Kelly × Motion Consulting Group

AI Transformation

Foundational Sales Deck — Internal Working Draft

KELLY × MCG — AI Transformation Deck
Slide 1 of 13
Client-Facing Presentation — Foundational Deck v1

AI in the Enterprise

A practical guide to AI adoption — from strategy to execution — for technology leaders ready to move beyond the hype.

KELLY SERVICES × MOTION CONSULTING GROUP
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Slide 1

Why AI, Why Now

Three forces are converging to make AI adoption an urgent strategic priority — not a future initiative.

GenAI Crossed the Threshold
Large language models went from research curiosity to production-ready in 18 months. Claude, GPT-4, Gemini — these tools now write code, analyze contracts, triage tickets, and generate content at near-human quality. The cost of AI capability dropped 90% in two years.
Your Competitors Are Moving
McKinsey reports 72% of organizations have adopted AI in at least one function — up from 55% just one year prior. Companies that wait aren't standing still — they're falling behind. Early adopters are compounding advantages in speed, cost, and customer experience.
The Talent Math Changed
AI doesn't replace your workforce — it multiplies it. One developer with AI tooling produces what three did before. One analyst processes what a team used to handle. The companies that figure out human+AI pairing first will have an insurmountable talent advantage.
The bottom line: AI is no longer a "future bet." It's a present-day competitive weapon. The question isn't whether to adopt — it's how fast you can adopt responsibly.
Slide 2

Why Enterprises Are Hesitant

These concerns are real — and valid. The difference between success and failure is addressing them head-on, not ignoring them.

01Data exposure and security risks — proprietary data leaking through AI tools
02Compliance and regulatory uncertainty — evolving AI legislation across jurisdictions
03Accuracy issues and hallucinations — AI that confidently generates wrong answers
04Lack of explainability and control — "black box" decisions in regulated industries
05Reputation and brand risk — AI failures that make headlines
06Unclear ROI and business value — pilots that prove technology but not business cases
07Talent and capability gaps — not enough people who understand both AI and the business
08Integration complexity with legacy systems — AI can't run on broken data foundations
09Governance and operating model challenges — who owns AI strategy? IT? Business? Both?
10Change management and workforce impact — fear, resistance, and the human factor
Every one of these is solvable. The companies that succeed aren't the ones without concerns — they're the ones who built structured approaches to manage them.
Slide 3

Where Enterprises Are Using AI Today

Not hype. Not theory. Real deployments delivering measurable results across every major business function.

IT & Software Development
AI-assisted coding (Copilot, Claude Code), automated code review, test generation, CI/CD optimization, predictive defect detection. Teams report 30-50% cycle time reduction.
Customer Service & Support
AI-powered triage, automated ticket resolution, agent assist (real-time suggestions), sentiment analysis, knowledge base generation. 40-60% reduction in routine ticket volume.
Operations & Supply Chain
Demand forecasting, predictive maintenance, inventory optimization, quality inspection via computer vision, exception-based processing. 10-20% forecast accuracy improvement.
Sales, Marketing & Revenue
Lead scoring, content generation, proposal drafting, competitive intelligence, personalization at scale, pricing optimization. 15-25% conversion rate improvement.
Cybersecurity
AI-prioritized threat detection, automated triage, continuous risk scoring, false positive reduction. MTTD reduced 30-50%, false positives down 40-60%.
Finance & Revenue Operations
AI-driven order validation, billing accuracy, collections prioritization, anomaly detection. Invoice disputes reduced 30-50%, DSO reduced 5-12%.
Slide 4

Common Challenges

Most AI initiatives fail not because of technology — but because of these three gaps.

Data Readiness & Quality
"Garbage in, garbage out" is amplified 10x with AI. Most enterprises have data scattered across systems, inconsistent formats, and no clear ownership. AI can't fix data problems — it exposes them.
→ Start with data assessment, not tool selection
Security, Compliance & Governance
Who approves AI use? What data can touch AI tools? How do you audit AI decisions? Most enterprises have no AI governance framework — and regulators are watching.
→ Build governance before you scale
Unrealistic Expectations & Failed Pilots
The #1 mistake: pilots that prove technical feasibility but lack a business case. "We built a chatbot!" isn't a strategy. "We reduced call volume 40% and saved $2M" is.
→ Tie every initiative to a measurable business outcome
Slide 5

What "Good" Looks Like

We've studied the companies succeeding with AI. They share five characteristics.

