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Long-Range AI · A guide for CEOs

The AI Maturity Journey

Where your company is today, what it can gain, and what to protect at each stage.

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Why it matters to the CEO

The wins show up fast. The cracks show up later.

What the CEO sees first

Faster emails, quick analysis, a dashboard the team could never afford to build before. Real energy across the business.

What shows up months later

A customer, a lender or a buyer asks how the company uses AI, and nobody can answer with confidence.

“How do we use AI, and how do we protect your data?” Every CEO should be able to answer that today.

The journey in five stages

Value grows at every stage. So does the cost of a gap left open.

Stage 1

Exploring

Curious, but not using AI on purpose yet.

Stage 2 · Most are here

Individual use

People use AI on their own, often on personal accounts.

Stage 3

Sanctioned tools

Company accounts, a policy and basic training.

Stage 4

Team workflows

AI built into how teams work, using company data.

Stage 5

Enterprise AI

Governed, integrated and measured across the business.

Where most companies are

Most companies are still at stage 2: people using AI on their own.

80%
of AI users at small and midsize companies bring their own AI tools to work.
Microsoft and LinkedIn, Work Trend Index, 2024
34%
of office workers have entered customer data into public AI tools. 66% have used AI their company does not allow.
PagerDuty and Wakefield Research, 2026
1 in 3
companies under $1 billion in revenue are scaling AI across the business. The rest are still experimenting.
McKinsey, The State of AI, 2026
Stage 1

Exploring

Leadership is curious and reading about AI, but the company is not using it on purpose yet.

Opportunity

Start clean. Set up accounts, policy and data rules before habits form.

Challenge

Too many tools and too much hype. Hard to know where to start.

Risk

Criminals and insiders already use AI: fake voices, fake receipts, fake hires.

Real case: Arup lost US$25M to a deepfake CFO video call, 2024
CEO check
  • Who here already uses AI, and for what?
  • Would staff call back before paying a request that sounds like me?
  • What data should never go into an AI tool?
Stage 2 · Where most companies are

Individual use

People use ChatGPT and similar tools on their own, often on free personal accounts.

Opportunity

Quick personal wins on emails, summaries and first drafts. Good energy and curiosity.

Challenge

The gains level off quickly. Saving time on emails only goes so far.

Risk

Shadow AI. Company data leaves through personal accounts, with no record.

Real case: Samsung engineers allegedly pasted source code into ChatGPT, 2023
CEO check
  • Could we tell a customer how we use AI with their data?
  • Which free AI tools and extensions are on work laptops?
  • Do people know what never to paste in?
Stage 3

Sanctioned tools

The company pays for business accounts, writes a policy and gives people basic training.

Opportunity

The same productivity, with the data protected. The company owns the accounts and the work.

Challenge

Uneven adoption. Licenses get bought and not used, and the policy goes unread.

Risk

Connected tools become a door into email, drives and CRM.

Real case: stolen chatbot keys exposed 700+ companies, 2025
CEO check
  • Who owns our AI accounts, and what is connected to them?
  • Do our vendor terms rule out training on our data?
  • Is AI access shut off the day someone leaves?
Stage 4

Team workflows

AI becomes part of the team. Repeat processes get automated, using the company’s own data.

Opportunity

Real value starts: dashboards, automations and assistants the business runs on.

Challenge

Data has to be clean and ready, and someone with a development background has to set it up.

Risk

Builds and agents with too much access change or delete real systems.

Real case: Replit’s AI agent deleted a live database during a code freeze, 2025
CEO check
  • Has someone qualified reviewed what was built, and what it can touch?
  • Could anyone besides the builder fix it if it broke?
  • Can we undo what an agent changes?
Stage 5

Enterprise AI

AI runs inside core processes, with clear ownership, governance and measured results.

Opportunity

Value compounds. AI is measured against growth, margin and service.

Challenge

Governance, vendor management and people change all move at the same time.

