AI governance & adoption for financial services

Put AI to work - without losing control.

We help banks, fintechs and insurers identify where AI creates real value, govern how it is used, implement the right solutions, train teams and measure what actually works.

Explore our AI capabilities
A smiling Speednet team member writing on a glass board during a workshop.

AI adoption with productivity and control built in.

  1. Assess
  2. Govern
  3. Implement
  4. Train
  5. Measure
  6. Scale

Backed by Speedwave, our open-source AI-SDLC framework

What scalable AI adoption requires

Successful AI adoption needs more than just access to the right tools.

AI has to be useful enough for people to adopt, controlled enough for the organisation to trust, and measurable enough to justify scaling.

AI has to be useful enough for people to adopt, controlled enough for the organisation to trust, and measurable enough to justify scaling.

  • Productivity

    Help people do more valuable work faster.

  • Adoption

    Move beyond pilots into repeatable everyday workflows.

  • Control

    Know where AI is used, what it can access and who remains accountable.

  • Evidence

    Measure usage, impact, risk and compliance.

Key challenges

Why AI pilots struggle to become real organisational capability.

AI experiments are easy to start. The difficult part is turning fragmented usage into secure, useful and repeatable ways of working across the organisation.

  • AI use is already happening without a common model

    Different teams adopt different models, assistants and workflows before the organisation has agreed what should be allowed.

    Turn Shadow AI into governed adoption.

  • Governance becomes a blocker instead of an enabler

    Policies written without understanding how people actually use AI either fail to control risk or make useful adoption unnecessarily difficult.

    Design controls around real workflows.

  • Organisations struggle to identify valuable use cases

    AI gets applied where it is interesting rather than where it improves cycle time, quality, customer experience or operational cost.

    Start with measurable value, not the model.

  • Pilots never become organisational capability

    A successful PoC does not answer how to train teams, manage access, measure effectiveness or scale usage safely.

    Design the rollout before the pilot becomes another isolated experiment.

  • AI productivity is difficult to prove

    Tool adoption and licence counts do not demonstrate whether AI is actually improving delivery or business outcomes.

    Measure impact, not just usage.

  • Regulation and accountability are still evolving

    AI Act, GDPR, DORA and internal governance create obligations around risk, data, oversight and evidence.

    Build governance and evidence into adoption from the start.

Illustration of a brain made of circuit lines on an orange circle.

Our AI capabilities

Move AI from experiment to everyday work.

  • AI readiness & governance assessment

    Understand where you are before deciding where AI should go next. Assess current AI usage, workflows, tools, data exposure, governance gaps, risk ownership and organisational readiness.

  • AI strategy & use-case prioritisation

    Focus investment where AI can create measurable value. Identify and prioritise AI opportunities across software delivery, operations and customer-facing products against value, feasibility and risk.

  • Responsible AI & governance

    Define the rules without stopping useful adoption. AI policies, roles, risk classification, access boundaries, human oversight, audit requirements and evidence aligned with the organisation's regulatory environment.

  • AI implementation & pilots

    Turn promising use cases into working solutions. Architecture, integration, prototyping and implementation of AI-enabled workflows or products, followed by validation against agreed success criteria.

  • Training & adoption

    Give teams the skills and operating model to use AI effectively. Role-based training, practical workshops, approved workflows and adoption support for engineering, product, business and governance teams.

  • Measurement & optimisation

    Make AI effectiveness visible. Define baselines, adoption metrics, productivity measures, quality indicators and governance evidence so AI investment can be managed rather than assumed.

Featured · Speedwave

Scale AI-assisted software delivery without losing control.

Speedwave is Speednet's open-source AI-SDLC framework for regulated engineering teams. It works around the AI tools developers already use, adding controlled environments, access boundaries, sensitive-data protection, auditability and governance.

  • Controlled environments

    Limit what AI can access across code, tools and project context.

  • Sensitive-data protection

    Reduce exposure of credentials and sensitive information in AI-assisted workflows.

  • Auditability

    Create visibility and evidence around AI-assisted engineering activity.

  • Tool flexibility

    Work with approved external or local models instead of forcing one AI vendor.

Developers get AI assistance. Your organisation keeps control.

Explore Speedwave
Speednet team members in a meeting room with a view over the city.

Governance meets performance

Governance should make AI easier to scale, not harder to use.

Control enables scale. Measurement proves value.

  1. Safe to use

    Clear access, data and approval boundaries.

    01

  2. Useful in real work

    AI embedded into the workflows people already use.

    02

  3. Adopted at scale

    Training, standards and support beyond the first pilot.

    03

  4. Measurable

    Productivity, quality, usage and risk tracked against a baseline.

    04

Let's talk AI

Already experimenting with AI but unsure how to scale it safely?

Whether the challenge is governance, adoption, measurable productivity or moving a promising use case into production, we can help define the next step.

Talk to an AI expert

Our approach

Start with one use case. Prove it works. Scale from there.

  • 1

    Assess

    Map current AI usage, tools, workflows, business objectives, security constraints and governance maturity.

