AI and Data Discovery and Adoption Consulting

Q: How is progress or ROI measured?

We measure progress and ROI through identified initiatives, developed MVPs, their adoption level, and obtained or projected results. The monitoring dashboard allows management to know what has been launched, what impact it generates, and where it makes sense to continue investing.

Q: How much time does my team need to dedicate?

The dedication is adapted to each person’s role. Internal managers have a more active and recurring participation in the project, while other teams intervene when necessary. We define the dedication during activation to integrate it into regular operations.

Q: Can I start with a closed project?

Yes. It is possible to start with a closed initiative to test its methodology, identify opportunities, or develop an MVP to validate its viability. Based on the results, it is assessed whether it is necessary to extend the work towards a continuous AI adoption model, with new initiatives and regular monitoring.

Q: What is an AI Champion and what is their role?

An AI Champion is the key person within the company to drive AI adoption in their department. They help identify opportunities and connect the rest of the team with the project. Their role is fundamental to generating adoption from within and avoiding total dependence.

Bring AI from paper into everyday work

Most companies want to adopt and apply artificial intelligence, but many fail to implement it. With our AI and data discovery and adoption consulting, we help companies that want to start using these technologies by identifying opportunities, creating measurable prototypes, and supporting the team during implementation.

We work with internal champions, a regular rhythm, and continuous monitoring to turn initiatives into work habits. This is not AI training or an isolated strategic report, but rather a service aimed at facilitating the adoption and integration of artificial intelligence into the company’s daily operations.

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Why choose LISDATA?

LISDATA’s AI adoption service combines strategy, prototyping, change management, and ongoing support to ensure adoption progresses within the organization and translates into concrete business applications.

For years, we have worked with large and medium-sized companies in data, analytics, and artificial intelligence, from data capture and governance to the implementation of artificial intelligence and machine learning projects.

We are committed to a more agile and accessible model than that of a large consultancy, with less external dependence, more internal capacity, and a pace that fits the company’s operations without a full-time deployed team.

Ongoing support, not one-off training

We support teams throughout the adoption process so that new tools and ways of working are incorporated into daily operations seamlessly.

Functional prototypes, not just a roadmap

Identified opportunities are quickly put into practice through prototypes that allow their usefulness to be validated before undertaking larger-scale artificial intelligence projects.

Technology and change management

We address both the technological solution and its adoption, with data and AI training adapted to each role, internal communication, and team support.

Internal champions to drive adoption

We involve a key contact person per department, an AI Champion, to facilitate implementation, gather new opportunities, and develop capabilities within the company.

A sustained system over time

We establish a regular work rhythm to identify and prioritize ideas, develop prototypes, and monitor progress, providing continuous insight into advancements and opportunities.

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Why AI adoption fails

95% of AI projects do not generate ROI. The problem is usually not a lack of interest or investment; what typically fails is how to implement AI in the company for daily use.

Without internal champions, a prioritization rhythm, and rapid prototyping, initiatives cool down, and investment becomes difficult to justify. These blockages are usually due to:

Not knowing where to start

Many opportunities exist, but it’s hard to identify which ones have the greatest business impact.

Lack of alignment

Business, technology, and people do not always share priorities, objectives, or criteria for evaluating initiatives.

Absence of internal leadership

Without internal champions within the organization, initiatives lose continuity.

Strategies that are not executed

Use cases and roadmaps are defined, but a system to convert them into concrete, real actions is missing.

Unvalidated ideas

Before making a major investment in a digital transformation plan, it is necessary to verify that a solution is technically viable and provides value.

Licenses without adoption

Having AI tools does not guarantee that teams will incorporate them into their work processes.

What our discovery and adoption consulting includes

Discovery involves identifying, prioritizing, and validating the use cases with the most impact. Adoption is the next step: ensuring these initiatives are actually used.

In our AI and data discovery and adoption consulting, we work on strategy, use cases, prototypes, people, and monitoring.

Diagnosis and activation

We evaluate the organization’s maturity level, identify opportunities and quick wins, and define the first AI adoption roadmap. We also select a key contact person per department to drive internal incorporation.

This diagnosis includes data governance to decide which data can be worked with, what can be prototyped now, and what limits must be respected.
Use case discovery

More use cases are identified together with the teams themselves, who have first-hand knowledge of the processes where AI can add value. A regular committee helps prioritize them and maintains an initiative pipeline with owners and next steps.
Rapid prototypes (MVP)

A prioritized initiative becomes a functional test in 5-10 days, allowing its viability and usefulness to be validated before undertaking a larger-scale development. MVPs are planned and developed according to the needs and scope of each initiative.
Change management and adoption by roles

Adoption requires adapting the solution to the people who will use it. We work on contextualized training by role, internal communication, and team support to facilitate their incorporation into regular processes. When it is necessary to delve deeper into a specific tool, we also offer specific training.
Monitoring for management

We create dashboards that track the evolution of initiatives, developed prototypes, adoption level, and projected impact. This allows managers to know what is being tested, what has progressed, and where it makes sense to focus the next efforts.

1. Activation

First month. We lay the foundations for the project to advance within the company. We select a key contact person per department who helps identify opportunities and facilitates connection with the teams.

From there, we evaluate the maturity level, detect opportunities, and define a quarterly work plan with the initial priorities.
2. Living methodology

Months 1 to 6. The project enters a recurring work dynamic. A bi-weekly committee reviews initiatives, makes decisions, and keeps the AI project pipeline active within the organization.

In parallel, we work on change management to facilitate the incorporation of new solutions by teams.

