Agentic AI: How to Maximise ROI through Strategic Deployment?
Recording of a recent online workshop presentation by Simon Torrance, CEO AI Risk, on ‘How to Maximise ROI from Agentic AI’
This recording - and summary transcript below - supports the launch of our new our new ‘Agentic AI Enterprise Blueprint’ which provides a practical roadmap for leaders looking to leverage the full power of Agentic AI. Details here: www.ai-risk.co/agentic-enterprise
Most organizations approaching agentic AI today are destined to capture only a fraction of its potential value. Not because they lack resources or technical capability, but because they're asking the wrong question.
They're asking: "How can we use AI to optimize our existing operations?"
They should be asking: "How can we fundamentally rethink our business model with a new type of workforce?"
The difference between these two questions represents the difference between incremental cost savings and transformative competitive advantage. Between modest ROI and exponential returns. Between the efficiency trap and strategic deployment.
Why Most AI Initiatives Will Fail to Maximize ROI
The consulting advice flooding boardrooms today follows a predictable pattern: identify functions where AI can reduce headcount—customer service, finance, claims handling—cut staff by 30%, and redeploy savings into higher-value activities. It's a tidy business case with quantifiable savings and manageable change.
It's also the equivalent of breeding faster horses when the automobile has already been invented.
This "faster horses" thinking captures short-term efficiency gains but misses the paradigm shift entirely. In just 13 years during the early 20th century, automobiles completely displaced horses in urban centres once the benefits became evident. The AI transformation will unfold even faster.
The efficiency trap looks financially prudent in spreadsheets, but it optimizes you into irrelevance. While you're reducing costs by 30%, competitors are deploying agentic AI to unlock entirely new revenue streams and capture markets you've deemed "too complex" or "too expensive" to address.
What Strategic Deployment Actually Means
Strategic deployment of agentic AI isn't about better productivity tools for existing workers. It's about creating an entirely new workforce—what Nvidia CEO Jensen Huang calls a "digital and biological" workforce, where AI workers potentially outnumber human employees several times over.
This isn't hyperbole. Accenture is training 700,000 people in agentic AI, expertise that will flood the market within 12-18 months. Moderna has merged its IT and HR departments specifically to manage this new hybrid workforce.
The strategic deployment question isn't "How can we do what we currently do with fewer people?" It's "How can we do exponentially more with our existing human workforce augmented by AI workers at 3x, 10x, or 100x scale?"
Consider the insurance industry, which has maintained a flat GDP share for decades. ‘Protection gaps’—uninsured risks—represent a market nearly equal in size ($6 Trillion) to the entire existing industry ($7 trillion). Companies haven't addressed these opportunities because they're operationally infeasible. Agentic AI makes them economically viable by providing unlimited operational capacity at near-zero marginal cost.
The ROI Framework: Three Paths, Dramatically Different Returns
Path 1: Do Nothing – Wait and watch competitors. Result: gradual market share erosion. ROI: negative relative to market.
Path 2: Optimize Existing Processes – Deploy AI tools to reduce costs. Achieve 20-30% efficiency gains. Result: modest margin improvement, business model unchanged. ROI: incremental, quickly matched by competitors.
Path 3: Strategic Transformation – Create a portfolio of agentic AI systems that enable fundamentally new business models. Result: new capacity to serve customers better, unlock previously unaddressable markets, and grow ahead of competitors. ROI: exponential and defensible.
Analysis of commercial underwriters, for example, shows approximately 45% of their tasks can be automated or augmented by AI. The efficiency-trap response: reduce underwriting staff by 45%. The strategic deployment response: free underwriters to focus on judgment-intensive work and managing hybrid human-AI teams, while AI workers handle routine analysis at scale.
The financial difference is profound. Cost reduction delivers one-time margin improvement. Capacity expansion enables continuous revenue growth into new markets at margins competitors can't match.
Strategic Deployment in Practice: Real ROI Results
Two commercial deployments demonstrate the ROI differential between optimization and transformation.
Case Study 1: Autonomous AI Workforce
To enable a company to launch a new insurance business two years ago we created an autonomous team of AI agents with specialized roles—facilitators, experts, analysts, and specialists—collaborating on Slack with each other and with human workers.
