"Prepare quarterly review." These three words once triggered a cascade of manual tasks - data gathering, analysis, presentation creation, and stakeholder coordination. Today, these exact three words can set in motion an AI system that can handle all those tasks AND identify trends, suggest strategic talking points, and proactively prepare for potential challenges. This isn't science fiction - it's already transforming how enterprises work.

In five years, will your organization still be giving step-by-step instructions to AI, or will you be deploying agentic AI copilots that understand your business objectives and autonomously drive results? The difference will separate market leaders from followers.

Understanding how agentic AI transforms enterprise operations is the first step.

From imperative to declarative instructions

The leap from traditional software to agentic AI represents one of the most significant shifts in enterprise technology. To understand its impact, we must grasp a fundamental change in how these systems operate.

Traditional software follows "imperative" instructions – compare this to a detailed recipe where every step must be precisely specified and followed in exact order. If you've ever worked with conventional business software, you've experienced this: click here, enter data there, and follow this exact sequence to generate that report.

But agentic AI operates on a completely different paradigm. Agentic systems use a "declarative" approach, where you state what you want to achieve, and the system determines how to get there. Instead of programming every possible scenario or maintaining complex decision trees, these systems interpret objectives and autonomously plan their approach. It's the difference between giving turn-by-turn directions and simply saying, "Get me to the airport on time" to someone who understands traffic patterns, construction delays, and alternative routes.

This shift will transform how work gets done in enterprise environments. Traditional systems require explicit programming for every scenario they might encounter – if customer satisfaction drops below 4.5, send this alert, generate that report, and schedule these reviews.

Agentic systems, however, can be given broad objectives like "monitor customer satisfaction" or "maintain brand consistency across regions" and independently determine the necessary steps, tools, and approaches to achieve these goals.

The agentic AI difference in action

Use case 1: Brand management for marketers

Consider how marketing teams currently launch regional campaigns. With traditional AI capabilities, teams must follow a rigid, step-by-step process using traditional software. This involves sequential tasks like accessing the brand portal, searching for approved assets, reviewing regional guidelines, and ensuring compliance - with human supervision required at each stage.

In contrast, with agentic AI, a marketing manager can simply state, "I need assets for our Canadian summer campaign." The system then autonomously accesses relevant systems, analyzes performance data, and decides which assets and strategies work best. More importantly, it anticipates potential needs and proactively suggests solutions – like connecting with regional experts or scheduling review meetings based on past successful patterns.

Use case 2: Account management transformation

Preparing for a critical client review traditionally involves hours of manual data gathering, analysis, and presentation preparation. With agentic AI, an account director can simply request, "Prepare for Acme Corp's quarterly review."

The agentic copilot immediately:

• Synthesizes multiple data streams from its knowledge base
• Identifies critical patterns in service metrics and client engagement
• Proposes strategic solutions based on successful past interventions
• Identifies ideal executive sponsors based on similar case success
• Prepares briefing documents proactively
• Suggests preventive measures for potential issues

This level of autonomous analysis and strategic planning transforms the account manager's role from data gatherer to strategic advisor, armed with AI-driven insights and recommendations.

Use case 3: Customer service augmentation

Customer service agents often juggle multiple screens and databases to find the right telecom plan for each customer, often leading to long wait times and suboptimal recommendations. With agentic AI, an agent can simply say, "Find the best telecom plans for a healthcare worker."

The agentic copilot immediately:

• Analyzes the customer's usage patterns, job requirements, and location data
• Compares these factors against all available plans and promotions
• Identifies plans with features most beneficial to healthcare workers (e.g., priority network access, enhanced coverage in medical facilities)
• Calculates potential cost savings based on the customer's current plan
• Prepares a concise summary of the recommended plan with key benefits highlighted

Instead of searching through databases, human agents can focus on what they do best - helping customers.

