The Right Technology for the Right Solution
Built around your business. Designed for the outcome you need.
Every business faces a different mix of challenges, from repetitive work that can be automated to complex decisions that require deeper reasoning. There is no single technology that is right for every problem.
We start by understanding the business problem and the outcome you want to achieve. From there, we determine the right combination of Generative AI, AI agents, Causal AI, automation, existing technologies, or other capabilities needed to solve it.
The sections below introduce two areas where we see significant opportunity: Causal AI for problems that require deeper reasoning, simulation, and an understanding of cause and effect; and AI Workflows and AI Agents for work that can be automated, coordinated, or performed more efficiently. In many solutions, these technologies work together.
And technology will continue to change. That's why we configure solutions around the business capability you need, with the flexibility to evolve as better technologies emerge. Your investment shouldn't depend on today's technology remaining tomorrow's best answer.
The goal isn't to use more technology. It's to use the right technology to create the outcome you want.
The right problem. The right technology. The right outcome.
Causal AI: Reasons, Recommends and Explains
Understanding Why Things Happen and What to Do About Them
Most AI works with information, data, or patterns. Generative AI and LLMs can find, summarize, create, explain, and communicate. Machine Learning and Data AI can identify patterns and correlations in historical and real-time data.
Causal AI can include those capabilities but adds something fundamentally different: reasoning based on cause and effect. It models how factors within a business affect one another, combining the knowledge and reasoning of subject matter experts with enterprise data to understand why something is happening, what else it may affect, and what actions could change the outcome.
This makes it possible to move beyond identifying a problem. Causal AI can trace root causes and downstream impacts, evaluate trade-offs, and simulate multiple paths forward before a decision is made.
Imagine Being Able to Test the Decision Before You Make It.
Imagine receiving a recommendation from AI and being able to review the simulation that produced it. You can see the assumptions, understand the trade-offs, and examine the expected impact across the business. Then run additional simulations yourself: change an assumption, adjust a constraint, test another option, and see how the outcome changes.
Instead of asking people to simply trust an AI-generated answer, Causal AI makes the reasoning transparent and the recommendation testable. Your team reviews the options, challenges the assumptions, explores alternatives, and makes the final decision.
Multiple Forms of Intelligence Working Together
A Causal AI solution can bring together multiple forms of intelligence:
Knowledge AI captures and applies the knowledge, judgment, business rules, and reasoning of your subject matter experts.
Data AI analyzes enterprise data, identifies patterns, and continuously evaluates changing conditions.
Causal AI reasons across cause-and-effect relationships, traces root causes and downstream impacts, simulates alternatives, and recommends a path forward.
Generative AI and LLMs provide a natural way for people to interact with that intelligence, ask questions, understand recommendations, and challenge the reasoning behind them.
Together, these capabilities can become a digital co-worker that works alongside your people. It can continuously evaluate what is happening, understand why it is happening, determine what else may be affected, simulate alternatives, and prepare recommendations with the evidence and reasoning behind them.
People remain in control. They can review the recommendation, understand the reasoning, compare alternatives, run additional simulations, adjust assumptions, and make the final decision.
From Machine Learning to Machine Teaching
Traditional Machine Learning learns patterns and correlations from historical data. Causal AI can also be taught by the people who understand the business best. Subject matter experts teach the relationships that matter, why decisions are made, what conditions change those decisions, and what outcomes need to be protected.
The knowledge and reasoning of your best people become an enterprise capability that can be preserved, scaled, and continuously applied.
Where Causal AI Creates Value
OPERATIONS
Production optimization, scheduling, root-cause analysis, energy optimization, quality, and process performance.
SUPPLY CHAIN & PROCUREMENT
Supplier monitoring, disruption analysis, sourcing decisions, capacity, material availability, and downstream impact.
KNOWLEDGE & DECISION SUPPORT
Knowledge capture, digital co-workers, expert reasoning, simulations, and complex decision support.
RISK & PERFORMANCE
Risk analysis, cause-and-effect analysis, scenario modeling, churn analysis, and other complex business decisions.
Knowing why is the first step to getting it right.
AI Workflows & Agentic AI: Putting AI to Work
Automate Work. Connect Tasks. Give People Back Time.
Not every business problem requires complex reasoning. Sometimes the opportunity is work that takes too long, requires too many manual steps, or consumes valuable employee time.
AI Workflows connect and automate repeatable steps across information, systems, and people. Agentic AI takes this further, using AI agents that can pursue goals, perform tasks, interact with systems, monitor changing conditions, and coordinate work across multiple steps.
The range of possibilities is enormous. Workflows and agents can do everything from finding and summarizing information, creating reports, processing documents, and supporting employees and customers to monitoring operations, coordinating work across systems, and carrying out complex multi-step tasks.
These solutions can use Generative AI, LLMs, traditional automation, enterprise software, and, when deeper reasoning is required, Causal AI. The technology depends on the work that needs to be done.
The opportunity isn't to automate everything. It's to automate the right work and give people more time to focus where their judgment and experience create the most value.
A Workflow Follows a Process. An Agent Pursues a Goal.