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AI strategy & implementation
Enterprise Agentic AI for
Workshops and trainings
AI Strategy & Implementation
Every success accompanied by four failures
When thinking about AI implementation, discussion very often wrongly starts with technology. Most AI programs don’t stall because of models—they stall because of method. Global research points to high failure rates and limited returns when the hard parts—employee engagement, training, and governance—are skipped.
Think of AI not as a one-time surgery but as physiotherapy: outcomes improve only with a plan, steady practice, and clear milestones. Enterprise AI also isn’t plug-and-play. It meets outdated systems, regulatory guardrails, risk-averse culture, hiring gaps, and procurement hoops. The opportunity is to embed AI into everyday work—product, operations, compliance, HR, finance—so many small, safe improvements add up to durable, measurable value.
The data supports a human-augmenting approach; attempts to replace people often backfire, while involving teams early raises adoption and results.
Your Challenge: AI sounds great in theory, but enterprise reality - old systems, regulation, skills gap, resistant culture - makes it messy.
Our Promise: A modular, step-by-step program that reduces risk, builds buy-in, and delivers measurable results.
Choose your plan
Example use cases
Customer support triage & drafting
Cluster tickets, suggest replies grounded in your help center/policies, and route to the right queue - agents approve and send.
HR - automate what’s boring
Pre-screen incoming CVs against clear criteria; surface top matches with short rationales; generate role-specific checklists, first-week plans, and FAQ answers from your docs
Sales - lead qualification and activation
Score inbound leads, draft short, context-aware outreach, plan the next best touches: follow-ups stalled deals reactivation
Quality & compliance policy assistant
Draft and update quality/privacy/security policies, map clauses to evidence, and prep sections for ISO applications with a simple change log.
Foundations 🥉
Outcome
Shared baseline and alignment; clear next steps
Process
Uncovering company resources
Analyzing hidden capabilities
AI strategy workshops with key stakeholders
Drafting implementation roadmap
Deliverables
AI Readiness Assessment Report
1-page Adoption Brief
RACI map
High-Level Design (HLD) implementation plan
Proof of Value 🥈
Includes Foundations plus:
Outcome
Working pilot in a live workflow with measurable impact
Process
Selecting pilot success metrics
Mapping the live workflow and integration points
Connecting required systems and preparing data access
Training roles with quick-start cards and launching the pilot
Monitoring usage, fixing obvious snags, and evaluating impact
Deliverables
Live thin-slice pilot
Governance, security & privacy rules
Evaluation checklist & quick-start cards
Low-Level Design (LLD)
Company-adapted AI Innovation SOPs
Enterprise Rollout 🥇
Includes Proof of Value plus:
Outcome
AI embedded in daily operations, governed and measurable
Process
Prioritizing additional workflows and rollout waves
Standardizing guardrails and access controls across teams
Creating runbooks and enablement assets for repeatability
Standing up a KPI mini-dashboard and alerting
Running monthly reviews and driving continuous improvements
Deliverables
KPI Mini-Dashboard Tracking 2–3 Critical Metrics
Improvement backlog (prioritized list)
Full Low-Level Design (LLD) For The AI Innovation Engine
Monthly Review Pack And Decision Logs
Adoption Playbook And Enablement Assets
Rollout Plan For Additional Workflows
ENTERPRISE AGENTIC AI
Few teams have agents embedded in their daily workflows; governance and observability are the common blockers.
Your challenge
Agents are powerful but only with clear decision rights, ground truth, and auditability.
Left unchecked, they create chaos: spam customers, make unreviewed changes, or touch data they shouldn’t. Practical agents run on least-privilege access, use system-of-record data or RAG, and expose explanations, logs, and handoffs when confidence is low or policy requires approval.
Integrating AI Agents is more than a usual technology project, it is transformational! It requires both AI and Change Management skills and knowledge that are not the ones usually present in most companies.
You have the choice to Build, Borrow or Buy these competencies:
You have to decide whether to change the focus of your team from their current responsibilities, hire new team members, or take shortcut and partner with us
But whatever you decide, know that you will go through this transformation once, when we do it as our daily duty.
We offer you to speed up the learning curve and/or the implementation timeframe.
Our promise
Agents that are explainable, governed, and human-approved. They act where it matters, and hand off when it doesn’t. 3 packages that adapt the service to your particular needs: Bronze, Silver and Gold.