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    Clear ownership and executive sponsorship. AI isn't an IT project — it's a business initiative with a named executive owner and board visibility.
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    Prioritized use cases tied to value streams. They don't try everything at once. They pick 2-3 high-impact areas where AI removes real friction — and prove them.
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    Measurable outcomes, not just technical metrics. They measure cycle time, revenue, cost savings, customer satisfaction — not model accuracy scores that mean nothing to the CFO.
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    Governance built in from day one. Data policies, AI usage guidelines, risk frameworks, and human oversight — before they scale, not after something breaks.
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    A culture of experimentation with guardrails. They treat AI as a continuous capability — pilot fast, measure honestly, kill what doesn't work, scale what does.
The companies ahead on AI didn't wait for perfect. They started with clear problems, small pilots, honest measurement, and the courage to scale what works.
Slide 6

Enterprise AI Considerations Framework

Four pillars that determine whether AI initiatives succeed or fail. Every engagement starts here.

Strategy Alignment
Is AI tied to business objectives? Do you have executive sponsorship? Are use cases prioritized by value, not novelty?
Data & Infrastructure
Is your data clean, accessible, and governed? Is your cloud infrastructure AI-ready? Can you move from pilot to production?
Talent & Operating Model
Do you have the right skills? Is there a clear operating model for AI? Who owns what? How do humans and AI agents work together?
Risk & Governance
Is there an AI governance framework? Are security, compliance, and ethics addressed? Can you audit AI decisions?
How we use this: Before recommending any technology or approach, we assess where you stand across all four pillars. This ensures we're solving the right problems — not selling solutions for problems you don't have.
Slide 7

Our Approach

Start small, prove value, scale what works. No boiling the ocean. Every phase has clear outcomes and go/no-go decisions.

Phase 0
Strategy & Alignment
→
Phase 1
Discover & Map
→
Phase 2
Prioritize & Design
→
Phase 3
Pilot & Learn
→
Phase 4
Scale & Evolve

Quick Wins (0-6 months)

  • —AI-assisted development workflows
  • —Automated code review & testing
  • —Knowledge base & documentation agents
  • —Customer service triage & routing

Strategic Bets (6-24 months)

  • —End-to-end agentic workflows
  • —AI-powered product features
  • —Platform modernization for AI at scale
  • —M&A technology integration playbooks
Key principle: We use outcome-based stage gates. Only scale pilots that hit agreed business thresholds. If something doesn't deliver measurable value, we pivot or stop — not throw more money at it.
Slide 8

Organizing for the Agentic Future

The next evolution: AI agents that own repeatable processes while humans handle strategy, exceptions, and relationships.

How We Align Agents

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    Align agents to value streams, not just systems. An "Order Triage Agent" owns the whole flow, not just one system.
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    Orchestrator + Specialist pattern. One agent coordinates the end-to-end flow; specialist agents handle specific tasks within it.
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    Agents take repeatable decisions; humans handle exceptions, relationships, and judgment calls.
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    Change first, tech second. Redesign roles, KPIs, and training before scaling agents — not after.

New Roles & Structures

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    Product Owner: owns value-stream outcomes and customer experience
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    Agent Owner: owns specific agent goals, behavior, guardrails, and performance
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    Cross-functional team: product + engineering + data/ML + SME + change lead
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    AI Council: lightweight governance for standards, reuse, and conflict resolution
Slide 9 — Case Study

