, and ways of working
- Definition of team structure and resource allocation
- Commercial alignment (pricing, scope, terms)
- Iterative working sessions with leadership to validate decisions
What This Phase Does Not Do
- No operational assessment or workflow analysis
- No data collection or system deep dives
- No hypothesis building or diagnostics
- No implementation or solution design
Intake
+Setting the Ground for Discovery
This phase establishes a controlled environment where fragmented information — across workflows, systems, data, and organizational structure — is centralized, validated, and made analyzable. The objective is not to solve — but to reduce ambiguity to a level where targeted intervention becomes possible.
Operating Model
- Fully remote execution
- Early engagement with leadership group
- Initial cadence of working sessions established
- Stakeholders identified and aligned
- Discovery sessions pre-scheduled
What This Phase Does Not Do
- No process redesign
- No system changes
- No implementation decisions
This phase builds clarity — not solutions.
Discovery
+From Visibility to Validated Understanding
Building on the foundation established during Intake, this phase moves into direct observation and engagement with the organization. Rather than relying on documentation or perception, Discovery tests assumptions against reality — identifying where workflows break, where decisions stall, and where inefficiencies compound. This phase produces a validated diagnosis of the current state — grounded in evidence, not interpretation.
Operating Model
- Hybrid execution (on-site + remote)
- High-touch engagement with leadership and teams
- Direct observation of workflows in action
- Iterative validation of findings with stakeholders
- Continuous refinement of hypotheses
Outcome
- Current-State Report — A validated, end-to-end diagnosis of workflows, systems, data, and decision-making
- Transformation Roadmap — A prioritized and sequenced set of initiatives defining how the operation will be redesigned
The AI Transformation Landscape
AI has moved from experimentation to strategic priority — yet most organizations remain far from realizing its full impact. 88% of enterprises now use AI — but only 34% are truly reimagining their business. Here’s where the industry stands, and why Taylor’s timing is strategic.
01
Adoption is widespread, but impact is concentrated
- ~60% of organizations report using AI in at least one function
- Fewer than 25% achieve meaningful bottom-line impact
- Less than 10% have scaled AI across multiple business units
AI is no longer a question of if, but of how effectively it is deployed.
02
Scaling AI remains the primary challenge
- The majority of AI initiatives remain at pilot or use-case level
- Only a small subset of companies successfully scale AI enterprise-wide
- Common barriers: fragmented data environments, lack of integration across systems, organizational misalignment
The challenge is not building AI — it is operationalizing it.
03
Technology investment is increasing without structural change
- Global investment in AI and digital transformation continues to grow significantly
- Most organizations add new tools without removing legacy systems
- AI is layered on top of existing workflows without redesigning how work gets done
As a result, complexity increases faster than capability.
04
Data readiness is the limiting factor
- A majority of organizations cite data quality and accessibility as primary constraints
- Data remains siloed across systems, inconsistently structured, and difficult to activate in real time
Without a unified data layer, AI cannot scale effectively.
05
Leaders operate differently, not just better
- 2 – 3x more likely to capture significant value from AI
- Operate on integrated data and technology ecosystems
- Embed AI directly into workflows and decision-making
The advantage is not AI itself, but the system that enables it.
06
The gap is widening
- Early movers capture disproportionate value as leading organizations scale integrated capabilities
- Late adopters face higher costs and slower implementation
- The performance gap compounds over time
Others accumulate fragmented initiatives while leaders pull further ahead.
Phase of AI use among organizations using AI in 2025
- Core systems, data, and workflows are connected
- Real-time visibility across sales, assets, and production
- A single operational layer replaces disconnected data, AI and automation
- AI embedded across core workflows (sales, planning, coordination)
- Automated follow-ups, recommendations, and decision support
- Teams operate with system-assisted execution
- AI agents coordinate tasks across functions
- Generative systems support design, planning, and production inputs
- Execution flows with necessary manual intervention
- Digital replicas of spaces, assets, and workflows
- Ability to test layouts, experiences, and production scenarios before execution
- Continuous optimization through simulation
- Fully integrated, end-to-end operating model
- AI orchestrates from concept to delivery
- Taylor sets the standard for how the industry operates
The gap isn’t between companies that use AI and those that don’t. It’s between those that connected their AI into a backbone — and those still running pilots.
What Your Competitors Are Doing
Jack Morton + Impact XM
Merged in January 2026 to create a larger, end-to-end experiential platform. Beyond scale, the intent is explicit:
“This is a pivotal moment. Together, we’re building an agency ready for a marketing landscape reshaped by AI — one that keeps real, human experience at the center.”— Craig Millon, CEO
- Consolidating capabilities across the full lifecycle
- Strengthening data and measurement to prove impact
- Positioning the organization for an AI-driven marketing landscape
They’re not just getting bigger — they’re preparing for an AI-shaped industry.
Freeman
Freeman is actively embedding AI across its operating model — from event execution and audience insights to content intelligence and digital marketing capabilities.
