AI Transformation Consultancy. Twenty years of enterprise delivery.

AI transformation that shows up in the P&L

For 20 years we have helped enterprises run digital transformation, modernize software, and get real value out of data. We bring that operating history to the hardest question on the board agenda right now: how to turn AI from pilots into measurable outcomes.

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20

Years of Enterprise Delivery

30

Days from Kickoff to Board-Ready AI Roadmap

5

Engagement Models, Sequenced on Purpose

Enterprise Clients Across Two Decades of Work

Wolters Kluwer
NBC Universal
Standard Chartered
Lloyds TSB
First Tennessee
Burger King & Tim Hortons
Greenwich Associates
First Southern Bank
Bennett International Group
Baker Companies
Promotora Ambiental
Fischel
Taché
Monsieur Touton Selection
Dix Communications

How We Work With You

Five ways to engage, sequenced the way enterprise AI transformation actually has to happen.

AI Value Diagnostic (30 days)

A bounded 30-day engagement that audits your existing AI stack, surfaces shadow AI activity, quantifies realistic P&L impact by workstream, and delivers a board-ready roadmap against your actual cost structure. It is the entry point to every other engagement we do.

  • AI feature inventory across your existing stack, including shadow AI already in use
  • Phase-appropriate measurement architecture and governance scoping
  • Prioritized portfolio with business cases sequenced by effort, impact, and time to value
  • CFO-ready financial case against your actual cost structure

AI Tool Selection, Governance, Training, and Rollout

The boring but necessary foundation. We select the right tools, design the governance, build the training, and run the rollout. The goal is to upskill the workforce and remove the fear layer before anyone is asked to trust AI with a decision that matters.

  • Tool selection across productivity suites (Microsoft 365 Copilot, Google Workspace AI, Claude for Enterprise, ChatGPT Enterprise) and embedded copilots in your line-of-business systems
  • Acceptable use policy, risk lane classification, and governance intake
  • Training curriculum tailored to functional roles, designed to reduce fear and accelerate adoption
  • Rollout plan with adoption metrics and a standing support model

Data Strategy for AI

Without a real data strategy, AI will fail. Every enterprise AI program we have seen fall over in the last 18 months fell over at the data layer. We design the data strategy that makes the rest of the AI portfolio possible, grounded in 20 years of enterprise data analytics work.

  • Data audit and lineage map for the domains in scope
  • Quality standards, monitoring, and a semantic model the AI stack can actually use
  • Governed access and retention policy for AI workloads
  • A data operating model that keeps the foundation healthy as the AI program scales

Agentic AI and Automation Operating Model

This one goes last, on purpose. Once the workforce is upskilled and the data layer is solid, we design the operating model for automation and agentic AI. Explicit action permissions, accountability boundaries, policy frameworks, audit surfaces, and rollback discipline.

  • Action governance model in plain language, with policy frameworks and approval paths
  • Memory and retention rules treated as system-of-record policy
  • Auditability specification that internal audit and legal can sign off on
  • Staged rollout from rule-based automation through assisted workflows to supervised autonomous agents

Fractional Head of AI

Embedded AI leadership for organizations moving too fast to wait on a full-time hire and too seriously to hand the portfolio to a committee. Our founder has held this role at a Fortune 500 parent company, leading the AI portfolio across multiple brands and thousands of employees.

  • Standing executive presence on AI strategy and governance
  • Portfolio oversight for your active AI workstreams
  • Board and leadership communication
  • Vendor and platform decision ownership with a clear escalation path

The 7 Layer AI Transformation Framework

The model we use to scope, sequence, and govern enterprise AI transformation. Skip a layer and the ones above it collapse. Every engagement maps to it.

  1. 00

    AI Feature Discovery in the Existing Stack

    Audit what is already in your environment before you buy anything new. Copilot features, workflow automation entitlements, cloud AI services, license seats you are already paying for and not using. Almost always the fastest path to measurable value.

  2. 01

    Data Readiness

    The non-negotiable foundation. Data audit and lineage, quality standards, semantic models. Most failed AI programs we have seen were failures of Layer 1 that got diagnosed as model or tooling problems.

  3. 02

    AI Analytics and Business Intelligence

    Descriptive and prescriptive analytics on the data layer. Conversational BI, dashboards that answer questions in plain language, margin and inventory and performance visibility. The first layer where leadership feels a productivity lift.

  4. 03

    Generative AI in the Workforce

    Productivity tools (Microsoft 365 Copilot, Google Workspace AI, Claude for Enterprise, ChatGPT Enterprise) and generative AI embedded in line-of-business systems (ERP, CRM, service desk, vertical copilots). Training the workforce here is what removes the fear layer.

