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5 Steps for Aligning AI/ML With Business Outcomes

Michael Simms
Dec 6, 2024
Michael Simms
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5 Steps for Aligning AI/ML With Business Outcomes

 

More than 80% of business leaders say they have less than 18 months to execute an AI strategy before their organization experiences negative consequences.

However, implementing AI simply for the sake of it can lead to disappointing results. Research suggests that over 80% of AI projects have failed or will fail soon, and 35% of business leaders are concerned that their team isn’t technically skilled enough to work with AI. With all this uncertainty, how can business leaders maximize their steep AI investments?

First, businesses must define their desired AI outcomes and create metrics that reflect those goals. Beyond that, successful AI implementation hinges on a commitment to change management and reliance on knowledgeable change agents, be they third parties like consultants or internal stakeholders.

Let’s explore the critical steps needed to ensure your AI initiatives succeed — from involving cross-functional teams to fostering digital transformation and refining your data processes — all while ensuring a sustainable and scalable approach to AI integration.

 

1. Outline your desired business outcomes

Many organizations dive headfirst into AI without first considering the business problem they’re attempting to solve. This can lead to expensive projects that fail to deliver meaningful results. The key to success is to ask, “What are we trying to achieve with AI, and how is AI better suited to accomplish those goals than any other technology?”

For instance, if your goal is to reduce maintenance costs, business users can leverage AI for predictive maintenance by analyzing equipment performance and predicting failures before they occur. AI is a powerful option for this use case because machine learning (ML) algorithms enable systems to evolve over time, becoming smarter and more in tune with signs of failure. This use case is therefore a strong contender for AI adoption. 

You may find that other, less complex tasks might benefit from comparably simple, automated tools. Identifying this fact early — before you’ve started integrating AI — benefits your bottom line and company progress. Just remember that your desired outcome is business value, not AI adoption.

 

2. Engage cross-functional teams

You’ll need more than just data scientists and IT specialists to effectively implement AI. Engaging a cross-functional team, including shop floor workers and department heads, is essential. These individuals likely have a more hands-on understanding of the challenges and inefficiencies that AI aims to solve.

For example, if you’re using AI to optimize supply chain management, involve warehouse workers and logistics personnel in the conversation. They understand the day-to-day challenges of these functions, and their input can lead to more practical and effective solutions.

By inviting diverse perspectives into the fold early on, you accomplish a few goals at once:

  • Buy-in with all eventual stakeholders
  • A sense of ownership across the organization
  • More informed project expectations

Ultimately, these benefits will reduce resistance to AI adoption. Additionally, they’ll inform your AI strategy from the outset, enabling you to set more realistic KPIs.


3. Develop a realistic change management program

AI may feel like a direct threat to the expertise and livelihood of your employees, especially if they’ve spent years learning and growing in their roles. In fact, fears about AI and job replacement are growing yearly. In 2023, 52% of Americans were more concerned than excited about AI (compared to 38% in 2022). Among your team, these concerns can lead to significant resistance and dissatisfaction. 

The key to overcoming these challenges is transparency. Clearly explain the goals of your AI project and how it will enhance — rather than replace — employees’ roles. It may help to discuss your organization’s overall roadmap, demonstrating how employees’ current roles will look different in 5-10 years.

Train employees on how to use the new technology, and show them how it will make their jobs easier. Establishing open channels for feedback and questions throughout the implementation process also helps reduce fear and build trust.

 

4. Ensure your data and processes are ready for AI

One of the biggest obstacles to successful AI implementation is poor data quality and immature business processes. Organizations often expect AI systems to operate on incomplete, outdated and siloed data, which leads to suboptimal performance.

Therefore, before diving into AI, it’s crucial to assess the maturity of your data. Is it clean, consistent and up-to-date? Are there clear processes for managing and updating the data over time? Establishing strong governance is also critical. To maintain the long-term value of your AI implementation, you’ll need protocols for data access, sharing and security. 

Furthermore, you must ensure that all employees are extensively trained on proper data-sharing protocols when interacting with AI. If your AI systems don't safeguard all inputs, disclosing confidential client information or sensitive data can result in breaches of contract and other serious liabilities. 

Bottom line: Verify and invest in the quality of your data before implementing AI. Perhaps the most frequently skipped step on this list, to the detriment of many organizations, is this one.

 

5. Leverage existing expertise

Businesses have many tools at their disposal to support AI and ML initiatives. For example, with the proper training, many employees can pivot into new roles that involve managing or enhancing AI systems. A seasoned warehouse manager could transition into a data engineer role, helping to ensure the AI systems are aligned with real-world business needs.

By tapping into existing expertise, you can build a more agile, AI-ready workforce. And that’s a critical part of the adoption process. In fact, the MIT Sloan School of Management deems worker buy-in and feedback as the top secret to successful AI implementation.

Furthermore, you don’t need to consider AI adoption a stand-alone effort. External third parties, including a global consultancy like Columbus Global, are extremely well-qualified to guide you and other senior leaders down the path of AI adoption.

Ultimately, make sure you choose a partner with experience in a wide range of technologies, not just AI. This approach ensures that your tech strategy is truly fit for purpose, leveraging AI only when it’s the most optimal choice. After all, AI is just one tool in the toolkit. It’s also crucial that the partner you choose has a deep understanding of both technology and the business processes of your specific industry.

 

Where will 2025 take AI?

Most businesses are currently nestled in what Gartner calls the peak of inflated expectations. This period is rife with misconceptions about AI’s utility — namely, the mistaken notion that AI will deliver quick, dramatic results regardless of the quality of adoption. To avoid this untruth, leaders must identify realistic AI use cases that deliver tangible value.

AI isn’t a silver bullet. When properly applied, it’s a tool that can significantly improve specific aspects of your business. The secret is defining what “proper application” looks like for your organization. In 2025, we’ll see many organizations identify this secret while others continue down the peak of inflated expectations — and straight into the trough of disillusionment
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Michael Simms

Michael Simms

Michael Simms is the Practice Director for Data & Analytics at Columbus, a global IT services and consulting corporation offering a comprehensive solution portfolio with deep industry knowledge, extensive technology expertise and profound customer insight. Michael has been in the Data & Analytics space for nearly three decades and has worked on data specific initiatives on dozens of successful projects for companies in a multitude of industries.

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