It’s Not Too Late to Leverage AI, but You Must Get Started Today

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Read more about author Brett Hansen.

AI’s potential enchants experts across all industries. For customer support specialists, generative AI solutions have improved productivity by up to 35%. For software developers, AI can handle mundane tasks like repetitive coding and automated deployment, enabling engineers to focus on essential quality-of-code updates. For transportation services, AI-backed predictive analytics can re-route based on traveler demand, increasing an organization’s resource allocation skills.

Use cases continue ad nauseam.

With so many examples of successful AI implementation, some leaders worry that they’ve already missed the boat on AI and machine learning (ML) deployment. I’m here to disabuse you of this misguided belief – in fact, now is the perfect time to start planning and implementing AI for the enterprise.

Leaders Have Time to Get Started on AI

Contrary to popular belief, only 35% of organizations have started piloting AI use cases, with 42% currently reviewing their AI options, according to Altair. So, there’s still time to implement AI in a meaningful way. But time is dwindling: More than half of organizations (59%) are keen to implement AI for large-scale projects over the next 12 months.

Why wait a whole year? Because AI planning, implementation, and maturation are all distinctive – but equally lengthy – processes. Leaders rushing into deployment may alienate their workforce or invite incorrect AI outputs.

According to industry research, only 14% of frontline employees working at AI-enabled organizations believe they’ve received adequate training. Perhaps even more concerning, 63% of adopters cite content inaccuracies as a major challenge when co-working with AI – yet they continue to use these tools. Continued reliance on a predictably inaccurate AI integration increases the likelihood of errors, decreasing the tool’s value and potentially damaging brand reputation.

Leaders can avoid these troubling AI side effects by adopting a well-prepared and thorough deployment strategy today.

It’s a Marathon, Not a Race

Leaders who’ve yet to implement AI and ML should take time in the new year to strategize about AI’s applicability, educate their workforce and prepare organizational data.

  • Strategize: Before rushing into deployment, leaders must comprehend how AI will benefit their organization. Start this process by identifying your organization’s strengths and weaknesses, then strategize relevant AI solutions. For example, if your operating costs cut into margins, adopting analytics solutions offering efficiency insights may be advantageous.

    Take this time to also consider the risks associated with AI adoption, including inaccuracy, cybersecurity, intellectual property infringement, regulatory compliance, and explainability. According to McKinsey, only 16.5% of organizations are actively working to mitigate risks and challenges associated with AI – a significant misstep leaving organizations open to regulatory fines. It’s important to engage stakeholders during this phase to include diverse perspectives from all departments. Doing so ensures that all relevant employees understand the far-reaching implications of AI use.

    Finally, develop an AI roadmap. Communicate timeline expectations to employees during this stage – and include education as one of many steps on your roadmap to AI success.

  • Educate: Employees who understand AI’s utility are more likely to embrace these tools, leading to smoother integrations and better results. Furthermore, employees must understand how to use – and not use – AI. Otherwise, they may run afoul of regulations and compliance requirements.

    It’s also essential to educate employees about the importance of AI re-skilling. Experts predict that generative AI will absorb 30% of human work hours by 2030. That’s a lot of new time to account for. To remain productive, employees must acquire new skills and adopt innovative workflows that allow a deeper breadth and higher quality of outcomes.

    Before implementing AI, leaders must offer tailored training programs with insights specifically designed for different roles. Additionally, they should promote a culture of continuous learning to ensure employees remain optimistic about their AI co-workers, not cautious.

  • Prepare: AI requires high-quality data to run efficiently and provide correct outputs. Generative AI tools generate solutions at an unprecedented rate, but flawed system logic can lead to gross inaccuracies. And if leaders base organizational decisions on those inaccuracies, important KPIs like revenue and trust may suffer.

    To combat this possibility, leaders must prioritize proper data management, including appropriate storage, synthesis, and analysis protocols. Start by establishing clear data policies and defining how data should be collected, stored, and used. Consider eradicating dark data that may contribute to organizational overload or unnecessary costs. Foster a data-centric culture that encourages employees to understand the importance of data and its role in AI’s effectiveness.

    Perhaps most importantly, leaders should consider investing in improved data infrastructure, such as a master data management (MDM) solution. These systems provide a cohesive platform in which to manage large datasets more efficiently. When one central repository stores and analyzes all data, AI adoption becomes much easier – and data-backed decision-making becomes the norm.

AI Will Accelerate to New Heights in 2024

It’s impossible to avoid the hype about generative AI and large language model (LLM) solutions. However, rather than rushing into AI adoption, wise leaders will lay the proper groundwork first. This process must include identifying initial use cases, mitigating risks, communicating timelines and expectations, providing tailored training programs, promoting continuous learning, implementing data management best practices, and investing in data infrastructure.

Organizations failing to plan adequately risk inaccurate outputs, workforce alienation, compliance issues, and missed market opportunities. However, those who approach AI methodically will be poised to unlock productivity gains, cost savings, enhanced offerings, sharper decision-making, and lasting competitive advantages.

The runway is still long enough, but the time for thoughtful AI preparation is now.