AT&T: AI-Powered Cost Optimization

Query Probe Agent — Agentic AI for Compute Optimization
AT&T's Chief Data Office was spending tens of millions on Azure compute, LLM API calls, and data costs across AI and EDW applications. Motion/Kelly built an agentic AI application that intercepts queries at runtime, optimizes them, and dramatically reduces cost and execution time.
MCG Delivery
5
Consultants
$10M+
Annual Cost Base
Sig.
Runtime Reduction
Lower
Token & Data Cost
Healthcare: Conversational AI for Patient Support
A regional healthcare provider replaced an outdated rules-based chatbot with an AI assistant powered by Azure OpenAI. Three-person team delivered a HIPAA-compliant solution handling scheduling, intake, and routine support.
MCG Delivery
60%
Call Volume Reduced
70%
Faster Intake
3
Person Team
HIPAA
Compliant
Slide 10 — Case Studies

Proven Results Across Industries

Energy: AI Delivery at Enterprise Scale
An energy company launched a sweeping AI transformation tied to a $12B acquisition. Motion deployed senior consultants across data science, MLOps, and cloud infrastructure under SOWs to accelerate their AI roadmap.
Energy
$12B
Acquisition Scale
250+
Fortune 500 Clients
82
Client NPS
Biotech: AI-Powered Forecasting & Decision Intelligence
A global biotech leader modernized forecasting with NLP, automated ML pipelines, and cloud-native analytics. Transformed executive insight delivery into a scalable AI platform.
Biotech
Consulting Firm: Precision AI Teams at Scale
Stood up 10 AI hires in 3 months (scaled to 30+) for a consulting firm building GenAI capabilities. 1:5 submit-to-hire ratio — 60% conversion from submission to placement.
Professional Services
10→30+
AI Hires
60%
Submit-to-Hire
3 mo
Initial Ramp
Slide 11

Measurable ROI

Illustrative outcomes across value streams where AI delivers quantifiable business impact.

Order-to-Cash
30-50%
Invoice disputes reduced
5-12%
DSO reduced
Software Development
20-35%
Defect escape rate reduced
30-50%
Cycle time reduction
Cybersecurity
40-60%
False positives reduced
30-50%
MTTD reduced
HR / Hire-to-Retire
25-40%
Time-to-hire reduced
20-35%
Self-service adoption up
Manufacturing / ERP
10-20%
Forecast accuracy improved
20-35%
Expediting reduced
Data & Integration
30-50%
Data quality issues reduced
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Analytics trust increased
Slide 12

Where We Can Help

We're not selling a platform or a product. We bring execution, scale, and the practitioners who've done this before.

01
AI Readiness Assessment
Map your value streams, assess data readiness, identify high-impact AI opportunities, and build a prioritized roadmap tied to business outcomes.
02
Pilot Design & Execution
Scope, build, and deploy AI pilots with 8-12 week cycles, clear success metrics, and go/no-go stage gates. Prove value before you scale.
03
AI Talent & Team Augmentation
AI/ML engineers, data scientists, prompt engineers, and platform architects — embedded with your teams, not working in isolation.
04
Agent Development & Integration
Design, build, and deploy AI agents aligned to your value streams. Connect them to your existing tools — Jira, GitHub, Salesforce, ERP, CRM.
05
AI Governance & Operating Model
Build the policies, frameworks, and organizational structures that enable responsible AI at scale — before regulators require it.
06
Enterprise Rollout & Scaling
Take proven pilots to production across teams, products, and geographies with repeatable playbooks, training, and change management.
Our difference: Kelly + Motion Consulting Group is one of the 15 largest IT staffing and consulting firms in North America. We don't just advise — we deliver. We embed with your teams, build production-grade solutions, and stay until the outcomes are proven.
Let's Talk

Ready to Move from Strategy to Execution?

The first step is a conversation about your specific challenges, opportunities, and readiness. No pitch. No pressure. Just an honest assessment of where AI can move the needle for your business.

AI Readiness Workshop
Half-day working session to map opportunities and assess readiness
Pilot Scoping
Define one high-impact AI initiative with clear metrics and timeline
Executive Briefing
Custom presentation for your leadership on AI strategy specific to your industry
KELLY SERVICES × MOTION CONSULTING GROUP
Top 15 IT Staffing & Consulting • 250+ Fortune 500 Clients • 82 Client NPS