Trusted AI Data Advisory
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Trusted AI Data Advisory helps companies find what’s blocking AI in their data, fix the highest-value gaps first, and build the governance and skills to adopt AI with confidence.
Scored against the AI use cases you actually care about, not a generic checklist.
The demo worked on a clean extract. Production data is scattered, inconsistent, or held by teams no one thought to involve.
Unclear ownership, undocumented definitions, and quality problems that only surface after an AI output looks wrong.
Privacy, security, and compliance reviews that halt a project in the final mile because guardrails weren’t designed in.
A structured baseline of your data across quality, access, architecture, governance, and skills, scored against the AI use cases that matter to your business.
A scored readiness profile and a prioritized list of gaps.
We trace each candidate AI use case back to the data it needs: where it lives, who owns it, what condition it’s in, and what it will take to use it.
A ranked use-case portfolio with data dependencies and effort.
Right-sized ownership, quality standards, privacy, and security controls for AI, designed to help teams move, not to slow them down.
A governance model and review paths your teams can actually follow.
Hands-on support to close priority gaps, plus the operating model, skills, and habits that make AI stick after we’ve left.
A sequenced roadmap, working sessions with your team, and a handover plan.
Interview stakeholders, inspect the data landscape, and establish where you really stand.
Rank use cases by value and feasibility so effort goes where readiness unlocks the most.
Close the critical gaps in quality, access, and governance, in the order your use cases demand.
Equip teams, embed ownership, and set the cadence that keeps readiness improving.
Every engagement ends with your team able to run it without us.
Who need AI initiatives to scale on a foundation the organization can trust and support.
Who need to show where data stands, and make the case for what to fix first.
Who need pilots to reach production and deliver the value that was promised.
Albert Lalaj has spent more than 20 years helping large, regulated organizations turn fragmented data into assets they can trust. He has led enterprise data governance at a regulated financial institution, delivered data programs across industries as a consulting manager at Accenture, and today leads AI acceleration and data governance for a workforce of more than two million people.
His work centers on the foundations that decide whether AI succeeds: semantic layers that keep business metrics consistent, access models that match who needs data to how sensitive it is, quality controls that catch problems before a model does, and governance people actually follow. He has built the data councils, stewardship networks, and literacy programs that turn those practices from policy into habit.
Trusted AI Data Advisory brings that hands-on experience to organizations that want their AI programs to start on solid ground.
Leads data governance, analytics, and AI acceleration for a workforce of more than two million people: semantic layers, persona-based data access, and AI adoption at enterprise scale.
Ran enterprise data governance at a regulated financial institution: policies, stewardship, data quality, and the data behind regulatory reporting, including remediation of supervisory findings.
Delivered data quality, architecture, and master data programs for pharmaceutical, financial services, publishing, and higher-education clients, then led public-sector reporting and e-services projects in Europe.
No. Readiness is relative to the use cases you’re pursuing. We focus on the data those use cases depend on, so you make progress on real projects while your foundations improve.
Yes. Our background is in regulated environments, including banking and life sciences, where AI readiness has to account for model risk, privacy, and auditability from the start.
We start from your AI goals, not from a technology. We work out what needs to change and in what order, then work alongside your team and existing delivery partners to make it happen.
[X–Y] weeks, depending on the number of business units and use cases in scope. We confirm scope and timeline on our first call.
Tell us what you want AI to do for your business. We’ll tell you, plainly, what stands between you and it.