The program’s evolution is reflected in a progress dashboard for management, with visibility into ongoing initiatives, their evolution, and next steps.
3. Acceleration

Quarterly or semi-annual review. Periodic review allows adapting the program to business priorities. We review the funnel, reprioritize initiatives according to strategy and available resources, and plan new MVPs. Thus, validated initiatives move to larger-scale deployment, and those that are not are discarded or reformulated.

We also adjust the roles and dedication of those responsible according to the needs of each stage.

Situations we solve with AI discovery and adoption

AI adoption often stalls when opportunities lack clear priority, resources are insufficient for implementation, or teams do not incorporate tools into their workflow.

Our AI discovery and adoption consulting is designed to overcome these blockages and turn ideas into real changes.

Focus and prioritization

When different teams propose possible AI applications and it is difficult to decide which ones to address first, we help organize and prioritize use cases according to their business value, viability, and available resources.

Validations before investing in major development

We develop MVPs and proofs of concept that allow for rapid validation of the usefulness and viability of ideas before committing more resources.

Companies without a dedicated internal AI team

We work with assigned managers within the organization, providing structure and support that enables initiatives to be driven without complete reliance on an external team.

Available tools with little actual use

Having access to AI tools like Microsoft Copilot does not guarantee that teams will incorporate them. We identify application opportunities by area, support users, and work on adoption to convert available tools into concrete uses.

Need to demonstrate results

We build quick wins, use cases, and results from initial initiatives that help assess impact and justify investment.

Tangible results

A large Spanish industrial group had several internally prioritized AI initiatives, but needed specialized support to evaluate their viability, review technical approaches, and guide the team before proceeding with development.

We worked together on a monthly support service for 6 months. The result was a continuous support framework for making technical decisions with greater discernment and advancing the company’s prioritized initiatives.

Viability assessment

Alternatives and risks of each initiative.

Review of approaches and architecture

Models, data, and tools.

Technical mentoring for the team

For the internal team, to facilitate decision-making.

Trends, frameworks, and providers

Recommendations on trends, frameworks, and providers.

AI and Data Science best practices

Alignment of projects with AI and Data Science best practices.

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Do you need adoption, training, or both?

Adoption and training address different needs and can be combined. LISDATA’s AI adoption consulting helps identify opportunities, organize change, and ensure artificial intelligence is incorporated into work processes.

During the project, training needs may be detected and addressed through more specific data and AI training.

If the challenge is more specific and the team already uses tools like BI, Copilot, Claude, ChatGPT, or a specific tool, it can be contracted independently to improve its use and better leverage its functionalities.

Tools and technologies

Whenever possible, we start with the tools and technologies the company already has available, avoiding the introduction of new solutions when not necessary. The goal is to make the existing stack generate more value and facilitate its adoption by the teams.

We work with Business Intelligence (BI) tools, cloud environments like Azure, and AI solutions such as Microsoft Copilot, Claude, ChatGPT, and proprietary tools like aisQ AGENT.

Additionally, we have experience and collaboration as a Claude partner and a Data Governance team with DAMA certification.

How is this different from AI training?

Training develops specific knowledge and skills about specific tools. Adoption consulting addresses the complete process from diagnosis, opportunity identification, prioritization, prototyping, and integration. Training can be part of the support when additional team needs are detected.

Do I need to have an internal AI team already to start?

No. Our consulting is also designed for companies that do not yet have a dedicated AI team. We identify AI Champions within the organization and work with them to build internal capacity, coordinate initiatives, and facilitate adoption, with our ongoing support throughout the process.

What if my data is not organized?

Data analysis is part of the initial AI maturity diagnosis. We evaluate whether the quality, availability, and organization of the data allow each initiative to be addressed and detect its limitations. This enables prioritizing viable use cases and defining necessary data improvements.

How long does the support last?

The duration depends on the objectives and starting point of each company, but the minimum for consulting and deploying AI adoption is 6 months, extendable according to each company’s needs.

How is progress or ROI measured?

We measure progress and ROI through identified initiatives, developed MVPs, their adoption level, and obtained or projected results. The monitoring dashboard allows management to know what has been launched, what impact it generates, and where it makes sense to continue investing.

How much time does my team need to dedicate?

The dedication is adapted to each person’s role. Internal managers have a more active and recurring participation in the project, while other teams intervene when necessary. We define the dedication during activation to integrate it into regular operations.

Can I start with a closed project?

Yes. It is possible to start with a closed initiative to test its methodology, identify opportunities, or develop an MVP to validate its viability. Based on the results, it is assessed whether it is necessary to extend the work towards a continuous AI adoption model, with new initiatives and regular monitoring.

Are prototypes (MVPs) included?

The development of an MVP depends on the needs of each project. It can be addressed through a specific project with us, a Proof of Concept (PoC), or with a technology partner, depending on the case.

How does this fit with your artificial intelligence consulting?

Both services complement each other. Artificial intelligence consulting identifies how to implement AI in the company and proposes machine learning, generative AI, or predictive analytics solutions according to detected needs. Adoption consulting focuses on AI change management, ensuring teams use it and maintaining its evolution.

What is an AI Champion and what is their role?

An AI Champion is the key person within the company to drive AI adoption in their department. They help identify opportunities and connect the rest of the team with the project. Their role is fundamental to generating adoption from within and avoiding total dependence.

Ready for AI to be truly used in your company?

Move from ideas to application with our custom-designed adoption program for your organization. We identify opportunities, validate initiatives, and support your teams to incorporate AI into their daily work. Let’s discuss your starting point and the first opportunities we can activate.

Request an AI adoption diagnostic