Reliable, sovereign, human-centric
First, your digital double.
Next, frontier models and orchestration,
unlocking trusted AI Autonomy.
Join Our Waitlist
Sign up and get early access to our private beta.
From AI roadmap to working agents
for growing companies
We help you choose the right workflows, build governed agents into your
existing tools, and scale what works
Many teams experiment with ChatGPT or AI apps, but real work still runs on emails, spreadsheets, and legacy tools. Leaders worry about security, privacy, cost and vendor lock-in; employees hesitate, unsure what’s safe, useful, or how AI might quietly break things
We help growing companies move from scattered experiments to governed, working agents. Together we pick real workflows, design guardrails, use the cheapest capable models, and build pilots inside existing tools—then scale what works, with clear ownership, simple governance and proof you can show to security, finance and leadership
Plan, pilot, roll out
Choose a starting domain - Sales, Support, Finance, or Compliance
Workshops and trainings
Fast alignment and hands-on skills
AI Agent examples & demos
LLM Routing Agent
Picks the cheapest capable model for each task
One-page routing table with costs & snippets
EU Tender Intelligence
Finds the newest tenders that match your criteria
Ranked summaries with TED source links
Weekly Compliance Digest
Tracks regulatory changes on topics you choose
Weekly brief with highlights and citations
EIC Proposal Generator
Drafts a structured Accelerator proposal from your inputs
Executive summary + market
snapshot
Founders
Bartek Boniecki
AI agents, orchestration, AI cybersecurity
Venture builder, experienced manager, and innovation consultant with 100+ completed technology commercialization projects and go-to-market strategies
Our insights
Stay in the loop, get our insights & updates
AI strategy & implementation
Enterprise Agentic AI for
Workshops and trainings
AI Strategy that actually ships
We turn your AI intent into action: start with one high-value workflow and a governed pilot in weeks. Then a clear rollout and playbooks your team can run.
Every success accompanied by four failures
Most AI conversations start with tools. Most AI failures start there too. In our experience, models are rarely the real blocker—the method is. The hard parts are choosing the right first workflow, involving people early, training them well, and putting simple guardrails in place.
We see AI less like a one-time surgery and more like physiotherapy: outcomes come from steady practice, clear milestones, and working within real-world constraints—legacy systems, regulation, risk concerns, procurement, and hiring gaps. Our work focuses on embedding AI into everyday processes in a safe, measured way so many small improvements add up to durable value.
Your Challenge
AI sounds great in theory, but enterprise reality - old systems, regulation, skills gap, resistant culture - makes it messy.
AI talk starts with tools instead of real workflows.
Legacy systems, risk, and approvals stall promising ideas.
Teams aren’t sure how AI changes their day-to-day work.
Our Promise
A modular, step-by-step program that reduces risk, builds buy-in, and delivers measurable results.
Start with one clear, measurable pilot—not a 50-page deck.
Design with your people: training, ownership, and guardrails included.
Build a path from first pilot to repeatable, safe rollouts.
Choose your plan
Adopt where it's needed
Customer support triage & drafting
Cluster tickets, suggest replies grounded in your help center/policies, and route to the right queue - agents approve and send.
Target: −30% handling time
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
Target: 2× screening speed
Sales - lead qualification and activation
Score inbound leads, draft short, context-aware outreach, plan the next best touches: follow-ups stalled deals reactivation
Target: +10% restarts
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.
Target: −40% manual edits
Foundations ★
Align on where AI fits and what happens first.
Best when:
You explore AI, hear mixed opinions, and don’t want a 6-month project.
You get:
Shared view of AI priorities
Shortlist of strong first use cases
Clear owners and next steps
How we work (2 weeks):
Interviews with key people
1 working session to decide focus
1–2 concise docs: readiness snapshot + adoption brief
Proof of Value ★★
Turn one workflow into a measurable pilot.
Best when:
You know a promising use case and want proof it works in your environment.
