How AI is powering operational excellence in 2025 and beyond

Enterprise operations in the ai era

Enterprise operations have become significantly more challenging in the last 10 years due to the rapid adoption of artificial intelligence (AI) technologies. The means for creating operational excellence is being radically reshaped. It’s no longer just about incremental improvements within a single facility or region; it’s about embracing transformative technologies that fundamentally reshape how we work on a global scale. While AI stands at the forefront, as the enabler of this transformation, implementing AI in the right ways remains a distinctly human challenge.

The rise of AI: from the sidelines to center stage

In 2024, analysts wisely advised businesses to thoroughly assess AI applications before broad implementation. However, our recent global survey of over 400 Fortune 1000 executives, operational leaders, and IT professionals reveals a seismic shift: 66% of organizations now consider AI central or supplementary to their overall strategy. This isn't a gradual uptake; it's a full-fledged embrace, a significant leap from the 35% reported in our 2021 survey with ABI Research.

So, what's driving this rapid adoption? The answer lies in AI's ability to deliver tangible results across key operational areas.

AI: the new engine of operational excellence

AI has transitioned from an experimental tool to a core enabler for data-driven decision-making, product innovation and operational efficiency. Here's how:

1. Boosting operational efficiency

AI is streamlining processes, automating repetitive tasks, and optimizing resource allocation. According to McKinsey & Company, AI can reduce greenhouse gas emissions by up to 10% across sectors by optimizing energy use, improving logistics, and enabling predictive maintenance. Sixty-two percent (62%) of respondents to our CGS Immersive survey are already leveraging AI for operational efficiency. Think about predictive maintenance in manufacturing, intelligent routing in logistics, and automated customer service interactions. 

Use case for operational managers:Imagine starting your day with an AI-powered dashboard that highlights potential equipment failures before they happen, allowing you to proactively schedule maintenance and minimize downtime. This is the power AI brings to daily operations.

2. Empowering data-driven decisions

AI algorithms can analyze vast datasets to uncover hidden patterns, predict future trends, and provide actionable insights. This empowers leaders to make informed decisions based on evidence, not just intuition.

Use case for operational managers: AI provides real-time visibility into key performance indicators (KPIs), allowing you to quickly identify bottlenecks and optimize resource allocation based on actual performance data, not just gut feelings. Fifty-five percent (55%) of respondents are leveraging AI for data-driven decision-making.

3. Fueling product innovation:

AI is accelerating the product development lifecycle, enabling personalized product recommendations, and driving the creation of entirely new product categories

Use case for operational managers: COOs and other operational managers must restructure development processes to leverage AI tools, potentially leading to significant cost savings and improved resource allocation.

The next 2-5 years: a glimpse into the future

Looking ahead, the next 2-5 years will see AI become even more deeply embedded in operational processes. In fact, according to our report, use of immersive technologies will rise: 84% anticipate AR/VR/XR as crucial by 2025. Survey respondents recognize the transformative potential of these technologies across various business functions.

In addition, sustainability remains a pressing issue for operational leaders. By 2025, 50% of CIOs will have performance metrics tied to the sustainability of the IT organization. This means operational managers will need to factor sustainability into every decision, leveraging AI to optimize

Navigating the challenges: a word of caution

While the potential of AI is undeniable, it's crucial to acknowledge the challenges.

• Data quality: AI models are only as good as the data they're trained on. Poor data quality can lead to inaccurate predictions and flawed decisions. Fifty-three percent (53%) of organizations emphasize improved data infrastructure as a key enabler.
• Legacy system integration: Integrating AI with existing legacy systems can be complex and costly.
• Resistance to change: Employees may resist adopting AI-powered tools if they fear job displacement or lack the necessary skills.
• Ethical concerns: For 28% of our survey respondents, ethical considerations like fairness, transparency, and accountability, are top of mind.

Key takeaway for operational managers: Addressing these challenges requires a proactive approach. Invest in data governance initiatives, prioritize integration projects, and develop comprehensive training programs to upskill your workforce.

Addressing the skills gap and the human element in AI adoption

One of the most significant challenges in AI adoption is the widening skills gap. Organizations find it challenging to keep their workforce up to date with the latest AI advancements while also managing day-to-day operations. To address this:

• Prioritize training and cross-functional collaboration to overcome adoption barriers.
• Invest in upskilling employees and empower them with the knowledge and tools necessary to integrate AI seamlessly into their day-to-day activities. 
• Foster a culture of AI fluency to enable employees to collaborate more effectively with AI tools, driving enhanced business value.

Reminder: While AI is transforming operations, it's crucial to remember that it's meant to augment human capabilities, not replace them entirely. A recent Gartner report notes that 92% of employees say they want AI to help them with administrative tasks and to summarize information on a particular topic.1 This highlights the importance of focusing on how AI can empower employees to be more productive and efficient in their roles.

You need a consultative approach to AI implementation.

Given the complexity of AI adoption, many organizations benefit from a consultative approach. Companies like CGS Immersive offer comprehensive engagement models that combine AI expertise with deep business transformation acumen to help enterprises move beyond experimentation and achieve production-ready generative AI solutions that deliver measurable outcomes.

Ready to unlock the full potential of AI for your operational excellence initiatives?

Below are pragmatic steps you can take, and CGS Immersive is ready to help:

• Build foundational capabilities: focus on the core elements that will underpin all your AI efforts.
• Embrace agility and iteration: shift to shorter planning cycles and flexible approaches.
• Continuously monitor the AI landscape: stay informed about the latest trends and breakthroughs.
• Plan for different scenarios: anticipate potential challenges and opportunities.
• Forge strategic partnerships: collaborate with experts to accelerate your AI journey.

Ready to dive deeper?

For a detailed roadmap and specific recommendations, download our full report.