Sales & Marketing Agents
Lead Qualification & Outreach Agent
Problem
Reps waste time on low-fit leads and generic outreach.
How it works
Agent scores inbound leads for fit/intent, drafts short, brand-safe messages, and queues tasks in the CRM for review.
Inputs
CRM fields, recent activity (opens/clicks), ICP rules, brand voice guide.
Outputs
Lead score + rationale, suggested subject/body, next task with due date.
Guardrails
Never sends without review; respects do-not-contact lists; logs every suggestion.
Deliverables
Agent Spec (allowed actions, thresholds, review points)
Rollout Playbook (pilot cohort → phased expansion)
Pipeline Reactivation (Activity Analytics)
Problem
Dormant deals go unnoticed; reps don’t know the next move.
How it works
Agent flags stalled opportunities, proposes tailored re-engagement steps, drops them for rep approval.
Inputs
CRM activity history available, persona tags, objection library, win themes.
Outputs
Re-engagement task, suggested message, coaching tip (“mention X case study”).
Guardrails
Caps weekly nudges; respects stage rules and opt-outs; full audit trail.
Deliverables
Stall Detection Rules
Reactivation Dashboard (revived count, stage, opt-out rate)
Sales Co-Pilot (Recommendations Before Calls)
Problem
Prep is slow; insights are scattered.
How it works
Agent compiles a one-page brief: account activity, likely objections, and next-best actions.
Inputs
CRM timeline of interactions with a given lead, product usage (if any), notes, content library.
Outputs
Brief (PDF/HTML), two next-best actions based on interaction history, links to assets.
Guardrails
No edits to CRM possible without approval; source links for every claim.
Deliverables
Asset Map (which content to pull, by stage/persona)
Usage Telemetry (opens, actions taken, feedback)
Regulatory & Compliance Agents
Regulatory Filing Assistant (RAG) Agent
Problem
Filing is slow and prone to multiple errors; source tracking is manual.
How it works
Assistant drafts sections from up-to-date policies/guidance and prior submissions, with citations for reviewer edits.
Inputs
Policy corpus, regulator guidance, past filings.
Outputs
Draft sections with citations, change log, reviewer checklist.
Guardrails
No submission allowed; flags uncertain claims; version history kept.
Deliverables
Filing Template Pack (sections, required fields)
Reviewer Checklist (items to verify each time)
Continuous Monitoring & Risk Alerts
Problem
Controls drift; issues surface late; audits become hunting expeditions.
How it works
Agent watches data/logs for control breaches, raises explainable alerts, and attaches evidence for review.
Inputs
Policy rules/controls library, systems logs, access events.
Outputs
Alert with rule hit, context, and suggested next step.
Guardrails
Severity thresholds; no enforcement actions without human approval; full trail.
Deliverables
Control Library Sheet (rules, owners, thresholds, sources)
Evidence Pack Template (export-ready PDF/ZIP)
Policy Implementation Assistant
Problem
Policies exist, however, applying them to real processes is the gap.
How it works
Assistant maps policy clauses to actual steps, creates simple SOPs, and prepares ISO evidence sections.
Inputs
Policies/controls, process maps, prior audit notes.
Outputs
Draft SOPs, clause-to-process mapping, evidence checklists.
Guardrails
Human sign-off; change log; cross-references back to company policies.
Deliverables
SOP Drafts (Markdown/PDF)
ISO Evidence Checklist (by clause/owner)
Finance & Controlling Agents
Profitability Recommendations
Problem
Teams lack a clear, shared view of what to eliminate, reduce, or raise and create
How it works
Agent analyzes product/segment/activity margins, ranks initiatives to boost or reduce, and explains the drivers
Inputs
Revenue, COGS, discounts, channel costs, support/time logs.
Outputs
Operationalized invest/cut list with implementation plans, ranked by ICE (Impact, Confidence, Ease), driver explanation, sensitivity note.
Guardrails
No budget changes; recommendations require owner approval; changes logged.
Deliverables
Driver Tree Diagram (what drives margin for this business)
Metric Dictionary (definitions + lineage)
Recommendation Report (weekly PDF/HTML with rationale)
Cash Flow Co-Pilot
Problem
Cash positions and overdue payments surprise the team, short-term decisions lack visibility.
How it works
Agent produces a rolling 13-week cash forecast, proposes follow-up tasks, and raises early-warning alerts.