  • 2

    Prioritise

    Identify use cases where business value, feasibility and acceptable risk overlap.

  • 3

    Govern

    Define access, data boundaries, human oversight, accountability, policies and evidence requirements.

  • 4

    Pilot & measure

    Implement a controlled use case with a clear baseline and measurable success criteria.

  • 5

    Train & roll out

    Prepare teams, operating processes and technology for broader adoption.

  • 6

    Improve

    Measure effectiveness, risk and usage, then refine workflows and governance as adoption grows.

A practical way to start with Speedwave

Start with one engineering team and one measurable use case.

What you leave with

A focused workshop to identify a high-value engineering use case, define the required controls and create a measurable path to pilot.

A focused workshop to identify a high-value engineering use case, define the required controls and create a measurable path to pilot.

  • Priority use case

    A concrete workflow where AI can create measurable delivery value.

  • Control requirements

    Clear boundaries around code, context, tools and human oversight.

  • Success baseline

    Agreed measures for productivity, quality and adoption.

  • Pilot roadmap

    A practical path from assessment to controlled implementation.

Responsible AI by design

Know what AI is doing, what it can access and who is accountable.

AI Act · GDPR · DORA · ISO 42001

AI Act · GDPR · DORA · ISO 42001

  • What AI do we use?

    Systems · Models · Tools · Use cases

  • What can it access?

    Data · Code · Repositories · Internal tools

  • Who remains accountable?

    Owners · Human oversight · Decision rights

  • Can we prove what happened?

    Logging · Monitoring · Evidence · Auditability

Where we apply AI

Practical AI use cases across products, engineering and operations.

  • Software engineering

    AI-assisted development, testing, documentation and code modernisation.

  • Legacy modernisation

    Code understanding, dependency analysis, documentation and migration support.

  • Business operations

    Knowledge workflows, document processing and repetitive decision-support tasks.

  • AI-enabled products

    Customer-facing and internal AI capabilities integrated into existing systems and processes.

Featured insight

Research and practical thinking for AI leaders.

Report cover: Your organisation has implemented AI. What's next?Report cover: AI Governance Costs in EU Banking: Meta-analysisReport cover: Beyond AI: Agentic Banking

  • 9 AI governance pillars you cannot overlook: what to do after deploying AI so it stays reliable, safe and compliant.

    Download for free

  • AI Governance Costs in EU Banking: what compliance for high-risk AI models really costs and where the costs come from.

    Download for free

  • Beyond AI: Agentic Banking. A guide to what actually works with AI agents in EU banking in 2026.

    Download for free

AI insights

Ideas for adopting, governing and scaling AI in regulated organisations.

FAQ

Frequently asked questions about AI governance & adoption

  • Do you only help with AI governance?

    No. We support the full adoption lifecycle: readiness assessment, use-case prioritisation, governance, architecture, implementation, training, rollout and measurement.

  • Can you audit how AI is already being used in our organisation?

    Yes. We can assess current tools and use cases, how data and systems are accessed, existing policies and controls, ownership and governance gaps, then provide prioritised recommendations.

  • Can you help us identify the right AI use cases?

    Yes. We evaluate potential use cases against business value, feasibility, available data, integration requirements and risk rather than starting from a particular model or vendor.

  • Do you implement AI solutions as well as advise on them?

    Yes. Speednet can support architecture, integration, development, testing and production rollout after the assessment or pilot phase.

  • Do you provide AI training?

    Yes. We can provide role-based training and practical workshops focused on approved tools, workflows, risk awareness and effective use of AI in everyday work.

  • How do you measure whether AI is actually improving productivity?

    We define a baseline before rollout and agree relevant measures such as delivery time, throughput, quality, rework or task-specific efficiency. The exact metrics depend on the use case.

  • What is Speedwave?

    Speedwave is Speednet's open-source AI-SDLC framework for regulated engineering organisations. It provides a controlled environment around AI-assisted software delivery so organisations can manage access, visibility and governance without replacing the AI tools developers already use.

  • Is Speedwave another coding assistant?

    No. Speedwave works around AI coding assistants rather than replacing them. Its role is to provide controlled access, security boundaries, governance and auditability around AI-assisted engineering.

  • Can we use local models?

    Yes, where supported by the selected architecture. We can help design an operating model that accommodates local or external models depending on data sensitivity, security policy and infrastructure requirements.

  • Can we start with one team or one use case?

    Yes. We generally prefer a controlled assessment or pilot with a measurable baseline, then scale once value and controls have been validated.

  • Can you work with the AI tools and models we already use?

    Yes. Our approach is not tied to a single model or vendor. We can work with existing AI tools and architecture, then help define the controls, integrations and operating model required to use them safely and effectively.

Let's talk AI

Where is AI adoption getting stuck in your organisation?

Tell us whether the challenge is value, governance, implementation, adoption or measurement. We'll connect you with a Speednet expert and help define a practical next step.

Michał Grela, a smiling man in a dark jacket

Michał GrelaHead of Growth

michal.grela@speednet.pl+48 600 023 843