Impact of Implementing an Agentic AI Strategy on Return on Investment (ROI)

AI-Driven Approach Boosting Profitability through Agency-Centric AI Strategies

In the rapidly evolving world of technology, the adoption of generative AI has become a buzzword for many businesses. However, too many high-impact initiatives often stall in pilot mode due to fragmented data, legacy tech, unclear ownership, and resistance to operational change.

To navigate this challenge, it's crucial to focus on targeted use cases that address high-impact processes with clear bottlenecks, repetitive manual work, or high error rates. Oleksandr Sheremeta, the Managing Partner & Co-Founder at Dataforest, a custom software development company with a focus on Data Engineering & AI, emphasizes this approach.

The key to unlocking the true potential of generative AI lies in its practical application as embedded operators, automating workflows and driving decisions. This shift from reactive tools to proactive, autonomous collaborators can be a critical component of pivoting business strategy. A report from McKinsey's AI arm, QuantumBlack, notes this promising shift in generative AI from a reactive tool to a proactive, goal-driven virtual collaborator.

To transition from using generative AI as mere tools to proactive, goal-driven virtual collaborators, businesses need to adopt a structured strategy that aligns AI with business objectives. This strategy should prioritise AI use cases based on return on investment and key performance indicators (KPIs), invest in workforce training, set clear success metrics, and integrate AI deeply into workflows and decision-making processes to generate measurable financial impact.

Key strategies include developing a clear AI integration strategy aligned with business goals, reworking processes and embedding AI-driven innovation, investing in workforce training and change management, setting challenging and quantifiable goals, leveraging data readiness and governance, encouraging early deployment and iterative learning, and integrating AI at the core of the business model.

By following these strategies, businesses move beyond experimentation towards AI-enabled virtual collaborators that drive innovation, efficiency, and tangible financial returns. For instance, companies have documented reducing campaign time-to-market by 50% and scaling AI usage broadly through clear policies and ongoing training.

However, it's essential to remember that this transformation requires a cultural shift. Leaders must frame AI as a collaborator that elevates human expertise, not as a threat. Building durable, cross-functional teams from day one, uniting business owners, IT architects, and data engineers, is crucial for AI workforce integration.

In the coming years, the practical application of agentic AI for business will crystallize around four pivotal patterns of transformation, including Custom Vertical AI Agents. Competitive advantage will go to those who integrate agentic AI systems at their core, not to those who continue to experiment at the edges.

In conclusion, businesses can harness the power of generative AI to drive significant financial returns, but it requires a strategic, disciplined, and collaborative approach. By focusing on targeted use cases, investing in workforce training, setting clear goals, and integrating AI deeply into business operations, companies can transition from using generative AI as mere tools to proactive, goal-driven virtual collaborators.