  5. 04

    Automation and AI Agents

    A maturity continuum. Rule-based automation, then AI-assisted workflows, then autonomous agents that can plan and act across systems. Every step up requires more governance. Every step up compounds the return.

  6. 05

    Machine Learning and Predictive Intelligence

    Purpose-built models for the highest-value prediction problems. Demand forecasting, pricing, retention, fraud. The most expensive layer and the longest time to value. Only pays off when Layers 1 and 2 are stable.

  7. 06

    Security and Governance (Cross-Cutting)

    The governance layer runs in parallel across every other layer from day one. Acceptable use policy, tool intake controls, risk lane classification, data privacy, action governance. Not a final gate. It runs the whole length of the program.

Why Aventurasoft

Twenty years of digital transformation. One year into AI. The combination is the point.

Aventurasoft has been in business for 20 years. We have run digital and tech transformation programs, built custom software, and delivered data analytics work for enterprises across financial services, healthcare, retail, and technology. That is the operating history we built the firm on and it is the reason AI transformation work actually lands when we take it on. You cannot help an enterprise use AI if you do not already know how the systems, the data, and the org behave under pressure.

We launched our AI consulting practice in 2025. It was not a pivot. It was the next logical layer on top of the work we have always done. Our founder, Enrique Guitart, led AI transformation at Restaurant Brands International as Head of AI, where he was responsible for the AI portfolio across four brands and more than 2,000 employees spanning Burger King, Tim Hortons, Popeyes, and Firehouse Subs. Running a real AI program at that scale, with real governance, real workforce, and real P&L stakes, forces a level of discipline that you only get by doing it.

Most AI programs we see from the outside produce activity, not outcomes. The technology works. The measurement does not. The governance was borrowed from a data access model that was never built for autonomous action. The organization is still wired for roles, when the new unit of work is a decision loop. We focus on closing those gaps in the right order. We start with the boring work that actually moves the needle, upskilling the workforce and removing the fear layer, then we fix the data foundation, then we design for agents. In that order, for a reason.

We work with clients across the Americas and beyond. Current and recent engagements span New York, Miami, Panama, and Buenos Aires, and we travel wherever the work is.

Twenty years of operating history

We are not new to enterprise technology. Two decades of digital transformation, custom software, and data analytics sit underneath the AI practice. That history is why our AI work is grounded in how enterprises actually run.

Boring-but-necessary first

Tool selection, governance, training, and rollout come before agentic AI. We sequence the work so the workforce is upskilled and the fear layer is removed before we ask anyone to trust an agent with a decision.

Senior in the room

Every engagement is led by senior practitioners with operator experience. No junior layer, no account team handover. You work with the people who have actually delivered this work at scale.

Frequently Asked Questions

Common questions about AI transformation and how Aventurasoft can help your organization.

AI transformation consulting is the work of helping a leadership team move from isolated AI pilots to an operating model where AI is producing measurable, durable outcomes. In our experience the hard problems are not technical. They are measurement (reporting phase-appropriate progress instead of P&L returns that are not yet possible), operating model (making human and agent accountability explicit), and governance (treating action permission as its own design discipline). Aventurasoft focuses on those three, in that order.

Aventurasoft has been in business for 20 years. We spent the first two decades delivering digital transformation, custom software development, and data analytics work for enterprises. We launched our dedicated AI consulting practice in 2025, grounded in that operating history and in our founder's experience leading AI at a Fortune 500 organization.

Enrique is Co-Founder and CEO of Aventurasoft. Most recently he served as Head of AI at a Fortune 500 restaurant group, where he led enterprise AI transformation across multiple brands and more than 2,000 employees. Before focusing on the AI practice he spent two decades running digital transformation and software engineering engagements for enterprise clients across multiple industries. He holds executive programs from MIT Sloan in AI for business strategy and from the University of Miami Herbert Business School in Chief AI Officer practice.

Aventurasoft has offices in Miami and Buenos Aires and we work with clients across the Americas and beyond. Current and recent engagements span New York, Miami, Panama, and Buenos Aires. We travel wherever the work is.

Our dedicated AI consulting practice is new. We launched it in 2025. Our operating history is not new. The firm has been delivering digital transformation, software engineering, and data analytics for 20 years, and that is the reason our AI work lands. We have seen enough enterprise systems under pressure to know where AI programs actually fall over, and we have the data and engineering muscle to fix the foundation before the AI layer goes on top. Our founder also brings the direct operator experience of having led AI transformation at a Fortune 500 organization with multiple brands and thousands of employees in scope.

The AI Value Diagnostic is a 30-day engagement that audits your existing AI stack, surfaces shadow AI activity, quantifies realistic P&L impact by workstream, and delivers a sequenced roadmap against your actual cost structure. It is the entry point to every other engagement we offer. We kept it to 30 days on purpose, because a diagnostic is supposed to be fast and bounded. It is priced at consulting rates and produces a board-ready deliverable. Most organizations we run it for discover they are already paying for significant AI capability they have not activated.

