Breaking through the hype
A practical guide to building an effective AI strategy
Published on: May 28, 2024
Last update: September 23, 2024
There’s a ton of hype around AI, with widespread talk about its potential to transform every aspect of business. However, while every exec knows they need to invest in AI, most grapple with questions like, “Where do I begin?”
Ensuring that AI adds value to your organization requires a strategic approach. Like many digital transformation initiatives, this means shifting the conversation from “How soon can we start?” to “What should we consider?”
As Modus Create CEO Pat Sheridan succinctly puts it in his article on the top digital transformation trends, “As enterprise leaders, we need to figure out how AI augments and supports overall digital initiatives before blindly investing.”
With that in mind, I’m pulling back the curtain with a practical look at how to:
- analyze your organization’s readiness for AI
- identify your AI priorities
- create an action plan to guide your company through the complexities of building an effective AI adoption strategy
Assessing your AI adoption priorities
Finding a starting point involves assessing how AI fits into your existing digital strategy. In our 2023 research, 39% of enterprises cited AI as a top initiative in the coming year, more than any other initiative.
Interestingly, smaller companies cited AI as a greater focus area than their larger counterparts with 10,000+ employees, which were more heavily focused on initiatives like cloud migration, user experience, and modernizing their customer-facing products. Just 22% of companies with 10,000+ employees cited AI/RPA as a core focus.
This is likely because larger enterprises are taking a “go-slow” approach, given AI’s security risks and ethical concerns. Larry Fitzpatrick, Executive Vice President and CTO of OneMain Financial, the leading nonprime lender in the United States, provides insight into how the world’s top executives plan to leverage AI technology.
In our Conversations with Chief Innovators series, Larry explains that business leaders are concerned about AI because it “exhibits behaviors we might label as bad in humans.” As a result, he says, “We need to understand and quantify the risk profile of new solutions using generative AI.”
There’s no doubt that a comprehensive AI strategy is critical. But what does building one look like for your business?
Top considerations for developing an AI strategy
AI initiatives are not one-size-fits-all—yours should be tailored to organizational needs and strategic goals. We’ve defined the most critical considerations as you navigate AI’s complexity and start to take advantage of its capabilities.
Mitigating security and compliance risks
Managing your cybersecurity risk should come first when building your AI strategy, particularly now that regulators are taking notice. Under the EU AI Act, which was approved by the EU Council on May 21st, organizations must categorize AI systems based on risk level, from unacceptable to limited, and adhere to numerous regulatory controls.
As my colleague Bill Reyor, Modus Create Director of Security, explains in his article on AI risk, “Similar to GDPR, US-based businesses that use AI and interact with European markets or handle data of EU citizens could be in scope. Even if they aren’t, this benchmark regulation will probably be the basis for due care of AI systems going forward.”
The EU AI Act aims to protect human rights and ensure that AI applications are safe and reliable, and this is just the beginning. Last October, the White House issued Executive Order 14110 to establish new standards for AI safety and security, and earlier this year, the National Institute for Standards and Technology (NIST) announced members of the US AI Safety Institute’s (AISA) executive leadership team. The goal of AISA, according to NIST, is to “create the science, practice, and policy of AI safety and trustworthiness.”
Rather than wait until the last minute, business leaders should act now. Security is a do-not-pass-go step so your organization can effectively protect sensitive information and make security part of your culture. The risk of fines, sanctions, and reputational damage is simply too costly to ignore the issue.
Action Step: Assess your cybersecurity risk and ensure your policies are up-to-date, including proper training on security best practices for your employees.
AI initiatives are not one-size-fits-all—yours should be tailored to organizational needs and strategic goals. We’ve defined the most critical considerations as you navigate AI’s complexity and start to take advantage of its capabilities.
The importance of AI governance
When AI implementation is rushed, governance can sometimes be an afterthought. This is a huge oversight that can put you at risk. Governance is the cornerstone, setting the organizational rules and norms and aligning AI with your mission and values to realize efficiencies without overstepping boundaries.
Tech leaders like Brad Smith, Vice Chair & President of Microsoft, are weighing in on the importance of governance in AI. As Brad says, “We’re all becoming, really around the world, more focused on the guardrails that are needed, the risks that we need to address and manage well to ensure that AI stays under human control and serves people around the world.”