Risk

The company answers for what its AI tells customers and how it treats people.

Real case: Air Canada was held liable for its chatbot’s made-up policy, 2024
CEO check
  • Do we measure what AI returns?
  • Does a person check what AI tells customers?
  • Who answers when an AI decision about a customer or candidate is wrong?
Every stage

Fraud got cheaper, from outside and from inside.

71%
Fake receipts. Share of fake receipts flagged by AppZen that were AI-generated by May 2026, up from 0% in March 2025.
Accounting Today, June 2026
4 interviews
A fake hire. Passed at KnowBe4 by a North Korean operative with a stolen identity and an AI-enhanced photo. The new laptop then started loading malware.
CyberScoop, July 2024
1 recording
A fake boss. A school athletic director allegedly used AI to fake a racist rant in his principal’s voice. The principal was harassed and later resigned.
CBS News Baltimore, April 2025
The compounding risk

Dirty data plus AI becomes slop that compounds.

1One bad record sits in your systems.
2AI writes the forecast, the email and the board summary from it.
3People reuse those outputs as inputs, and the error spreads.
63%
of organizations lack confidence in their data practices for AI.
Gartner, 2025
60%
of AI projects without AI-ready data will be abandoned, Gartner predicts.
Gartner, 2025
$186
per employee per month lost to “workslop,” AI output that looks finished but is not.
Stanford and BetterUp, 2025
The CEO test

Five questions every CEO should be able to answer

  1. Which AI tools and extensions does our team use, and on whose accounts?
  2. Where does our company data go when people use AI, and who can see it?
  3. What are employees allowed, and not allowed, to paste into these tools?
  4. Who owns what was built, and who besides the builder can maintain it?
  5. If something went wrong tomorrow, who would know, and what would we do?
How we help

Move fast with AI. Know what you are protecting.

Assess where the company sits on the journey. Build a roadmap that closes the gaps first, then moves up a stage. Build and train alongside your team, so people keep it running.

Independent advice. We do not resell tools or sell security products. Ask us for the AI readiness questionnaire to find your stage.

Where is your company on the journey? Let's talk

Sources & notes

Every case, cited.

Microsoft and LinkedIn, 2024 Work Trend Index: 31,000 knowledge workers in 31 countries; 78% of AI users bring their own tools, 80% at small and midsize companies.

PagerDuty and Wakefield Research, 2026: 1,250 office workers at companies with $500M+ revenue; 66% used AI they believed was not allowed, 34% entered customer data.

McKinsey, The State of AI, 2026: 1,719 respondents; about one third of companies under $1B scaling AI across the enterprise.

Arup: CNN, May 16, 2024. About US$25M sent in 15 transfers after a video call with a deepfaked CFO.

Samsung: Korean media reports, 2023, not confirmed by Samsung; Samsung then restricted generative AI tools.

Salesloft Drift: TechTarget, August 2025. Stolen connection tokens used to pull Salesforce data from 700+ organizations.

Replit: eWeek, July 2025. AI agent deleted a production database during a code freeze, in a test run by SaaStr founder Jason Lemkin.

Air Canada: Manatt, February 2024. Tribunal held the airline liable for its chatbot’s invented refund policy.

Fraud cases: Accounting Today, June 2026 (AppZen); CyberScoop, July 2024 (KnowBe4); CBS News Baltimore, April 2025 (Pikesville; charges, not a conviction).

Data readiness: Gartner, 2025; Stanford Social Media Lab and BetterUp, “workslop,” 2025.

Compiled October 2026 by Long-Range AI · lrangeai.com

Long-Range AI
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Customer Intelligence

Finding, reaching and nurturing your ideal customers requires the right data strategy, technical infrastructure, and measurement framework. I diagnose growth barriers and build solutions—from prospect identification to channel analytics to predictive modeling.

Specialty: Financial services (banking, credit unions, fintech)

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