You get:
One live agent / workflow in a real process
2–3 KPIs tracked (e.g., time saved, accuracy)
Guardrails tested with your team
How we work (2-4 weeks):
Co-select use case & KPI
Configure and connect safely
Run pilot, review results, decide go / no-go
Enterprise Rollout ★★★
Scale what works, without losing control.
Best when:
You’ve proven value in one area and want 3+ workflows running safely.
You get:
Prioritised rollout plan
Shared governance & access model
Review cadence and improvement backlog
How we work (8-12 weeks):
Standardise guardrails and patterns
Support new workflow launches
Transfer ownership to your teams
Stay in the loop, get our insights & updates
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
Governed AI agents
For your sales, support, finance, and compliance.
Inside your existing workflows. With clear approvals, safe data use, and simple logs.
Your Challenge
AI sounds great, but old systems, rules, and skills gap make it hard to start.
We’re worried about unpredictable AI costs and paying for tools we barely use
Security and IT won’t say “yes” without control and visibility
Off-the-shelf AI feels generic, and people aren’t sure how it will change their work
Our Promise
Start small: one real workflow, live in your existing tools, measured in weeks
Cost control by design: cheapest capable models, smart reuse, and clear usage caps
Built-in trust: minimal access, human approval on sensitive actions, simple activity logs so your security lead can say “yes” quickly
Co-designed with your team: training, ownership, and guardrails so people actually adopt it
Sales & Marketing Agents
Lead Qualification & Outreach Agent
Your team loses time chasing low-fit leads
This agent scores incoming leads using simple rules you define, drafts short message your reps can send and proposes the next task.
Deal prioritization let you focus on the right ones
Pipeline Reactivation Agent
Deals quietly die in your CRM
This agent flags stalled opportunities & proposes relevant follow-ups with coaching tips (“mention X case study”).
You get reactivated deals, no changes to IT
Sales Call / Meeting Co-Pilot
Reps dig through tabs before every call.
Before each call, this agent compiles a short brief from CRM data, past emails, and key facts.
Outcome: better-prepared conversations in minutes with coaching tips (“mention X case study”).
Regulatory & Compliance Assistants
Regulatory Filing
Drafting filings is slow and error-prone.
Filing Assistant drafts sections based on your existing policies, templates, and regulatory guidance with clear references for legal/compliance review.
You get Filing Template Pack + reviewer checklist
Continuous Monitoring & Risk Alerts
Regulatory changes are easy to miss.
The agent scans agreed, trusted sources and produces a concise digest of relevant changes with links and suggested priority
Stay ahead and know what to look at first.
Policy Implementation
Policies exist, adoption doesn't.
The agent maps processes, requirements & policy docs, turning them into action plan for your team.
Outcome: practical rollout plans you can use + drafted SOPs and checklists
Finance & Controlling Co-Pilots
Profitability Insights
Seeing true margin by product or segment takes hours in spreadsheets
This agent analyzes your P&L and sales data to spot products, customers, or channels that underperform.
Simple insights where you earn, where you leak, what to cut, fix, or double down
Cash Flow Management
Cash surprises appear too late.
Using financial data, this Co-Pilot builds a rolling short-term cash view with clear timeline, prioritized collections list and upcoming gaps and their drivers
Result: 13-week cash forecast with liquidity risks and levers
Pricing Optimization and Strategy
You change prices but still miss your goals.
The agent combines sales data and competitor signals to recommend where to raise, hold, or flex prices, providing alerts on risky deals.
Get data-based pricing moves for revenue growth, profitability, or new markets
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.
Stay in the loop, get our insights & updates
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
Each workshop is short, practical, and ends with tools your team can actually use the next day.
Scope
When AI meets privacy
Goal
Turn GDPR into simple daily rules.
Deliverables
Starter AI Privacy Policy
Data Cheatsheet
Reusable Use-Case Review Template
When AI meets cybersecurity
Goal
Spot threats, set practical controls, ship safely.