Inputs
AR and AP schedules, contract terms, bank feed, pipeline-to-cash assumptions.
Outputs
13-week forecast, prioritized collections list, variance/early-warning alerts.
Guardrails
Human approval before outreach; escalation thresholds and snooze rules.
Deliverables
13-Week Cash Forecast (auto-updating sheet/report)
Liquidity Levers Sheet (terms, timing, reserve triggers).
Collections Playboard (prioritized AR with suggested outreach)
Pricing Optimization and Strategy
Problem
Prices and discounting drift drift from value and costs, no insights on willingness-to-pay.
How it works
Agent analyzes elasticity signals and competitive quotes to recommend prices and discounts by segment or SKU.
Inputs
Historical deals, volume, customer segments, competitor price snapshots, COGS.
Outputs
Price corridors, segment-specific discount guidance, and “deal guardrails” for reps.
Guardrails
Approvals required for corridor updates; audit log of each recommendation.
Deliverables
Price Corridor Pack (per SKU/segment: list/target/floor)
Discount Policy Sheet
Pricing Dashboard (margin lift, win rate vs. corridor adherence)
Targeted Agent Pilot
Starter
Starting point
Design 1 Agent Spec (allowed actions, thresholds, review points)
Pilot launch in a single workflow (sales, compliance, or finance)
Built-in guardrails (audit logs, approvals, rollback paths)
Result
A safe, functional agent running in one critical area
Multi-Agent Expansion
Scale
Everything in Starter plus
Deploy 2–3 domain agents (e.g., Sales Outreach + Compliance Assistant)
Reactivation or Monitoring dashboards for visibility
Usage telemetry + improvement feedback loops
Result
Multiple agents embedded in workflows, improving productivity and compliance.
Enterprise Agent Mesh
Pro
Everything in Scale plus
Full suite of enterprise agents across functions (Sales, Compliance, Finance)
Unified governance & audit framework
Integration with system-of-record data & APIs
Monthly optimization cycles
Result
A governed AI workforce operating across the enterprise, with explainability, control, and measurable ROI.
📦 Deliverables Always Include: Guardrails, audit trails, and rollout playbooks so adoption is safe, fast, and scalable.
AI Adoption Workshops
Because training isn’t shelfware, it’s momentum, each workshop is short, practical, and ends with tools your team can actually use the next day.
🔹 When AI Meets Privacy → Turn GDPR into simple daily rules.
Deliverables: Starter AI Privacy Policy, Data Cheatsheet, Reusable Use-Case Review Template.🔹 When AI Meets Cybersecurity → Spot threats, set practical controls, ship safely.
Deliverables: Logging Map, Detection Rules, XDR Playbook, Secure Deployment Pattern.🔹 When Everyone Meets to Deploy AI → Align sponsor, ops, IT, and end-users around one high-value pilot.
Deliverables: Pilot Brief, Workflow Sketch, Success Metrics.
Why work with us?
Modular Engagements
Start with a workshop, a pilot, or a full rollout.
Low-Risk, High-ROI
Each phase pays for itself with measurable outcomes
Built for Enterprises
Security, compliance, and governance are baked in
Human-First AI
Adoption, not hype, is what drives results
When AI Meets Privacy
Who is it forPrivacy, legal, security, product, HR leaders and professionals who want to understand the impact of privacy breach on the short and long term, and the state-of-art implementation to avoid pitfalls.Objectives
Translate GDPR into everyday build choices.Agenda (½–1 day)
What “AI & personal data” really means
Prompts, training data, outputs, logs, and where personal data can sneak inSimple rules that work
What to collect, what to avoid, how long to keep it, who can see it.Using external AI tools safely
What’s sent to vendors, opt-outs, contracts.Who owns your data
How to identify these entities and make your data private again.Hands-on mini-clinic
Review one real use case; write 6–8 clear do’s/don’ts your team will actually follow.
Take-homes
AI Privacy Starter Policy (1–2 pages, plain language)
Data Handling Cheatsheet (collect / don’t collect, keep / delete)
Use-Case Review Template (one-pager teams can reuse)
Optional add-onsPolicy review, vendor assessment clinic.
When AI Meets Cybersecurity
Who is it for
Directors and chief executives responsible for Information / Application Security and Technology
Product owners
Public sector & defense contractors with emerging AI programs.