The ROI results:
Net promoter scores exceeding industry standards
Underwriting profitability significantly superior to established competitors
Human employee satisfaction increased
24/7 operational capacity with 70% lower human headcount
Successful market entry with zero prior expertise
When one of Google's AI strategy teams heard about our deployment, they acknowledged seeing nothing comparable in commercial production globally.
As our Agentic Team matured, an unexpected ROI accelerator emerged: human workers requested digital twins of their expertise because they couldn't keep pace with AI FTEs working continuously. One employee had her knowledge cloned into an AI agent, received a pay raise, and focused on higher-value work. Expert knowledge became a reusable asset rather than a capacity constraint.
Case Study 2: Multi-Agent Analytical Workflow
A large organisation regularly conducted major investment analyses typically requiring six senior consultants at £2,000 per day for six weeks—£300,000 per project. Our colleagues created a multi-agent workflow replicating this expertise.
The ROI transformation:
Development time: 3 weeks
Analysis time: 1 hour (versus 6 weeks)
Cost per subsequent analysis: near-zero
Quality improvement: 50 scenarios analyzed versus 2
Asset reusability: unlimited deployments at marginal cost
After recovering the development investment, every subsequent deployment delivers £300,000 in cost avoidance plus superior analytical depth. Ten analyses deliver £3 million in value. The ROI curve is exponential, not linear.
The Deployment Methodology: Maximizing ROI While Managing Risk
The proliferation of AI tools creates a dangerous temptation: distribute them across your organization and see what happens. This approach almost certainly leads to "pilot purgatory"—scattered experiments generating enthusiasm but no business transformation.
Strategic deployment requires:
1. Strategy Hypothesis First – Start with strategic questions: What business model changes could create 10x customer value? Which operationally infeasible opportunities become viable with unlimited capacity?
2. Enterprise Architecture for Agentic AI – You don't need perfect infrastructure before starting, but you need a coherent framework connecting legacy systems with front-end interfaces.
3. Portfolio Approach Across Capability Levels – Build capabilities spanning individual AI agents, multi-agent workflows for complex processes, and autonomous AI teams for operational support functions. Real competitive advantage concentrates in the advanced levels where you're creating proprietary capabilities competitors can't easily replicate.
4. The Accelerator Method – Move quickly from hypothesis to market testing. Build prototypes. Demonstrate value in weeks, not months. But ground everything in strategy, not IT-led experimentation. (More details here: www.ai-risk.co/agentic-accelerator)
5. Reframe Success Metrics – Traditional productivity metrics miss the strategic value. Track new revenue from previously unaddressable markets, customer value metrics, speed to market for new offerings, and competitive positioning in transformed market dynamics.
The Competitive Imperative
The window for strategic advantage is narrow. Organizations deploying agentic AI to optimize existing processes will achieve modest, easily replicated gains. Those using it to transform business models will open unbridgeable competitive gaps.
This isn't about technology adoption timelines. It's about strategic positioning before market structure changes. The insurance industry's protection gaps, banking's unserved credit markets, every sector's operationally infeasible opportunities—these become battlegrounds where companies with AI workforces compete against companies with human-only workforces.
The ROI question isn't whether to deploy agentic AI. It's whether to deploy it strategically or incrementally. One path delivers transformation and defensible competitive advantage. The other delivers efficiency gains your competitors will match within months.
Strategic deployment requires simultaneous focus on business model innovation, organizational design for hybrid human-AI teams, and technical architecture that enables rapid deployment while building toward scale.
Many organizations have extensive technical capability but most lack the strategic framework to deploy it for maximum ROI. They're optimizing for efficiency when they should be transforming for growth. The future isn't evenly distributed—some organizations are already operating in it, capturing returns that look impossible from a traditional workforce perspective. Maximum ROI goes to those who recognize that agentic AI isn't about making your current model more efficient—it's about making an entirely new model possible.
Our new ‘Agentic AI Enterprise Blueprint’ report provides a roadmap for leaders looking to create new growth and value from Agentic AI. You can download it here: www.ai-risk.co/agentic-enterprise