Deliverables
Logging Map
Extended Detection & Response (XDR) Playbook
Secure Deployment Pattern
When everyone meets to deploy AI
Goal
Align sponsor, ops, IT, and end-users around a pilot
Deliverables
Pilot Brief
Workflow Sketch
Success Metrics
Authors
Bartek Boniecki
AI agents, orchestration, AI cybersecurity
Venture builder, experienced manager, and innovation consultant with 100+ completed technology commercialization projects and go-to-market strategies
Based on our experiences from:
Detailed workshop programs
When AI meets privacy
For leaders and professionals responsible for privacy & security, legal, product, HR 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
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-ons
Policy review, vendor assessment clinic.
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.
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.
Objectives
Spot real threats, set practical controls, and ship safely
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-ons
System walkthrough, roadmap clinic
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.
When everyone meets to deploy AI
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
Take-homes
Pilot Brief (1-pager) — scope, metrics, owners, dates
Target Workflow Sketch — insert vs. reshape, checkpoints
Optional add-ons
Policy review, vendor assessment clinic
Agenda (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.
Stay in the loop, get our insights & updates
For leaders who want AI to pay off
The SME Playbook for Trusted AI Adoption
A practical guide for 20–200-person companies to deploy AI successfully and securely, without breaking workflows or compliance.
PDF, 38 pages • 15-minute read • No fluff
Is it you?
CEO / Founders of SMEs with 20-200 employees
Using basic AI tools (chatbots/early agents), worried about security
Seeking a safe, practical rollout now, not a 12-month program
It's NOT for you if you are not ready to invest your time and you results in 24 hours. Good results closely bound to smooth cooperation: we need your knowledge to succeed.
MIT report: 95% of genAI pilots at companies fail. Why?
The most important barrier? - Employees don't want to adopt enterprise AI tools. At the same time, the overwhelming majority uses AI chatbots like the most popular ChatGPT to better and faster perform their tasks. Why are they so reluctant to enterprise-level AI tools?
Unwilling stance may result from uncertainty. Persistent narratives about replacing human workers with AI agents reinforce negative sentiment towards AI tools. Persistent change of such an employee stance is a sine qua non to successful post-pilot adoption and requires three key steps: speak the language of human-enabling AI, not human-replacing, make this voice well-heard, and plan smooth change management. This is what our playbook is centered around.
The other part of the equation is trust in AI tools on your agenda. Forget about enterprise software offering rigid output with limited customization options; it will always be beaten by personal use of AI chatbots that let users tailor the output to their needs (i.e., guide conversation) and iterate until they are satisfied.
Our remedy: focus on people, start small, use the right tools
Identify the most promising use cases
Map your goals, key processes, and how work actually happens today. Our playbook will help you to spot internal AI champions, stakeholders, and decision makers (so you know who to involve from day one), as well as to identify use cases where AI can clearly save time or improve quality without disrupting what already works.
Align with your operations and priorities
This part of the playbook focuses on short-listing the use cases that match your current priorities and budget. If possible, prefer to reinforce, not revolutionize. Focus on a small number of initiatives that strengthen what you already do - either front-office or back-office operations. where the balance of impact and feasibility is obvious. In practice: pick a small set of projects with clear owners and a believable, measurable return (time saved, faster delivery, fewer errors).
Set the ground so projects don’t stall
We make sure the basics are in place so pilots don’t die quietly after a few weeks. You secure explicit executive support and a realistic budget, then assemble a small cross-functional team with clear roles and responsibilities.
In parallel, the playbook guides you through simple, SME-ready security basics: vetted-tools-only policy, least-privilege access and MFA, plus clear “do / don’t” rules so AI is secure by design and doesn’t overburden your IT or compliance.
Start small, prove it, and learn fast
Here are just a few tips: double down on what works, use visible, small wins to demonstrate progress and encourage calculated risk-taking across the company. Make human verification for critical actions must-have – AI never acts alone on high-risk decisions.