Watch Fuel iX Copilots agentic AI in action below:

The business value of agentic AI for the enterprise

The potential for agentic AI in the enterprise is significant. According to recent projections from Gartner, by 2028, 33% of enterprise software applications will include agentic AI, a dramatic increase from less than 1% in 2024. They also forecast that AI agents will handle 20% of digital storefront interactions by 2028. Additionally, Gartner expects that agentic AI will autonomously make 15% of daily work decisions by 2028, compared to none today.

The shift from imperative to declarative systems like agentic AI delivers measurable benefits across multiple dimensions:

1. Strategic impact - Focus shifts from task completion to driving business outcomes.
2. Time efficiency - Tasks that once took days can often be completed in hours, with higher quality outcomes.
3. Resource optimization - AI-driven quality assurance of business processes reduces the need for multiple human reviews and approvals.
4. Risk management - Instead of waiting for problems to occur and then fixing them (reactive approach), AI systems can analyze patterns and identify potential issues before they happen (proactive approach).

The impact of agentic AI on enterprise productivity

Companies using agentic AI are seeing significant improvements in how they work. TELUS's enterprise implementation provides compelling evidence of this potential: IT-support queries, now handled by Fuel iX Copilots, are resolved 75% faster than before. Even more dramatically, their AI-powered project planning assistant has reduced timeline calculation times from three hours to just 90 seconds – a 97% improvement in efficiency.

These results align with broader trends, as companies adopting agentic AI systems consistently report:

• Significant reductions in time spent on routine tasks
• Improved campaign performance and customer engagement
• Decreased error rates and compliance issues
• Enhanced ability to scale operations without proportional increases in headcount
• More proactive problem-solving and risk mitigation

Rather than simply automating old processes, these agentic AI systems help companies find smarter ways to work. This transformative potential explains why Deloitte predicts that 25% of enterprises using generative AI will deploy AI agents in 2025, growing to 50% by 2027.

The strategic advantage of adopting agentic AI

Organizations deploying agentic AI will gain decisive advantages over competitors still bound to traditional, instruction-dependent systems.

Recent research by METR underscores the urgency of this shift. They found that the length of tasks AI agents can complete is doubling approximately every 7 months, a trend that has held consistent over the past 6 years. This "Moore's Law" for AI agents signals a rapidly approaching future where AI systems can handle increasingly complex and lengthy tasks with minimal human intervention.

Enterprise leaders need to ask themselves if their current AI solutions are just following instructions or actively driving better business outcomes. The strategic advantage lies in embracing truly agentic systems that can grow with these advancements, continuously expanding their ability to handle more complex, nuanced, and lengthy business processes.

Fuel iX Copilots are natively agentic

Fuel iX Copilots provides enterprises with a unified, intelligent workspace where agentic AI transforms how employees interact with company knowledge and systems. Unlike traditional AI copilots that simply respond to queries, these copilots act as partners that understand context, anticipate needs, and take initiative across your organization's digital landscape.

At its core, Fuel iX Copilots serves as a central nervous system for your enterprise, connecting and activating information across your digital ecosystem. This native agentic architecture means they grasp the intent behind requests, combine insights from multiple sources, and autonomously execute complex workflows.

Hear more from Bret Kinsella, SVP TELUS Digital, GM of Fuel iX, at Gartner's Tech Growth and Innovation Conference as he discusses how AI agents impact business processes and why companies must implement them today.

Enterprise-wide impact with Fuel iX Copilots agentic intelligence

Fuel iX Copilots can be customized for different roles while maintaining consistent enterprise knowledge and compliance standards. Marketing teams gain deep campaign insights, finance departments uncover trends, and product teams synthesize market feedback through a unified, intelligent interface that anticipates needs and suggests relevant actions.

The future of work is agentic

As enterprises continue their AI transformation journey, the distinction between basic AI tools and agentic systems becomes crucial. Fuel iX Copilots represents the next evolution in enterprise AI – not just assistants that follow instructions but partners that understand objectives and autonomously work to achieve them.

Contact us to explore Fuel iX Copilots' agentic capabilities.