IBM is also taking AI governance seriously, announcing its efforts to partner with AWS to help clients responsibly scale AI.
A critical part of building your AI strategy should include developing comprehensive AI policies that help mitigate compliance risks, cover ethical considerations, and define how your organization and employees can use AI.
Action Step: Determine why you’re adopting AI and create policies around acceptable usage within your organization.
Data blindspots hinder AI performance
To get the most out of AI, it must be tuned to your specific needs and knowledge, embedded in your data. Yet, numerous companies grappling with AI strategy implementation often encounter setbacks or outright failures attributable to data-related issues. These challenges manifest in various forms, from poor data quality and reliability to inaccessible data sources, stemming from disparate systems and a lack of governance.
The numbers speak for themselves: According to a survey of 500 data professionals by the open source data quality tool Great Expectations, 91% report that poor data quality impacts their company’s performance.
When data is hard to find, access, understand, or trust, it blocks any AI initiative. On the other hand, successful AI projects are often the result of a healthy company-wide data ecosystem.
To overcome these hurdles, laying out the right data infrastructure and governance is key to unlocking AI potential.
Action Step: Catalogize data sources, assess their quality, set up a data governance framework, and ensure data is accessible to the right people.
Transforming the customer experience
In all the excitement about AI and its capabilities, it’s easy to focus on efficiency and productivity, overlooking AI’s effect on customer experience. But the most successful organizations keep customers at the center of their AI strategy.
This is important as your organization incorporates AI into digital products to create a smarter, more intuitive UX that meets customers’ expectations. By bringing AI early into the concept phase, you can ensure it’s fully integrated with your product instead of being a minimally functional afterthought.
Action Step: Speak with your data and UX team to evaluate how your customers interact with your products, which will help you understand how AI can improve their experience.
Your people are your biggest asset
As I’ve written about before, people are the most critical factor in digital transformation.
Your employees may resist AI implementation, fearing their jobs—or entire departments—will become obsolete. While a percentage of the workforce might fall to a reduction in force, the World Economic Forum forecasts the creation of 97 million jobs due to AI. AI can be a tool to increase employee productivity, cutting out tedious, repetitive tasks in favor of more strategic projects.
Larry Fitzpatrick says it best in his conversation with Pat: “Nobody…is deploying generative AI directly to customers without human oversight.”
Like with many digital transformation initiatives, the challenge is how to communicate clearly and engage employees. When building an AI strategy, it’s crucial to document the outcomes and benefits for employees. Part of this is designating a change agent — an executive leader who will execute, manage, and communicate the rollout of changes throughout your company.
Action Step: Have a change agent develop communications and messaging about AI in your organization to educate employees and set expectations.
Driving ROI with AI
ROI tracking of GenAI investments is complex, as Gartner VP Analyst Rita Sallam reminds us, writing that organizations will get the most out of the latest innovations if they “build a business case by simulating potential cost and value realization across a range of GenAI activities.”
Among the activities that Sallam identifies are:
- quick wins that focus on productivity improvements
- differentiating initiatives that provide a competitive advantage
- more transformative initiatives where “innovators may have to accept difficult-to-quantify hard financial returns and higher cost, complexity and risk in exchange for first-mover advantage.”
At Gartner’s Tech Growth & Innovation Conference, fascinating insights were shared about the IT spending breakdown and ROI expectations. For example, 67% of GMs predict they will see a slight increase in revenue by incorporating GenAI into existing solutions or creating add-ons, versus POCs and developing and selling separate GenAI solutions, which aren’t expected to yield as much return.
Ultimately, AI can drive growth, but in some cases, cost and complexity may overshadow quick returns. It’s best to determine which investments will enable you to compete and start there.
Action Step: Identify where to invest in AI and create a realistic plan for tracking success and impact within your organization.
Managing expectations for successful AI transformation
Building a successful AI strategy that positively impacts your organization hinges on setting realistic expectations. AI is a powerful tool, but it’s only a tool and one that requires thoughtful adoption and ongoing management as its capabilities evolve.
AI will impact nearly every industry. Taking the time today to create an effective AI strategy is the best way to position your company to thrive tomorrow.