ObjectivesSpot real threats, set practical controls, and ship safely.Agenda (½–1 day)
Threats in the real world
Prompt injection, data exfiltration via tools and connectors, over-permissive integrations.Prevention & hardening
Access boundaries, input/output filtering, secrets handling, environment isolation, least-privilege service accounts.Monitoring that matters (SIEM)
What to log from AI systems (prompts, outputs, tool calls), how to route into your SIEM, example detection rules and alerts.Counteraction & response (XDR)
Use XDR playbooks for containment (keys, tokens, accounts), rollback of bad actions, notify owners.Hands-on “break & fix”
Mini red-team on a demo app; tune a few rules; walk through a response.
Take-homes
AI Logging Map → SIEM (sources, fields, retention)
Baseline Detection Rules (prompt-abuse, exfil indicators, anomalous tool use)
XDR Response Playbook (Lite) (contain, rollback, notify, learn)
Red-Team Playbook (Lite) (Markdown)
Secure Deployment Pattern (diagram)
Optional add-onsSystem walkthrough, roadmap clinic.
When Everyone Meets to Finally Deploy AI
Who is it for
Leaders or Managers and Innovators who:
know that they need to implement AI with their team but do not know how.
face struggle to extract real value in their current AI implementation
Objectives
Pick one use case, agree the rules, ship a small pilot, and know how you’ll measure success.Agenda / Scope (½–1 day)
Pick the right first use case
Score quick value, low risk, clear owner.Map the real workflow
Where to insert an agent vs. where to reshape the flow; keep human checkpoints.Set simple guardrails
Who approves what, basic access, logging, rollback.Plan the pilot
What “done” looks like, 3–5 metrics, timeline, who’s doing what next week.Adoption beats ambition
Training slots on the calendar, champions named, feedback loop opened.Moat mindset
Identify the enterprise data that actually gives you an edge; plan how to use it safely in the pilot.
Take-homes
Pilot Brief (1-pager) — scope, metrics, owners, dates
Target Workflow Sketch — insert vs. reshape, checkpoints
Optional add-onsPolicy review, vendor assessment clinic.
Foundations: AI Readiness Assessment 🥉
Proof of Value: working pilot 🥈
Enterprise Rollout 🥇
Choose your plan
Outcome: A clear, shared starting point. No wasted efforts.
Methodology: Survey, interviews and business strategy analysis based on scientific frameworks
Deliverables: AI Readiness Assessment Report & One-page Adoption Brief. Including: RACI map; metric baseline, AI barrier analysis report and High Level Design implementation plan proposal
Outcome: A working pilot with adoption and measurable impact.
Methodology: Process and asset Analysis. Ship a thin-slice pilot in a real workflow; connect to the needed system; train the roles; fix the obvious snags quickly.
Deliverables: Working pilot; evaluation checklist; quick-start cards, Low level Design .AI Innovation SOP adapted to your company
Outcome: AI embedded into daily operations, governed and measurable
Methodology: interviews/ workshop. Create runbooks; put 2–3 KPIs on a small dashboard; schedule a monthly review; expand automation where the data supports it.
Deliverables: Full Low level Design for your AI innovation Engine, Runbook; KPI mini-board; improvement list.
Targeted Agent Pilot
Starter
Journey 🧭
Starting point
✓ Design 1 Agent Spec (allowed actions, thresholds, review points)
✓ Pilot launch in a single workflow (sales, compliance, or finance)
✓ Built-in guardrails (audit logs, approvals, rollback paths)
Destination 📍
A safe, functional agent running in one critical area
Multi-Agent Expansion
Scale
Journey જ⁀
Everything in Starter plus
✓ Deploy 2–3 domain agents (e.g., Sales Outreach + Compliance Assistant)
✓ Reactivation or Monitoring dashboards for visibility
✓ Usage telemetry + improvement feedback loops
Destination ⛳
Multiple agents embedded in workflows, improving productivity and compliance.
Enterprise Agent Mesh
Pro
Journey 🗺️
Everything in Scale plus
✓ Full suite of enterprise agents across functions (Sales, Compliance, Finance)
✓ Unified governance & audit framework
✓ Integration with system-of-record data & APIs
✓ Monthly optimization cycles
Destination ➤➤
A governed AI workforce operating across the enterprise, with explainability, control, and measurable ROI.