Deloitte introduced ReadyAI, a full portfolio of capabilities and services to help organizations accelerate and scale their artificial intelligence (AI) projects.
ReadyAI brings together skilled AI specialists and managed services in a flexible AI-as-a-service model designed to help clients scale AI throughout their organizations.
With Deloitte's ReadyAI, organizations now have access to the services, technology and expertise they need to accelerate their AI journey.
"In the Age of With™, human and machine collaboration is taking organizations to new heights. While AI adoption is accelerating, many organizations struggle to scale their AI projects," said Nitin Mittal, AI co-leader and principal, Deloitte Consulting LLP. "ReadyAI provides the flexible and scalable capabilities that these companies need to successfully become AI-fueled organizations."
ReadyAI offers comprehensive service capabilities including:
- Data preparation: Provide data extraction, wrangling and standardization services. Also supports advanced analytical model development through feature engineering.
- Insights and visualization: Design and generate reports and visual dashboards utilizing data output from automations to improve business outcomes and automation performance.
- Advanced analytics: Data analysis for both structured and unstructured data. Creation of rule-based bots and insights-as-a-service.
- Machine learning and deep learning: ML and deep learning model development. Video and text analytics to assist conversational AI.
- Machine learning deployment: Create deployment architecture and pipelines for upstream and downstream integration of ML models.
- Model management and MLOps: Management of model performance, migration and maintenance. Automation of model monitoring process and overall DevOps for machine learning.
"The promise of AI lies in its deployment at scale in a fair, ethical and trustworthy fashion," said Rohit Tandon, GM for ReadyAI and managing director, Deloitte Consulting LLP. "ReadyAI helps clients take AI all the way from labs and pilot programs to real-life business integration and adoption."
With a talent pool of more than 3,100 AI professionals, Deloitte can assemble teams that have the right combination of industry, domain and AI technology skills to best suit clients' needs. These experts include cloud engineers, data scientists, data architects, technology and application engineers, business and domain specialists, and visualization and design specialists. By leveraging the right combination of skills, organizations can quickly accelerate their AI journey.
ReadyAI teams operate as an extension of clients' teams often for engagements of six months or more. Services are available as a flexible, subscription model, allowing clients to scale resources and capabilities up or down based on business needs and priorities.
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...