AI transformation
AI succeeds when it’s built into how work actually runs, not alongside it. And that requires two things moving together: foundations built for continuous evolution and new operating models designed for human–agent collaboration.
Slalom builds both—meeting you where you are and moving you forward.
When AI runs through the business, the numbers follow. Organizations that build adaptable foundations and redesign work around AI see measurable results: faster decisions, scaled impact, and value that grows over time.
Note: These results are based on past client engagements. Outcomes will vary based on implementation and context.
Set clear AI priorities tied to measurable outcomes—aligning leaders, investments, and use cases to where AI will actually change the business over time.
This solution can help your organization:
An AI value assessment and roadmap that helps leaders evaluate readiness, prioritize high-impact use cases, and connect AI investments to measurable business value.
Establish ownership, governance, and operating models that define how AI decisions are made, measured, and managed across the business.
This solution can help your organization:
Identify where your organization is ready for AI, where gaps are slowing progress, and what to prioritize next.
Deploy embedded AI teams to design, validate, and refine AI in real workflows, connecting business priorities, engineering execution, and user experience from day one.
This solution can help your organization:
Analyze roles, tasks, and workflows to identify where AI can create the greatest operational impact.
Build reusable AI foundations—data, platforms, pipelines, and operational controls—that help AI run reliably across systems, teams, and use cases.
This solution can help your organization:
Quickly build and scale agentic AI workflows through a reusable framework to accelerate enterprise impact.
Redesign roles, workflows, and decision making for human–agent collaboration so teams can adopt new ways of working with clarity and confidence.
This solution can help your organization:
Measure leadership adaptability across five dimensions to identify where teams can better navigate AI-driven change.
Start where you are. Build what it takes to keep AI moving across the business.
AI doesn't usually fail in early testing. It breaks down when it hits real workflows, where the data isn't connected, ownership isn't clear, and operating models weren't built for AI. These common enterprise AI challenges often slow AI adoption and limit impact.
Making AI work at scale requires a connected system across strategy, technology, and how work gets done. Strong AI solutions combine clear priorities, scalable foundations, and workflows designed for human–agent collaboration so results can extend across the business.
Start with the business problem, not the technology. A strong AI strategy focuses on where decisions are slow, processes are manual, or better data could improve outcomes. From there, organizations can prioritize use cases, align leaders, and begin building toward scalable AI solutions.
AI governance defines how AI is managed across the business, including ownership, risk, and decision-making. As AI adoption grows, governance helps organizations scale responsibly, supporting responsible AI practices without slowing progress.
Even organizations with platforms and data in place often find each AI initiative feels like it’s starting over. Without shared operating models and a repeatable path to production, use cases stay isolated. The goal is to keep what’s working and connect it, so AI scales across the business instead of case by case.
Moving from pilots to production requires embedding AI into real workflows, not just testing isolated use cases. Approaches like embedded, cross-functional delivery teams help organizations rapidly build, validate, and scale solutions as part of day-to-day work.
Deploying AI is one thing. Running it is another. AI agents and systems need ongoing monitoring, optimization, and maintenance as models change and business needs shift. Organizations that plan for this from the start protect performance—and get more value from their AI investments over time.
Responsible AI means AI systems are secure, transparent, and monitored for risk, bias, and performance in real time. It’s an ongoing part of AI governance, ensuring AI can scale while maintaining trust and compliance.
You should be able to measure where AI is improving outcomes and where it’s not. Clear metrics and visibility into performance are essential for understanding AI ROI and making confident investment decisions.
Organizations choose Slalom because we help turn AI ambition into sustained business performance. As a trusted Microsoft and Databricks partner, we combine strategy, implementation, AI governance, and organizational change to help customers move from experimentation to enterprise adoption.
Our teams help organizations modernize data with the Databricks Data Intelligence Platform, deploy Microsoft Azure AI and Azure OpenAI solutions, implement Microsoft Copilot and Azure AI agents, and scale AI with the governance and operational foundations required for enterprise adoption.
Just as importantly, we work alongside the people responsible for making AI successful, aligning technology, governance, and new ways of working so solutions deliver measurable outcomes, not just technical deployments.
AI adoption happens when people have the confidence, skills, and support to use new tools in meaningful ways.
Slalom helps organizations accelerate adoption by bringing together Microsoft AI solutions, Databricks data and intelligence capabilities, and the enablement, governance, and change support required to make AI part of everyday work.
We help teams identify practical applications for AI, build new capabilities, and adopt new ways of working that create lasting impact.
Adoption becomes easier when teams can see where AI fits, how it helps, and what success looks like.
Many organizations can help deploy AI technologies. Slalom focuses on what it takes to make them work inside a complex business.
Together with Microsoft and Databricks, we help customers build the data, governance, and technology foundations required for AI transformation while also preparing teams, workflows, and operating models for change. By combining technical delivery with organizational adoption, we help customers move beyond pilots and create AI capabilities that continue to deliver value over time.
This approach helps organizations turn AI from a series of projects into a lasting business capability.
Microsoft and Databricks provide powerful capabilities for AI, analytics, and data modernization. The challenge is knowing where to focus next.
Slalom helps customers identify new opportunities within existing investments, from expanding Microsoft Copilot and Azure OpenAI use cases to improving data accessibility, governance, and AI readiness across Databricks environments. By aligning technology investments to business priorities, we help organizations increase adoption, reduce duplication, and unlock more value from the platforms they already own.
Speed comes from knowing where to start.
Rather than pursuing dozens of disconnected ideas, Slalom helps organizations focus on the opportunities most likely to improve performance. From modernizing data foundations on Microsoft Azure and Databricks to deploying Azure OpenAI, Microsoft Copilot, and AI solutions in targeted areas of the business, we help customers concentrate effort where it matters most.
Our teams bring proven delivery experience, accelerators, and deep platform expertise, but the real advantage is focus. By aligning technology investments to clear business priorities from the beginning, organizations can spend less time evaluating possibilities and more time moving initiatives forward.
AI ideas are easy to generate. Choosing the right ones takes discipline.
Slalom works with customers to evaluate opportunities through multiple lenses, including business need, data availability, technical readiness, and organizational fit. Using Microsoft Azure AI, Azure OpenAI, Microsoft Copilot, and Databricks capabilities as part of the evaluation process, we help organizations distinguish promising ideas from practical opportunities.
The process creates a clearer roadmap for where to invest, what to prioritize, and how to build momentum over time.
Build the foundations and operating models for human–agent collaboration.
