Operationalizing AI: What the C-suite needs to know

c-suite how to operationalize artificial intelligence

In my last post, I explained how recent advances in artificial intelligence (AI) have propelled us into a new techno-industrial paradigm and what this means for executives.

C-suite leaders can drive significant value for the enterprise by embedding AI into their operations and processes. Holistic AI implementation liberates their people from monotonous work, supercharging productivity, creativity, and the customer experience.

So, you’re probably wondering, how can you take advantage of AI in your organization?

For executives like yourself, AI demands careful attention. While the vast majority of AI actors will be great for your business; we shouldn't underestimate the damage only 5% of bad AI actors could do. They can be unpredictable, reputationally damaging, and frustrating to employees and customers if not done well. The potential for AI models to cause harm through disinformation is a prominent issue, even flagged by Geoffrey Hinton, a researcher instrumental to the creation of the latest LLMs. 

AI adoption can't be taken lightly. We suggest every enterprise consider AI holistically, starting with four key pillars:

  1. Business value

  2. Culture and enablement

  3. Governance and ethics

  4. Architecture and compatibility

Business value

The first—and most fundamental—question you have to answer is "What’s the business case for AI?”. Historically, AI’s net impact has been hard to quantify. However, maturity in use-cases and the expected business impact, unified behind a singular platform, makes it easier to define.

Vital to defining a compelling business case isn’t just understanding the quantum of near-, mid-, and long-term hard savings potential. You should also consider the broader benefits behind a unified platform and the impact this has on an Enterprise IT’s total cost of ownership. Executives also need to understand that in most cases with AI, an ROI profile will look exponential. Economies of scale, adoption, and use-case transferability typically accelerate benefit capture in the mid to late stages of their program.

A solid strategic AI business case aligns to the most important business challenges you face. Impact to operating margin due to spiraling inflation and geopolitical unrest; revenue velocity slow down due to a widening skills gap; increased compliance risk in accelerated regulatory environments; and the increasing expectations of customers and employees in an ever-competitive labor market. These are just some examples of value drivers peers seek to quantify in their business case.

Take Travelport’s automation program as an example. After embarking on a transformation with a case modeled on cost reduction, they quickly realized they also needed to model for the impact automation has on maximizing compliance, risk, and employee satisfaction.

Culture and enablement

When AI is deployed it has a profound impact on your company culture. To succeed, your strategy needs to be built around your people.

Remember that foundational models are just that—a foundation. Off-the-shelf and out-of-the-box models won’t automatically understand the specifics of your business such as the unique language, terminology, and customer requests. You need to fine-tune your models with that knowledge, and this can only be done by your employees—your subject matter experts.

Ensure you bring your employees on the journey. The UiPath 2023 Automation Generation Report found that “44% of global employees say they want to contribute to the creation of AI-powered automations in their workplace.” And 57% of respondents reported “that they view employers that use AI-powered automation to help support employees and modernize operations more favorably than those that do not.”

Enterprises need strong change management to drive AI adoption and minimize resistance. More specifically, keep a human in the loop and give them the tools to empower them to train and refine AI models correctly. Doing so is a fundamental pre-requisite in fostering the right culture and starting point for AI.

The automation journeys of USI and Hub International provide a good example of AI adoption done right. Though even they are just getting started with AI. By aligning with the needs of their people, the insurance services providers were able to successfully establish, and sustainably scale, their AI-powered automation programs. Beginning with simple automations of common frustrations and evolving into complex process automations enabled by AI.

Governance and ethics

Open-source algorithms are the easiest way to start implementing AI for most organizations. However, disparate AI systems (especially if they’re open source) can be hard to manage and govern across an enterprise. Consider that the regulatory environment around AI is heating up globally, with industry experts (including Sam Altman, founder of OpenAI) calling for a new system of regulation by governments globally to deal with AI. It's prudent to deliver strong levels of accuracy, consistency, fairness, and data security from the get-go.

AI adoption in the enterprise demands accountability and control. Its application, performance, and output must be closely monitored and improved. To do this, you’ll benefit from a platform-based approach.

Consider a platform like a safety blanket, wrapping around your enterprise to provide access to AI in a controlled way. In a comprehensive article by McKinsey and Company on scaling data analytics and machine learning (ML), platforms and systems “able to move data from one classification to another as their importance fluctuates” are key to fast-changing data strategies and provide strong governance.

A platform delivers built-in tools for model versioning, manages confidence thresholds across all model predictions, and qualifies each output in line with core value drivers and risk guidelines. It ensures users can interrogate the training data and training process for bias. 

With a platform-based approach, your enterprise can scale AI with sufficient transparency, proactive risk management, and ethics as a core business strength.

Architecture and compatibility

An AI platform doesn’t exist in isolation. You must consider the wider technology architecture of your business—in particular, data. Does the solution integrate with other automation and analysis tools to leverage the best and untapped data sources? Is it compatible with your business and IT systems? Can it manage the full data lifecycle? And can you ensure data quality?

You can never guarantee your models are always trained on accurate data. You need a human in the loop and data quality transformation algorithms to protect end users from bad decisions and misinformation. There’s an advantage in having strong data connections—whether they are prebuilt connectors, tools to easily build custom APIs, or user interface (UI) integrations for your enterprise systems.

For example, public sector organizations are a complex patchwork of enterprise systems. The architecture can be brittle, and it’s difficult to introduce a new system when there are so many existing dependencies to consider. However, Summit2Sea Consulting was able to overcome these challenges. They helped a client increase their document processing success rate from 54% to 99.5% by integrating AI-powered Document Understanding with the customer’s financial systems. But there is still so much further they can go with AI—like embedding generative AI to accelerate key processes. The possibilities are endless!

What should executives do right now?

If you don’t already have an AI strategy for your business, make one now. Deploying AI tactically here and there isn’t enough. You need a plan to make AI core to all business operations. And this shouldn't be thought of as an ‘AI’ or even an ‘IT’ strategy. It needs to be a complete enterprise strategy. What’s more, you can’t overlook the power of automation to turn AI understanding into action and business value.

Here's my challenge to you: engage your leadership teams and work with them to create a business transformation plan for AI-powered automation. Put general-purpose AI at the center of the strategy and think about how your people can use automation to realize AI’s potential, mining and delivering data to actuate the outputs AI could generate. 

Think also about the guardrails and change management needed before you unleash AI on your enterprise. Consider the value AI can bring and set goals and targets to achieve. Think about what you need from your partners to architect your strategic foundation for AI—across strategy, structure, people, process, and, most importantly, technology.

Finally, I'd like to end on a famous quote by Edwin Louis Cole: “There are dreamers and there are planners; the planners make their dreams come true.” 

Let’s go!

For more insight on how executives can safely and successfully embed AI across operations, watch our on-demand session recordings from UiPath AI Summit.

Cam Lau UiPath
Cam Lau

Senior Director, Strategic Engagements and Transformation, UiPath

Get articles from automation experts in your inbox

Get articles from automation experts in your inbox

Sign up today and we'll email you the newest articles every week.

Thank you for subscribing!

Thank you for subscribing! Each week, we'll send the best automation blog posts straight to your inbox.