Beetroot

From carbon accounting and compliance reporting to energy efficiency and maintenance, AI can help organizations reduce emissions while also removing operational inefficiencies.

🌱 Our latest article explores where those opportunities exist and why carbon management doesn't have to come at the expense of business performance.

🔗 Read via the link in the comments

Where does AI actually create ROI in marketing?

In our latest white paper, we share practical lessons from AI initiatives in marketing operations, including use cases, implementation challenges, and how to approach adoption with both operational value and governance in mind.

📘 Download the resource via the link in the comments

Customer churn rarely comes out of nowhere. The challenge is turning those warning signals into timely action.

Our latest article explores how CX teams use predictive analytics to identify risk earlier, prioritize the right accounts, and build more proactive retention strategies.

🔗 Link in the comments ⬇️

Marketing teams have more data than ever. Yet getting a clear answer to a simple question can still mean jumping between dashboards, reports, documents, and multiple stakeholders.

Our recent article explores how RAG-powered marketing assistants help bridge that gap, and why the data layer matters far more than the chat interface itself.

🔗 Read on our blog: https://lnkd.in/dmrMfkVd

🏆 We're happy to share that Beetroot has been selected as one of the winners of Ukrainian Design: The Very Best Of for the Reroot app design.

For us, this recognition is first and foremost about the idea behind the project: supporting the long-term recovery of Ukraine. Seeing this approach acknowledged by the design community is both encouraging and meaningful.

🔗 Find out more about the award via the link in the comments

Your dashboards work, but your teams still don’t trust the numbers.

From conflicting metrics and broken pipelines to ML systems with unclear business impact, many enterprise data challenges are structural rather than purely technical.

What changes when companies start treating data as an operational product instead of a reporting byproduct? And why do so many data and AI initiatives struggle to create measurable business value?

We explore 10 common data management problems enterprises keep running into, along with practical ways to approach them.

🔗 Link in the comments ⬇️

Most supply chains still rely on data that is already outdated by the time decisions are made.

Our recent article explores where AI is already making a difference in logistics and supply chain operations: from demand forecasting and inventory optimization to route planning and disruption detection.

🔗 Read the full piece on our blog: https://beetroot.co/ai-ml/practical-ai-applications-for-autonomous-supply-chain-management/

If RAG is on your radar, this Medium piece by Den Pluhatar is worth a read.

A practical AI lead’s perspective on six retrieval-augmented generation strategies and where each one falls short.

The Journey Through 6 RAG Strategies to Find the Appropriate One Retrieval-augmented generation (RAG) has become a foundation for building high-accuracy, reliable systems on top of large language models…

In this article, Nick Tykhomyrov explores why so many AI assistants never make it into production, and what teams that succeed tend to do differently.

From governance and integration to UX and change management, it's a grounded look at what actually moves projects forward.

🔗 Read it on our blog: https://beetroot.co/ai-ml/conversational-ai-for-business-why-projects-stall/