Here are just a few tips: double down on what works, use visible, small wins to demonstrate progress and encourage calculated risk-taking across the company. Make human verification for critical actions must-have – AI never acts alone on high-risk decisions.
Cross the genAI chasm: turn one pilot into everyday use
This is where the majority fails. No AI initiative succeeds without real employee buy-in.
To keep adoption going after the first excitement fades, you will need to address the main concerns, either emotional or technical-oriented, provide ready-to-use upskilling paths and short training formats so employees feel equipped, not replaced, and understand where AI helps them do their best work.
For full sustainability, you’ll learn to build two connective tissues. On the organizational side, our IMAGINE framework gives you a simple structure for ongoing communication, clear guidelines, champions in each team, and incentives that reward using AI where it truly helps. On the technical side, we outline methods to address concerns like “I don’t want to use an AI tool that forgets context, doesn’t learn, and can’t evolve.” or “I don’t feel tools that our company uses are secure.
We believe that, together, these steps help SMEs move from “we tried a pilot once” to “AI quietly helps our people every day.”
Check what the top 5% AI adopters do differently to succeed
Authors
Bartek Boniecki
AI agents, orchestration, AI cybersecurity
Venture builder, experienced manager, and innovation consultant with 100+ completed technology commercialization projects and go-to-market strategies
Based on our experiences from:
Pure value. No fluff.
A practical guide for 20–200-person companies
Deploy AI securely without breaking workflows or compliance
People & Process: Scripts, training paths, role-based guardrails
Security & Compliance: Data classification flow, access policies, audit checklists
Governance & ROI: Lightweight governance model + KPI starter pack
Download the playbook.
Find the first use cases and secure tech stack
By submitting, I agree to the processing of my personal data by Otono.me as described in the Privacy Policy.
Stay in the loop, get our insights & updates
For leaders who expect their business to survive any AI or IT catastrophe
The SME Playbook for Resilient AI Operations
A practical survival guide for 20–200 employee companies to protect revenue, workflows, and operations from AI outages, failures, and silent risks.
PDF • 38 pages • 15-minute read • Practical templates included
Is it you?
CEO / Founder
20–200 person company
Already using AI daily
to automate operations and decision-making
Seeking robust technology
that won’t go down even if the entire Internet does
This playbook is not for companies who expect "set-and-forget AI" solutions. Real resilience requires leadership attention and action. Results depend on rapid cooperation and implementation.
Are you prepared for AI going down - again?
MIT report: Over 90% of employees use personal AI tools at work, often without IT approval.
Most SMEs don’t know:
which AI tools their employees use
for what tasks
how deeply core operations depend on these tools
When AI systems fail, it’s not just an IT issue — it’s a full operational breakdown, especially for sales, support, and frontline teams.
Large enterprises spend millions on redundant AI infrastructure. SMEs rarely have that luxury.
So what happens to your company when your AI stack goes dark for hours... or days?
Our remedy: spot every AI use, ensure offline fallbacks, craft a plan
Inside the Playbook
Multi-model strategy
CDN risk mitigation
Compute redundancy
Credential failover
Governance & compliance
Emergency response templates
Discover what the top AI-powered SMEs do differently
Authors
Bartek Boniecki
AI agents, orchestration, AI cybersecurity
Venture builder, experienced manager, and innovation consultant with 100+ completed technology commercialization projects and go-to-market strategies
Based on our experiences from:
Pure value. No fluff.
Secure AI deployment: no breaking workflows or compliance
People & Process: Scripts, training paths, role-based guardrails
Security & Compliance: Data classification flow, access policies, audit checklists
Governance & ROI: Lightweight governance model + KPI starter pack
Download the playbook.
Safeguard your AI operations today.
By submitting, I agree to the processing of my personal data by Otono.me as described in the Privacy Policy.