- AI Solutions powering real-time audience behavior mapping and engagement analytics
- Key Takeaways — a generative AI tool that transforms live sessions into structured insights and post-event content
- Zenus partnership enabling AI-driven behavioral data (dwell time, sentiment, engagement) at scale
- Tag Digital acquisition expanding AI-driven digital marketing capabilities globally
“Key Takeaways uses generative AI to capture, summarize and extend content from live sessions, improving engagement, learning and retention.”
They’re not experimenting — they’re operating with AI today.
George P. Johnson (GPJ)
Part of Project Worldwide, GPJ is building integrated experience capabilities across the full lifecycle — from strategy and design to production and delivery. Their model connects physical and digital environments, combining data, creativity, technology, and execution into a unified experience layer.
“We bring together data, creativity, technology and production to create connected brand experiences.”— Project Worldwide
They’re embedding tech into every step, not just the final event.
The Transformation Reality
Only 16% of digital transformations fully succeed — McKinsey 2025
Only 39% report enterprise-level EBIT impact from AI
53% average productivity boost in Lighthouse factories — McKinsey/WEF 2025
~75% of advanced-industry companies have adopted digital twin technology
Everyone is buying AI tools. Almost nobody is connecting them.
Supporting Research & References
Every data point in this proposal is drawn from published, peer-reviewed, or analyst-verified research. Sources are grouped by topic below.
AI Adoption & Impact
McKinsey • WEF • Accenture| Claim | Source | Link |
|---|---|---|
| 88% of orgs use AI in at least one function (up from 78% YoY) | McKinsey — The State of AI in 2025 | mckinsey.com ↗ |
| 39% report measurable EBIT impact from AI | McKinsey — The State of AI in 2025 | mckinsey.com ↗ |
| 53% labour productivity boost; 26% cost reduction (Lighthouse factories) | McKinsey / WEF — Global Lighthouse Network 2025 | mckinsey.com ↗ |
| 97% of executives believe AI will transform their company and industry | Accenture — Reinvent Enterprise Models with Generative AI | accenture.com ↗ |
| 93% say AI investments are outperforming other strategic areas | Accenture — Reinvent Enterprise Models with Generative AI | accenture.com ↗ |
| Only 16% of digital transformations fully succeed | McKinsey — Unlocking Success in Digital Transformations | mckinsey.com (PDF) ↗ |
Manufacturing & Industrial AI
IDC • MarketsandMarkets • Deloitte| Claim | Source | Link |
|---|---|---|
| By 2026: 45% of G2000 OEMs connect field + engineering data via AI | IDC — FutureScape 2026: Manufacturing Predictions | blogs.idc.com ↗ |
| AI in manufacturing: $34B → $155B by 2030 (35.3% CAGR) | MarketsandMarkets — AI in Manufacturing Market Report | marketsandmarkets.com ↗ |
| Digital twins: $24.5B market, ~75% adoption in advanced industries | McKinsey — Product Digital Twins | mckinsey.com ↗ |
| Predictive maintenance delivers 300 – 500% ROI | Deloitte — Predictive Maintenance and the Smart Factory | deloitte.com (PDF) ↗ |
Experiential Marketing Industry
EventTrack • IPA| Claim | Source | Link |
|---|---|---|
| $128B global experiential marketing spend (2024) | Industry aggregators — Experiential Marketing Statistics 2026 | kandephotobooths.com ↗ |
| 74% of Fortune 1000 marketers plan to increase experiential budgets | EventTrack 2025 | atneventstaffing.com ↗ |
| Event budgets growing +10.9% while overall B2B marketing declining -3.1% | IPA — Bellwether Report 2025 | salmonlabs.co ↗ |
Digital Transformation Benchmarks
Cost of Waiting sources| Claim | Source | Link |
|---|---|---|
| 20 – 30% lower efficiency in low digital maturity organizations | McKinsey — Unlocking Success in Digital Transformations | mckinsey.com (PDF) ↗ |
| 15 – 25% higher costs from fragmented systems and manual processes | BCG — Flipping the Odds of Digital Transformation Success | bcg.com ↗ |
| 2 – 3x higher revenue growth for digitally mature organizations | MIT Sloan / Capgemini — Leading Digital (Westerman et al.) | mitsloan.mit.edu ↗ |
| 30 – 50% faster execution for digitally enabled organizations | McKinsey — The Case for Digital Reinvention | mckinsey.com ↗ |
Technology Platforms
Microsoft • Industry| Claim | Source | Link |
|---|---|---|
| Microsoft 365 Copilot ships agentic capabilities at $30/user/month | Microsoft — 2026 Wave 1 for Dynamics 365 | microsoft.com ↗ |
| Jack Morton + Impact XM merged (Jan 2026) | Industry news — Competitors consolidating around data platforms | eventmarketer.com ↗ |
All links verified as of March 2026. Sources are publicly available publications from recognized research firms, industry bodies, and technology providers.