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AI Implementation Strategy · For companies and leadership teams

From AI ambition to measurable results.

Most vendors start with their product and look for a place to fit it. We start with your business: your needs, your numbers, your people. Then we find the use cases that make sense and rank them in order of value.

Find where AI pays

Where AI changes your economics

Four places the return usually comes from.

01

Expert productivity

Less time searching, drafting and re-typing, so qualified people spend their hours on work clients pay for.

02

Customer operations

Faster, consistent answers to routine requests, with complex cases passed to staff with full context.

03

Cash and process speed

Shorter cycles for quotes, documents, billing and approvals, releasing cash tied up in slow processes.

04

Knowledge and decisions

Reliable access to the company's own knowledge, with sources people can check and people still deciding.

Where to start

AI Value Assessment Sprint

Two to four weeks · fixed fee, agreed upfront

Before any money goes into tools or vendors, we find where AI pays in your business, size it in your own numbers and decide with you what to prove first.

What you receive

  • An opportunity register, each item sized in hours, days, euros and customers affected
  • An assessment of your data, systems and governance readiness
  • A roadmap ranked by value, feasibility and risk, with owners and KPIs
  • A recommended first pilot with success criteria agreed in advance
Find where AI pays

When to call us

  • Your people already use AI tools, with no shared rules
  • Vendors are pitching and you cannot compare their proposals
  • A pilot worked but never reached daily use
  • The board is asking for an AI plan with numbers behind it
  • Costs or service levels are under pressure
  • AI spend is growing but nobody can show the return
  • A client, auditor or regulator is asking how you govern AI

From roadmap to production

A disciplined path, not another strategy deck.

We follow real work from first contact to final invoice, speak with the people who do it, and size every opportunity in your own figures. Then we prove it on real work and scale only what earns further investment. Vendor proposals are tested against the same priorities, so a proposal that solves the wrong problem is caught before it is signed.

  1. 01

    Understand the business

    Map how work actually flows, where it stalls and which outcomes matter.

  2. 02

    Agree the approach

    Leadership sets ambition, priorities, boundaries, governance and decision rights.

  3. 03

    Build the roadmap

    Size and rank use cases, assign owners and KPIs, and sequence the work.

  4. 04

    Prove it in practice

    Pilot on real work against agreed test sets and success thresholds, measured in money, time and service quality.

  5. 05

    Scale what works

    Move proven solutions into daily operations with monitoring, adoption support and governance. AI gains autonomy step by step, from suggesting to drafting to acting with approval, only where measured accuracy and controls justify it.

From strategy to delivery

One point of accountability.

We lead the strategy, the roadmap and the proof of value. If you want the solution built end to end, we bring in vetted engineering partners, selected for the job. Either way, DisruptIQ leads the engagement and remains accountable for the agreed scope and milestones.

  • Vendor independent. Our advice follows your needs, not a supplier's product list.
  • Fixed scope and fee. Every phase is agreed in writing before it starts. For implementation, part of the fee can be linked to the results we measure together.
  • Confidential by default. Your data and your plans stay yours.

Who does what

DisruptIQ

Strategy, business case and roadmap; the proof of value; choosing and overseeing engineering partners; reporting to leadership; accountable for agreed scope and milestones.

Your team

A business owner for each use case; access to data, systems and approvals; the people who test the solution and use it every day.

Engineering partners

Build, integration, testing against agreed thresholds, and support after go live, working to our specification and under our oversight.

Technical foundations

What makes AI hold up in daily use.

Grounded in your knowledge
Answers drawn from your own documents and systems, with sources people can check.
Agents with guardrails
Scoped permissions, full audit logs and a clear hand-off to a person.
Evals before go-live
Tested against real cases and agreed accuracy thresholds first.
Observability in production
Quality, cost and latency monitored continuously.
Model agnostic
The right model per use case on cost, speed, data sensitivity and regulation.

Current engagements

Current engagements across four sectors.

Client names are confidential. Measured results will be published once the client agrees.