Stay in the loop, get our insights & updates
The SME Playbook for Resilient AI
Operations
Inside the Playbook
Mapping every AI dependency
Introducing fallback alternatives
Building local AI continuity
Securing computing independence
Key components of an AI-down action plan
Besides: * Multi-model strategy * CDN risk mitigation * Compute redundancy * Credential failover * Governance & compliance * Emergency response templates
Connect to check how we improve AI resilience
Stalled Adoption: Pilots don’t scale due to skills & change resistance (e.g., ~70% stall — example stat)
Security Exposure: Chatbots/early agents can leak PII/IP without guardrails (e.g., 1 in 5 prompts contain sensitive data — example stat)
Governance Gaps: No clear rules → shadow AI & audit risk (e.g., 40%+ lack an AUP — example stat)
People First: Role-based use cases + 2-week upskill path + change scripts
Secure by Default: Data boundaries, AUP template, DLP/PII filters, access controls
Lightweight Governance: RACI, DPIA checklist, logging & review cadence (monthly)
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
Pure value. No fluff.
A practical guide for 20–200-person companies
Deploy AI securely without breaking workflows or compliance
People & Process: Scripts, training paths, role-based guardrails
Security & Compliance: Data classification flow, access policies, audit checklists
Governance & ROI: Lightweight governance model + KPI starter pack
Download the playbook to find the first use case and the right security stack
By submitting, I agree to the processing and international transfer of my personal data by Okta as described in the Privacy Policy.
Pure value. No fluff.
A practical guide for 20–200-person companies to deploy AI securely.
Without breaking workflows or compliance
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 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.
Profitability Insights Agent
Problem: it’s hard to see which products/customers really pay off
How it works: highlights segments with weak or strong margins, proposes what to boost or reduce, explains the drivers
Inputs: basic P&L exports, discounts, channel costs, product/customer data
Outputs: simple views: where you earn, where you leak
Deliverables: Driver Tree Diagram (what drives margin for this business
Safeguards: read-only; no budgets or prices are changed
Cash Flow Co-Pilot
Problem: cash surprises appear too late, short-term decisions lack visibility.
How it works: creates a rolling short-term cash view, proposes follow-up tasks, flags risks
Inputs: accounts receivable & payable data, recurring costs, contract terms
Outputs: clear timeline, prioritized collections list, notes on what drives gaps
Deliverables: 13-Week Cash Forecast (auto-updating), Liquidity Levers Sheet
Safeguards: read-only; built on exported or permissioned data only
Pricing Optimization and Strategy
Problem: discounts and special deals quietly erode margin.
How it works: analyzes elasticity and competitive quotes to recommend prices and discounts
Inputs: deal history, standard price lists, segments, competitor price snapshots
Outputs: alerts on risky deals, segment-specific discount guidance, and “deal guardrails” for reps
Deliverables: Pricing Dashboard, Price Corridor and Discount Policy Sheet
Safeguards: No auto-changes; sales leadership approves all rules.
Regulatory Filing Assistant
Problem: drafting and updating filings is slow and manual
How it works: prepares draft sections from your existing policies and templates
Inputs: internal policies, previous filings, public rules and regulator guidance
Outputs: draft text with clear references for legal/compliance review
Deliverables: draft text with clear references for legal/compliance review
Safeguards: no auto-submission; all drafts marked as “for review”; flags uncertain claims
Continuous Monitoring & Risk Alerts
Problem: teams miss relevant changes in laws or guidance, audits become hunting expeditions
How it works: data/logs for control breaches, raising explainable alerts, review suggestions
Inputs: official sites, newsletters, trusted sources you approve.
Outputs: Short summary of changes with links and suggested priority
Deliverables: Control Library Sheet (rules, owners, evidence, sources)
Safeguards: Sources are transparent; you control the list and frequency.
Policy Implementation Assistant
Problem: policies exist, however, applying them to real processes is the gap
How it works: maps requirements into simple checklists and tasks
Inputs: your policy docs, process maps, prior audit notes
Outputs: Draft SOPs, clause-to-process mapping, evidence checklists.
Deliverables: Action lists by team, Standard Operation Procedures drafts.
Safeguards: No changes to systems; outputs are proposals for managers.
