Cloud Lighthouse Labs

Flexible, low-risk investments for strategically-minded, innovative organizations

We launched the Cloud Lighthouse Labs to take on technical challenges that exist between the arrival of new technology and a typical organization’s decision to invest heavily in that new tech. Our partner organizations with whom we craft, architect, and execute Cloud + AI strategy can rely on the Labs to rapidly experiment, innovate, and invest with like-minded organizations before making the decision to implement in-house or alongside a trusted implementation consultancy. And, as always, our clients need not be concerned that we have a hidden motive to sell them an army of implementation consultants afterwards: It’s not even a service we offer.

Technology Experimentation

Explore new and much-hyped cloud and AI tech, so that you’re smarter to their possibilities before you take the plunge on a big investment, contract, or costly implementation effort.

Early-Stage Innovation

Collaboratively develop early proofs of concept to see how specific technologies and architectures will work in your organization. Then turn everything we’ve built over to your internal team or implementation partner.

Shared Investment

Work with Cloud Lighthouse to identify other like-minded organizations facing the same challenges as you, then share the burden of innovation by investing together. You’re not alone.

What kinds of challenges are we solving in the Labs?

We’re helping our partners experiment, innovate, and share investment before they get to more costly implementation stages…

Can we connect our single source of truth sitting in OneLake or another data lake to a custom Copilot?

Is Power Pages the right choice as a portal to manage our thousands of field personnel and volunteers?

We need a better way to monitor and govern AI adoption, but we don’t need to own the tools ourselves.

Just how exposed are we to the risks of data sitting in SharePoint training AI tools like Copilot… without us knowing?

How straight-forward will it be to move our application data to Dataverse and shortcut it to the lake?

A law firm needs this custom connector. A global NGO and a utility company need it, too. Let’s share the cost!

Insights from the Labs

We share what we learn, so that you can learn with us…

Featured
"AI Strategy Framework" guides your organization's journey in the Age of AI
"AI Strategy Framework" guides your organization's journey in the Age of AI

We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time for a proper model through which organizations may realistically assess their readiness to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey. This AI Strategy Framework guides organizations as they construct their AI strategy atop five pillars, each with five dimensions to be considered, matured, and regularly evaluated.

Data Distribution in Power Platform
Data Distribution in Power Platform

When taking on the question of how Power Platform integrates with Azure data services, Point-to-Point, Data Consolidation, Master Data Node, and Data Distribution evolve a similar theme. Specifically, each focuses primarily on transactional data during any given users interaction with it. “Data Distribution” is different, focusing more on data distributed for analytics, enterprise search, integration with third-party or external sources via API, data science workloads, or training or augmenting a large language model (LLM). This blog overviews the Data Distribution pattern.

White Paper: “Crafting your Future-Ready Enterprise AI Strategy, e2”
White Paper: “Crafting your Future-Ready Enterprise AI Strategy, e2”

It has become obvious how difficult so many organizations are finding it to actually craft and execute their AI strategy, in part because of the (often) decades they’ve spent kicking their proverbial data can down the road, in part because it turns out that enterprise-grade AI really does require the adoption of ecosystem-oriented architecture to truly scale, but largely because many organizations have no idea where to start. Many lack the wherewithal to really assess where they stand on day one, and to identify areas where they must mature to get to day 100 (and beyond).

We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time to broaden the thesis, so in this second edition we offer a model through which organizations may realistically assess their current maturity to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey.

White Paper: “Ecosystem-Oriented Architecture in the Public Sector”
White Paper: “Ecosystem-Oriented Architecture in the Public Sector”

This whitepaper offers CIOs, enterprise architects, and other public sector technologists a comprehensive introduction underscoring the need to adopt ecosystem-oriented architecture (EOA) to build scalable, resilient, and flexible cloud ecosystems that can absorb successive waves of technological change.

Five strategies to integrate Power Platform in your data platform architecture
Five strategies to integrate Power Platform in your data platform architecture

When comparing architectural models for Power Platform, it’s important to avoid the instinct to choose just one. Instead, the goal is to explore various approaches that enable different scenarios for integrating Power Platform solutions with enterprise data. Each organization should strategically mix and match these approaches, considering factors like performance, flexibility, maintainability, and cost. This strategy allows for creating adaptable patterns within a cloud ecosystem where Power Platform plays a key role.