YASH Advisory | Enterprise & AI Transformation

The Future of Work Isn't Just Changing.
It's Accelerating.

In mathematics, the rate of change of acceleration is called "jerk." That is the moment enterprises are entering now. How should technology leaders prepare? The answer begins with Global Strategic Workforce Planning.

Explore the Jolt →
Change

CHANGE

I was always here... You stopped noticing.

TariffsEmbargosGeopoliticsAICOVIDCloudY2K
The Physics of Change
Velocity
Velocity: Change
Change has always been constant
Acceleration
Acceleration: Rate of Change
Technology compresses timelines
Jolt
"Jolt": Rate of Acceleration Change
AI is the Jolt, latest, not last!
Human Scale to UnIt
From Human Scale
to UnIt Evolution
The Four Reactions to the Jolt
Airbag

The Airbag Reaction

Caught Unawares

Stagnation & Loss of Relevance. The jolt hits and the enterprise deploys emergency measures.

Teams scramble to form committees. Pilots are discussed but not launched. Fear drives decisions. The organization protects what exists rather than building what is needed.
Parking Brake

The Parking Brake

Resisting the Trudge

High Friction & Slow Progress. The enterprise sees it coming but pulls the brake.

Legacy systems, organizational politics, and leadership misalignment create inertia. Committees form. Pilots stay as pilots. Cautious, risk-averse.
Propeller

The Propeller Reaction

Harnessing the Momentum

Strategic Agency & Fast Growth. Uses the jolt as fuel.

Pilots become production. Teams restructured around human+agent models. Investment in ELM, GSWP, and capability over headcount. Speed becomes a competitive advantage.
Jolt Giver

The Jolt Giver

Directing the New Next

Market Leadership & Autonomous Orchestration. Creates the disruption.

AI infrastructure is mature. The ELM is a proprietary asset. The enterprise publishes, leads, and attracts top talent. Defines disruption rather than responding to it.
Global Strategic Workforce Planning
Demand Generation at Large Enterprises

Five dimensions of demand flow through the organization. GSWP is how leaders design skills, talent, and capability for the AI era. The work gets executed across various tracks.

Run
Keep the lights on
Operational continuity, infrastructure management, steady-state delivery. Internal IT supporting business via the CIO function.
▾
Transform
Modernize & optimize
Legacy modernization, process re-engineering, platform migrations. Evolving what exists into what is needed.
▾
Expand
Scale to new markets
New geographies, customer segments, business lines. Growth demanding new operating models and regional deployment.
▾
M&A / Divestitures
Integrate & separate
Acquisitions, carve-outs, synergy realization, AI due diligence. High-stakes with hard deadlines.
▾
Digital
Digital products (CTO)
Product engineering, digital innovation, CTO-led initiatives. Innovation as a service. Building what the enterprise sells tomorrow.
▾
These demands flow through:
Outsourcing
HQ
Locations / Plants
GCCs
AI says: "I can do it all for FREE!!!!" (Psst... After you invest $1Bn...)
How Work Is Being Disrupted Across Tracks
Real Examples. Real Enterprises. Right Now.
SOFTWARE ENGINEERING
Product Owner
▶
LLM Platform
▶
Epics & Stories
▶
Developers
Human + Agent
▶
Testing & Deploy
70-80% accuracy across coding, test cases, story writing

A Large Bank Using Agents for Software Engineering

The product owner defines requirements. An LLM platform generates epics, stories, and code. Human developers work alongside agents in a pair-programming model.

Imagination, ideation, and hypothesis matter more than execution speed
WHAT matters more than HOW: articulating intent beats writing code
Abstract thinking and operating in gray zones become critical
Business-IT fusion: group outcomes over individual output
APPLICATION MANAGEMENT & SUPPORT
Ticket (P2-P3)
▶
SLM Agent
▶
Diagnosis
▶
Human Supervisor
▶
Resolution
SLMs focused on SAP and support systems knowledge only

A Large Manufacturer Running AI Agents on Production Tickets

P2-P3 support tickets assigned directly to SLM agents trained on SAP. Human supervisors govern the process, stepping in for edge cases.

Explaining logic matters more than writing code
Proactive monitoring and scenario building become key skills
Cross-module T-shape beats deep I-type specialization
Support lead assures VP Operations in Guadalajara
GLOBAL BUSINESS SERVICES
Global Demand
▶
Planning
▶
Forecast
▶
Procurement
▶
Distribution
$5Bn+ Service Parts Supply Chain | 1000s of dealers
Global logistics

AI Tools Driving Outcomes in a Backend Logistics Center

Mining machines, earth-moving equipment, trucks. AI handles demand forecasting and procurement across the $5Bn+ service parts supply chain.