Lead Qualification & Outreach Agent
Problem: sales spends time on low-quality leads.
How it works: scores leads, drafts messages based on simple rules, schedules tasks.
Inputs: CRM data, recent activity (opens/clicks), ICP rules, brand voice guide
Outputs: prioritized lead list + short email drafts, next task with due date
Deliverables: Agent Specification with allowed actions, Rollout Playbook
Safeguards: no email is sent without human approval; logs every suggestion
Pipeline Reactivation Agent
Problem: stalled opportunities remain untouched by sales / bizdev team
How it works: flags stalled opportunities and suggests specific follow-ups
Inputs: CRM activity history available, last contact date, notes, persona tags
Outputs: re-engagement task, suggested message, coaching tip (“mention X case study”)
Deliverables: Stall Detection Rules and Reactivation Dashboard with revived count
Safeguards: suggestions only; your team chooses what to send or change
Sales Call / Meeting Co-Pilot
Problem: reps are underprepared; info is scattered
How it works: pulls key context into a one-page brief before calls
Inputs: CRM notes, past emails, meeting history
Outputs: short call brief with key facts, risks, and next-step suggestion
Deliverables Asset Map - which content to pull by stage/persona
Safeguards: no edits to CRM possible without approval; source links for every claim.
Our proven 5-step AI Resilience Framework
Map every AI dependency
Create a clear inventory of all AI tools and identify single points of failure.
Introduce fallback alternatives
Ensure each critical AI function has a backup tool or model ready to deploy.
Build local AI continuity
Deploy local LLMs or self-hosted AI assistants as emergency operational support.
Secure computing independence
Leverage private cloud or on-prem infrastructure to protect essential automations.
Create an ‘AI Down – Action Plan’
One-page emergency procedure for every critical workflow
What the playbook also covers
Multi-model and multi-provider strategies
Cloudflare & CDN risk mitigation strategies
Compute-layer redundancy design
Credential & security vault failover planning
Governance, logging, and compliance alignment
Least-privilege access
We only request access to the minimum tools and data needed
Privacy-first
We don’t store your data unless a project explicitly requires it.
Transparent activity
Simple logs of agent activity are available on request
Human-in-the-loop approvals
Sensitive actions are always confirmed by your team
Lead Qualification & Outreach Agent
Your team loses time chasing low-fit leads
This agent scores incoming leads using simple rules you define and drafts short message your reps can send.
Result: Prioritized lead list, more time to invest in the right deals.
Problem: sales spends time on low-quality leads.
How it works: scores leads, drafts messages based on simple rules, schedules tasks.
Inputs: CRM data, recent activity (opens/clicks), ICP rules, brand voice guide
Outputs: prioritized lead list + short email drafts, next task with due date
Deliverables: Agent Specification with allowed actions, Rollout Playbook
Safeguards: no email is sent without human approval; logs every suggestion
Pipeline Reactivation Agent
Dormant deals quietly die in your CRM.
This agent scans your pipeline, flags winnable stalled opportunities, and proposes relevant follow-ups.
Result: Systematic reactivation of deals with zero changes to your systems.
Outputs: re-engagement task, suggested message, coaching tip (“mention X case study”)
Problem: stalled opportunities remain untouched by sales / bizdev team
How it works: flags stalled opportunities and suggests specific follow-ups
Inputs: CRM activity history available, last contact date, notes, persona tags
Outputs: re-engagement task, suggested message, coaching tip (“mention X case study”)
Deliverables: Stall Detection Rules and Reactivation Dashboard with revived count
Safeguards: suggestions only; your team chooses what to send or change
Sales Call / Meeting Co-Pilot
Reps dig through tabs before every call.
Before each call, this agent compiles a one-page brief from CRM notes, past emails, and key data.
Result: Better-prepared conversations in minutes, coaching tip (“mention X case study”).
