The data & AI advantage

Build the foundation that powers performance and growth

Most companies are sitting on a gold mine of data — insights on operational activity, client behavior, market performance, employee productivity and much more. But having a lot of data isn't the same as having useful information and intelligence to support critical decision making. Many companies fall short of using their data to its maximum potential, a stark reality confirmed by our 2023 Innovation Index.

Driving actionable insights and empowering rapid end-to-end process automation and intelligence — in particular GenAI — across your organization is dependent on trusted and accessible information. Organizations who commit to powering decisions and automation by a single source of truth, will be well positioned to operate efficiently, innovate boldly and gain a clear competitive advantage.

The value of being data-driven:

Make quick, confident decisions across the organization.
Create operational efficiencies while reducing errors and costs.
Harness the possibilities of unbiased, generative AI.

Build a Data Foundation
Understand the Data-AI Connection
Simplify AI Complexity

Build a data foundation to unleash the power of data

Organizations need trusted information to better navigate uncertainty, improve operational performance and enable valuable experiences for customers, employees, and partners. However, before you can power your organization’s value chain using analytics, automation and artificial intelligence a solid data foundation needs to be built by:

Accelerating business insights for decision making by ensuring that data can be analyzed at all points of the enterprise to enable data-centric decisions
Syndicating data throughout the enterprise by harnessing the latest modern data management technologies to drive value
Leveraging governance to enable data confidence, making sure all corners of the organization trust the data that’s accessible and available
Optimizing actions with a human-machine partnership that balances human and technology resources to create enterprise efficiencies and reduce errors and costs
Achieving data excellence through culture and operations by prioritizing data assets so teams can understand and harness the value of that data to drive culture change

The executive’s guide to unleashing the power of data

Download the Executive’s Guide to Unleash the Power of Data to help you understand where you are on your data journey and if you’re ready for automation or GenAI. Connect your data and technology for insightful decision making.

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Understand the data-AI connection before you invest

Having trusted and accessible information across your organization provides the necessary foundation for moving up the analytics and AI maturity curve – what we call the Enterprise AI Continuum. Artificial Intelligence includes many things from automation to machine learning to large language models. Understanding the capabilities and optimal uses of each can help organizations pursue specific business goals more effectively and optimize return on technology investment.

Enterprise AI Continuum

Automation & Analytics

This is the beginning of the continuum of AI. These include several capabilities like dashboards and data visualizations, advanced or interactive reporting, and robotic process automation. There's a lot that can be learned and unlocked from automation and analytics — especially when you align it to your organizational decision-making needs.
Data Science

At its heart, data science is math. These math models help decompose and solve business problems using common math tools, formulas and algorithms, to predict behaviors. These can be market behaviors in the form of demand forecasting to reduce risk and maximize opportunities or analyzing a company’s website and in its retail stores to have the right product in stock and offer customized promotions. It can also be the behavior of assets in a manufacturing plant, predicting equipment failures and maintenance needs to reduce service downtime and operational costs.
Machine Learning (ML)
ML is also based on a set of math problems. A lot of the times they answer the same business questions but the big difference between data science and ML is that the data science approach is more transparent. The downside however is that it requires more handholding — supervised learning as opposed to unsupervised learning.
Artificial Intelligence (GenAI)

GenAI is what’s dominating the AI conversation right now, hitting the market in tools like ChatGPT, Bard, Midjourney and many others. What differentiates GenAI from the AI that has come before it is that it creates new content. Primarily because it’s run by specific models that are trained on incredibly massive data sets. It’s generating new content by reconstructing existing information or images that it has already seen before.
Artificial General Intelligence (AGI)*

It’s exactly what you think of in sci-fi/dystopian future movies, where the AI can think and reason like humans (and in film, usually does take over the world). But you’ll notice there's an asterisk on AGI in the graphics because, luckily, this technology doesn't exist yet. So it’s one less thing you need a rapid deployment strategy for this year!

How can leaders zero in on the right AI investments for their organizations?

Hear from Wendy Collins, chief AI officer, on how to identify AI investments that can deliver real business value to your organization.

Simplify AI Complexity to Generate Value

Organizations want to jump right into AI. But artificial intelligence initiatives can be challenging given the market hype about GenAI and the speed of technology innovation. The stakes are high, and the pressure is on to launch and/or accelerate AI initiatives that generate value. The key is simplifying AI’s complexity by focusing on three fundamental success factors:

AI strategy. Align your AI strategy with your business strategy. If you are talking to investors or shareholders about increasing profitability or white space business model opportunities, then that's what your AI strategy needs to focus on as well.
Business use-cases. Focus on identifying high-value business use cases and creating accelerated pathways for moving from POCs to real-world solutions that increase measurable value.
Data is the foundation. Make data strategy the bedrock of your AI initiative. Because without a coherent data foundation in place, you won’t be able to bring even your highest-value use cases to fruition.