Humans focus on Fill Rate %, not data entry
Inventory turns and working capital become the human domain
Revenue protection replaces routine execution
From doing the work to governing trade-offs
From Execution to Orchestration
Tying It All Together

Across every track, the same three-stage evolution plays out. The destination: humans govern the work, they don't do it.

01
Current State
Human-Intensive, Manual
Ticket-driven, procedurally efficient. High headcount, deep process, reliable output.
Engineering teams of 50+ per project
AMS managing ticket queues manually
GBS executing procurement step by step
02
AI-Augmented
Shift-Down
Agent-led execution. Humans define and design. System availability, not SLAs. Data-driven decisions with domain awareness.
Agent-led code gen, human-led architecture
SLMs resolve P2-P3; humans handle P1 & change
AI forecasting; humans govern trade-offs
03
Business Orchestration
The New Enterprise
Business-IT led orchestration. Guaranteed stability. Human focus on change. They govern trade-offs: service, capital, risk.
Business-IT fusion as the operating model
Guaranteed stability; humans focus on change
They are not doing the work. They are governing it.
When humans are freed to focus on change, change itself accelerates, and agility becomes the enterprise's competitive advantage.
Reality Check
How Far Away Is This Future?

Few Say It's Around the Corner

  • "Digital coworker" sparking anxiety in $250Bn IT services
  • 400K+ tech employees laid off since 2022
  • 5-15% headcount cuts, AI roles up double-digits
  • Auto-documentation tools generating code wikis
VS

The Reality on the Ground

  • An agent may cost $100K+ in tokens for specific functions
  • 80% B2B trade via EDI, not modern APIs
  • 65%+ airline systems on legacy mainframes
  • 200B+ lines of COBOL in active production
AI is still just a tool. Its application still belongs to humans, as always.
Building Trust
1
Human ↔ AI Trust
Can teams trust the agent's output? Do they know when to override?
Upskilling: Coders → ArchitectsGamified Learning: Hackathons & Prompt-a-thonsPsych Safety: Open Communication
Fear of Replacement → Collaborative Empowerment
2
HQ ↔ HQ2 (GCC) Trust
Does HQ trust the capability center to make autonomous decisions?
Strategic Parity: Not just execution armShared OKRs: Co-ownership of outcomesInnovation Equity: IP co-creation rights
Back-office Subsidiary → Co-equal Strategic Hub
3
Leadership ↔ Teams
Do leaders trust teams to experiment and fail safely?
Vision Clarity: Why are we building this?Career Pathways: Growth in AI-augmented rolesEmpowerment: Autonomy with accountability
Directive Hierarchy → Empowered Builders
4
Leadership ↔ Partners
Can the enterprise and its partners build shared intelligence?
Contextual Insight: Domain & industry depthIn the Trenches: Execution partnershipIn the Boardroom: Strategic advisory
Vendor Relationship → Partner in Trenches & Boardroom
Enterprise Language Model
(ELM)
GLM General Language Model
LLM Large Language Model
SLM Small Language Model
DLM Domain Language Model
AI Orchestrator
AI OrchestratorGoverns which model handles which task. Prevents headless chicken agents.
Industry Vertical
Business Processes
IT Systems
Nature of Work
No headless chicken

Avoiding the 'Headless Chicken' Scenario

Without governance, FinOps for AI, and orchestration, agents proliferate unmanaged, creating the same chaos as citizen-developed RPA bots.

Orchestrated: Aligned, Compliant, Effective Unmanaged: Headless Chicken Risk
The Hybrid Model Zoo

Enterprise Language Model

An ELM is built exclusively for one organization, trained on its specific processes, systems, data, and domain context. It knows everything about that company and nothing it doesn't need to. It wraps GLMs, LLMs, SLMs, and DLMs under one orchestration layer.

GLMs provide broad general knowledge; LLMs handle complex reasoning; SLMs tuned for verticals and processes; DLMs encode deep domain expertise
Nuanced for industry (FinTech vs Manufacturing), process (HR vs Procurement), systems (SAP vs Oracle vs Salesforce), and nature of work
Governance, FinOps for AI, and orchestration prevent unmanaged agent proliferation
Enterprise-specific context makes the ELM a proprietary competitive moat
The ELM powers the UnIt model: every role paired with a context-aware AI agent
The Destination
There is no Human vs. AI. It is:
UnIt
Human (U) and (n) Agents (It)
From Human Scale to UnIt
Where the unit of work is neither purely human nor purely machine, but both working as one. The enterprise that builds this capability first will move faster than every competitor still counting headcount.
🧑
U (Human)
+
🤖
n Agents (It)
=
UnIt
Capability

Ready to Plan Your Workforce for This Shift?

This conversation begins with what we are seeing across industries. The question is: how are you, from your leadership vantage point, interpreting and responding to this shift?

Revisit the Jolt ↑
Y
Enterprise Advisor
YASH Advisory