Problem: reps are underprepared; info is scattered
How it works: pulls key context into a one-page brief before calls
Inputs: CRM notes, past emails, meeting history
Outputs: short call brief with key facts, risks, and next-step suggestion
Deliverables Asset Map - which content to pull by stage/persona
Safeguards: no edits to CRM possible without approval; source links for every claim.
Regulatory Filing Assistant
Drafting filings is slow and error-prone.
Filing Assistant drafts sections based on your existing policies, templates, and regulatory guidance.
Result: Filing Template Pack + Reviewer Checklist
Problem: drafting and updating filings is slow and manual
How it works: prepares draft sections from your existing policies and templates
Inputs: internal policies, previous filings, public rules and regulator guidance
Outputs: draft text with clear references for legal/compliance review
Deliverables: draft text with clear references for legal/compliance review
Safeguards: no auto-submission; all drafts marked as “for review”; flags uncertain claims
Continuous Monitoring & Risk Alerts
Regulatory changes are easy to miss.
The agent scans agreed, trusted sources and produces a concise digest of relevant changes for your topics.
Result: You stay ahead of updates and know what to look at first.
Problem: teams miss relevant changes in laws or guidance, audits become hunting expeditions
How it works: data/logs for control breaches, raising explainable alerts, review suggestions
Inputs: official sites, newsletters, trusted sources you approve.
Outputs: Short summary of changes with links and suggested priority
Deliverables: Control Library Sheet (rules, owners, evidence, sources)
Safeguards: Sources are transparent; you control the list and frequency.
Policy Implementation Assistant
Policies exist, adoption doesn't.
The agent maps processes, requirements & policy docs, turning into action plan for your team.
Result: practical rollout plans your managers can adjust and approve; drafted SOPs.
Problem: policies exist, however, applying them to real processes is the gap
How it works: maps requirements into simple checklists and tasks
Inputs: your policy docs, process maps, prior audit notes
Outputs: Draft SOPs, clause-to-process mapping, evidence checklists.
Deliverables: Action lists by team, Standard Operation Procedures drafts.
Safeguards: No changes to systems; outputs are proposals for managers.
Profitability Insights Agent
Seeing true margin by product or segment takes hours in spreadsheets
This agent analyzes your basic P&L and sales data to highlight products, customers, or channels that underperform.
Result: You get simple insight pages pointing to where to cut, fix, or double down
Problem: it’s hard to see which products/customers really pay off
How it works: highlights segments with weak or strong margins, proposes what to boost or reduce, explains the drivers
Inputs: basic P&L exports, discounts, channel costs, product/customer data
Outputs: simple views: where you earn, where you leak
Deliverables: Driver Tree Diagram (what drives margin for this business
Safeguards: read-only; no budgets or prices are changed
Cash Flow Co-Pilot
Cash surprises appear too late.
Using financial data, this Co-Pilot builds a rolling short-term cash view and upcoming gaps.
Result: 13-week cash forecast with liquidity risks and levers
Problem: cash surprises appear too late, short-term decisions lack visibility.
How it works: creates a rolling short-term cash view, proposes follow-up tasks, flags risks
Inputs: accounts receivable & payable data, recurring costs, contract terms
Outputs: clear timeline, prioritized collections list, notes on what drives gaps
Deliverables: 13-Week Cash Forecast (auto-updating), Liquidity Levers Sheet
Safeguards: read-only; built on exported or permissioned data only
Pricing Optimization and Strategy
Align pricing to your goals and outsmart rivals.
The agent combines sales data and competitor signals to recommend where to raise, hold, or flex prices based on your goals
Result: data-based pricing moves for revenue growth, profitability, or new markets
Problem: discounts and special deals quietly erode margin.
How it works: analyzes elasticity and competitive quotes to recommend prices and discounts
Inputs: deal history, standard price lists, segments, competitor price snapshots
Outputs: alerts on risky deals, segment-specific discount guidance, and “deal guardrails” for reps
Deliverables: Pricing Dashboard, Price Corridor and Discount Policy Sheet
Safeguards: No auto-changes; sales leadership approves all rules.