Mid-size law firmPhase one delivered

From scattered AI use to one governed practice

Lawyers were already using consumer AI tools individually, with no shared rules on confidentiality. Time was recorded late and partially, billing ran weeks behind the work, and the firm's best knowledge sat across thousands of folders from long, multi-year matters.

What the work covers
  • Executive interviews with the partners: where expert time goes, where revenue leaks, and which work must never leave the firm
  • Use cases sized in the firm's own numbers: time capture, billing preparation, precedent search, first drafts and document review
  • Confidentiality and governance guardrails agreed before any rollout
  • A phased adoption roadmap on a secure enterprise AI platform, starting with the practices most ready for it

What we measure

  • Share of billable time captured
  • Days from work done to invoice sent
  • Time to find the right precedent
  • Weekly active use by lawyers

DisruptIQ engagement

Mid-size life sciences groupIn progress

Finding where AI moves the P&L, not just the clock

An international group with highly qualified people spending a large share of their week on coordination, document handling and status chasing. Leadership wanted to know where AI would change the economics of the business, and which ideas only look good in a demo.

What the work covers
  • A six week assessment across operations, commercial and finance, following real work from request to delivery
  • Opportunities ranked by value, feasibility and risk, with a clear view of which can be built now
  • Focus on three levers: expert hours returned, faster response to clients, and cash released from slow processes
  • A controlled path into execution, with success thresholds agreed before any pilot

What we measure

  • Expert hours returned per month
  • Response time to client requests
  • Days of working capital tied up in slow processes

DisruptIQ engagement

European travel and logistics operatorIn progress

Customer service that scales with demand

Contact volumes surge with demand and with every disruption. Booking changes, service updates and routine questions arrive at the same moment, and every unanswered message is a cost or a lost booking.

What the work covers
  • Customer service, commercial and operational opportunities translated into value cases in the operator's own figures
  • An AI assistant grounded in schedules, fares and policies, handing complex cases to staff with full context
  • Faster, consistent communication during disruptions
  • Governance and data protection requirements, and a sequenced roadmap from pilot to the whole network

What we measure

  • Share of requests resolved without waiting
  • Response time during demand surges and disruption
  • Cost per customer contact
  • Customer satisfaction

DisruptIQ engagement

Mid-size bankIn progress

Faster credit decisions, with people still deciding

SME loan files take weeks: documents arrive incomplete, analysts retype data, and credit memos are written from scratch. Compliance teams face growing alert volumes. Any AI has to fit banking supervision, operational resilience rules and the EU AI Act.

What the work covers
  • Document intake that reads, checks and extracts data from loan applications, flagging what is missing on day one
  • First drafts of credit memos for analysts to review and complete; the credit decision always stays with people
  • Triage support for compliance alerts, with clear explanations and full audit trails
  • A policy and procedures assistant for branch and operations staff, answering from the bank's own rulebook with sources
  • Model, data and vendor controls designed with risk and compliance from the start

What we measure

  • Days from application to credit decision
  • Analyst hours per loan file
  • Alert handling time
  • Audit findings

DisruptIQ engagement

Before DisruptIQ

Two decades of enterprise AI at Voiceweb.

Voiceweb, which Nikos Patsis founded in 2001, built and deployed conversational AI for some 50 enterprise clients in 30 countries, through procurement, security and privacy reviews of the most demanding kind.

  • VodafoneConversational AI for Vodafone Turkey, Voiceweb's largest account at €1.5M in annual recurring revenue.
  • MTN GroupEnterprise conversational AI platform on Microsoft Azure for MTN Group, Africa's largest mobile operator.
  • Raiffeisen BankConversational AI for customer service in a regulated banking environment.
  • National Bank of GreeceConversational AI for one of Greece's systemic banks.

Decision tools

Run the numbers first.

See what AI could be worth in your business, in your own numbers. Built on the same logic we use with clients, calculated in your browser, with the method shown.

AI Value CheckWhat AI could be worth across your business: expert hours, customer service and cash.3 minutesOpen the tool →

Find out where AI pays in your business.

Start with a conversation, then a fixed fee assessment with a clear deliverable and date. We reply to every inquiry within two working days.

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