Cutting Costs, Driving Progress: Evolving Your Organization With Agentic AI

Forbes

5 days ago

President of TELUS Digital Solutions at TELUS Digital, a company dedicated to crafting unique and enduring digital customer experiences.
Artificial intelligence (AI) is rapidly moving past generative AI (GenAI) tools like ChatGPT, DALL-E and Gemini toward genuine autonomy. Google, OpenAI and Nvidia are among major technology companies that have recently announced expansions of agentic AI capability. These moves are prompting enterprise leaders across all sectors to consider—and prepare for—a future with agentic AI.
By definition, agentic AI has agency or autonomy to make decisions, plan and execute complex, end-to-end tasks. It takes the gains of GenAI, which generates human-like content, into new territory. By automating process optimization and resource allocation, agentic AI's autonomous decision-making capabilities present significant opportunities for businesses to reduce costs through efficiency gains.
Future-forward brands looking to harness the true power of AI to achieve business goals and put themselves on the leading edge are already taking note:
• A Statista survey commissioned by my firm, TELUS Digital, found more than a third (36%) of businesses plan to allocate more than $4 million apiece to GenAI initiatives in 2025.
• Deloitte predicts that 25% of companies currently using GenAI will launch agentic AI pilots or proofs of concept this year, growing to 50% by 2027.
• KPMG's survey of 252 business leaders found more than half (57%) plan to invest in or adopt agentic AI in the next six months and a third (34%) plan to within the next 12 months.
Even if a brand isn't equipped to take this next step, those looking to remain competitive in the long term should be readying their organizations to meet this transformative moment by laying the groundwork now.
The AI Ecosystem: Combining GenAI And Agentic Capabilities
GenAI assistants and similar foundational applications have allowed enterprises to create content and software at scale—as the name implies, these tools are focused on generation, namely through creating text, images and code. We've also seen GenAI streamline work processes, boost productivity, enhance search tools across large, complex data repositories and augment multimodal voice AI applications.
Many of the models behind these advancements are essentially supercharged chat interfaces that rely on human prompts, instructions and back-and-forth conversations. A core difference of agentic AI is that humans set the goals and the AI assistant decides how to approach the task.
In the finance industry, for example:
• A GenAI assistant may respond to a customer's or agent's request to change the payment due date on a credit card with step-by-step instructions and a link.
• An agentic AI assistant, however, could autonomously analyze a customer's financial profile (spending patterns and account history) and identify and enact cross-selling opportunities. It could proactively initiate a personalized offer, such as a travel rewards credit card with no foreign transaction fees based on observed international spending patterns, complete with pre-calculated savings and a pre-approval application. This autonomous approach could significantly reduce customer acquisition costs while increasing conversion rates through more efficient, targeted engagement.
Taking The Next Steps With Agentic AI
As enterprises look to expand their AI capabilities from GenAI to include agentic AI, leaders must assess their readiness in terms of both organizational structure and technological infrastructure.
Preparing your organization for agentic AI requires both cultural transformation and structured implementation planning. Success depends on fostering a culture of continuous learning, where employees develop new technical and soft skills through accessible training opportunities.
Building on this foundation, organizational readiness may include:
• Cross-departmental alignment: Establish clear goals and success metrics for your agentic AI strategy across all departments, ensuring measurements reflect both departmental needs and broader organizational objectives.
• Strategic prototyping: Identify an initial agentic AI use case with clean, accessible data for proof of concept. Define clear boundaries and limitations to manage expectations and maintain focused development efforts.
• Prioritizing use cases: Create an inventory of potential use cases across departments based on business impact, technical feasibility and implementation complexity to develop a phased implementation road map for scaling agentic AI across the organization.
• Documenting and assessing workflows: Nothing is more counterproductive than automating a poor workflow, so use this time to document and reassess the workflows themselves.
• Establishing governance frameworks: Implement clear ethical guidelines and oversight mechanisms, including a human-in-the-loop approach and regular audits, to ensure agentic AI aligns with organizational values throughout its deployment and scaling.
Assessing your organization's AI capabilities is another key stage for agentic AI readiness. At TELUS Digital, we use the AI maturity curve as a framework to understand how advanced or integrated the use of AI is, from early experimentation to full-scale strategic development. Organizations should aim for a point on the curve where AI is being deployed across business functions before proceeding.
To assess your technological readiness:
1. List every AI or machine learning project underway—even those still in the early concepting stages—and the data that flows into these systems.
2. Review your tech stack, data integration systems and processing capabilities to ensure they can support agentic AI requirements.
3. Assess your data governance frameworks and quality control processes to confirm they can support autonomous decision-making.
4. Identify and prioritize gaps in your current technical infrastructure that could impact agentic AI implementation, focusing on your highest-priority use cases.
This dual focus on organizational and technological readiness ensures a strategic approach to agentic AI adoption that balances innovation with practical implementation.
Understand The Future To Prepare For It
As enterprises explore how agentic AI can complement their GenAI capabilities, the potential for cost reduction becomes increasingly clear. While GenAI has already demonstrated efficiency gains, agentic AI's capabilities promise even greater savings by independently executing complex tasks and proactively identifying optimization opportunities. However, it's crucial to recognize that agentic AI is one piece of a broader, interconnected AI ecosystem, and its full impact on various industries is still unfolding. The key to realizing agentic AI's potential lies in thoughtful preparation and implementation.
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