Two reasons, and a risk we see in almost every client we meet. The risk is shadow AI. AI is already in your organization whether you sanctioned it or not. People are pasting customer data into free chatbots, building unreviewed workflows in personal accounts, and making consequential decisions on top of outputs that nobody audited. The first job of a serious AI program is to get that activity into the light, give people governed tools they can use openly, and put controls around what happens next. Training matters for two reasons. First, it reduces fear. Second, AI is not going to replace people, but people with AI skills will replace the people who do not have them. That is the real workforce story of the next three years.

It is the model we use to scope, sequence, and govern enterprise AI transformation. The layers are AI feature discovery in the existing stack (Layer 0), data readiness (Layer 1), AI analytics and business intelligence (Layer 2), generative AI in the workforce including productivity tools and embedded copilots (Layer 3), automation and AI agents (Layer 4), and machine learning and predictive intelligence (Layer 5). Security and governance run in parallel across all of them from day one.

Action governance is the set of controls that govern what an AI agent is allowed to do on your behalf. It is different from data governance. Data governance asks who can see what. Action governance asks who can trigger what action, under which policy, with what approvals, what memory, and what rollback. We treat it as a separate design discipline and we believe it will be the operating control point for enterprise AI over the next two years.

Because without a real data strategy, AI will fail. Every enterprise AI program we have seen fall over in the last 18 months fell over at the data layer. No model, no agent, and no copilot can compensate for a broken data foundation. We have been doing enterprise data analytics work for 20 years, which is why we do not treat the data layer as a checklist. We treat it as the thing the entire AI program depends on.

Fractional Head of AI is one of five services we offer. It is embedded AI leadership on a part-time basis, with standing meetings, board access, and real ownership of AI strategy, governance, and delivery oversight. Our founder has held this role at a Fortune 500 organization. The other four services are the AI Value Diagnostic, AI Tool Selection and Rollout, Data Strategy for AI, and the Agentic AI and Automation Operating Model.

Our deepest recent AI experience is in multi-brand consumer organizations, where our founder led transformation across multiple brands and thousands of employees at a Fortune 500 parent. Before the AI practice, our 20 years of digital transformation and software work covered financial services, healthcare, retail, and technology. Our current and recent engagements span New York, Miami, Panama, and Buenos Aires. Our framework is industry agnostic, but we take engagements selectively and we prefer to work where we can bring direct operational experience to the problem.

We use a federated governance model with risk lane classification. Low-risk AI use cases are fast-tracked through a lightweight intake. High-risk use cases go through a structured review that covers security, data privacy, regulatory alignment, and accountability. The model runs in parallel with delivery, not as a final gate.

Phase-appropriate metrics. In the first phase you are building capability and redesigning work, and the right signals are governance policy coverage, training completion, and feature activation rates, not P&L impact. In the second phase you are measuring adoption and workflow integration, and the right signals are usage depth, workflow completion time, and exception rates. In the third phase you are measuring P&L. Most organizations we see are running phase one or phase two work and being asked to report phase three metrics, which creates activity theater. We fix that first.

Yes. We evaluate tools from OpenAI, Anthropic, Google, Microsoft, Meta, and the serious open-source projects on a use-case by use-case basis. Our recommendations are based on fit, cost, governance surface, and operational maturity, not on partnership incentives. Claude for Enterprise, Microsoft 365 Copilot, Google Workspace AI, and ChatGPT Enterprise are all in scope on the productivity side. For embedded copilots we work with whatever ERP, CRM, and line-of-business systems the client is already running.

The AI Value Diagnostic runs 30 days from kickoff to final deliverable. AI Tool Selection and Rollout engagements typically run 12 to 20 weeks depending on the number of tools and the size of the rollout. Data Strategy engagements run 10 to 16 weeks depending on the number of domains in scope. The Agentic AI and Automation Operating Model runs 8 to 16 weeks for the design work, then becomes a standing oversight engagement through delivery. Fractional Head of AI is an ongoing engagement with a six-month minimum.

Yes. We publish regularly on enterprise AI transformation, agent governance, operating model design, data strategy, and the measurement architecture that makes any of it work. You can read our writing in the Insights section of this site. Our founder also publishes on LinkedIn.

Let's Talk

Tell us about your organization and what is live right now. We will come back within one business day.

Prefer a Conversation?

If it is easier to meet, we hold a small number of open strategy calls each week. Bring a live problem and we will bring directness. If we are not the right fit, we will tell you in the first ten minutes and save us both the time.

Book a Strategy Call

Our calendar runs on Eastern time. We work with clients across the Americas.

Direct Contact

info@aventurasoft.com