Unpacking Enterprise AI with Conor Twomey, CEO of AI One

Enterprise AI is no longer a futuristic concept; it’s rapidly becoming a core driver of innovation and strategy across industries. From automating processes to uncovering customer insights, AI is reshaping how businesses of all sizes compete and grow.

But success in enterprise AI isn’t about adopting the flashiest tools — it’s about aligning technology with real business goals.

We spoke to Conor Twomey, CEO of AI One, about how enterprise AI is evolving beyond the buzz. In a market saturated with jargon and hype, Twomey offers a grounded perspective on what actually works, what doesn’t, and how to achieve practical results with AI.

He also shares candid advice and a look ahead at what’s next in the world of AI-powered business.

Q: To begin with, would you like to share some details about your role as the Co-Founder / CEO of AI One?

Twomey: As Co-Founder and CEO, my mandate is simple: turn AI from a slide-deck promise into operational reality. AI One was built for the executives who are tired of 18-month data-lake projects and consultant PowerPoints. We connect directly to the systems enterprises already run, deliver a working prototype in a week, a limited production system in 5 weeks and then scale to a full production in ten weeks – our 1-5-10 model.

My job is twofold. First, I partner with CEOs and CIOs to pinpoint high-value workflows where a 90 % error cut or a 40 % opex reduction isn’t theoretical, it’s measurable within a quarter. Second, I make sure our platform actually achieves those outcomes by embedding agentic AI engineers that automate, monitor, and continuously optimize operations. We call this AI-Shoring: bringing critical work back in-house, but letting AI do the heavy lifting.

I draw on a 16-year career delivering real-time analytics in some of the most regulated environments including investment banks, insurers, and healthcare networks – so I’ve seen first-hand how complexity strangles progress.

At AI One we break that cycle: no data duplication, no rip-and-replace, just faster insight, lower cost, and a clear runway to autonomous system management. That’s the lens I bring to every customer conversation—and the standard I hold our team to every day.

Q: Despite record spending on AI infrastructure, most enterprises are failing to see operational improvements. As a leader, what are your thoughts on this? Why do most enterprise AI projects fail, and why so many are stuck in an expensive arms race mentality rather than practical outcomes?

Twomey: Because they’re building monuments, not machines. Executives have been told that the prerequisite for AI is a pristine data lake, re-platformed systems, and a castle of cloud contracts. Result: billions spent, dashboards everywhere—and not a single workflow running faster.

At AI One we flip the order of operations:

1. Start with the stopwatch, not the architecture diagram. We pick a use-case where time, cost, or risk is painfully visible (e.g., 14 hours to reconcile trades or 12 weeks to onboard a patient file). If the impact isn’t provable inside a quarter, we don’t touch it.

2. Connect, don’t copy. Our platform interrogates live systems in situ—Mainframes, SAP, custom Oracle stacks—so there’s no data duplication, no six-month migration, and no compliance limbo.

3. Ship in weeks, scale in months. A one-hour diagnostic yields a working pilot in five days and enterprise-grade automation in ten weeks. That cadence builds trust and unlocks budget far faster than a seven-figure “foundation” program.

Why do 85% of enterprise AI projects fail? Because the budget owner can’t point to a line item where cost dropped or revenue rose. Johnson & Johnson’s success in drug discovery wasn’t about spend; it was about targeting a bottleneck – molecule screening – and compressing that cycle from months to days. Same playbook, different domain.

In short, AI isn’t an arms race; it’s a precision strike. Win one beachhead with measurable ROI, reinvest the savings, and the transformation funds itself. Anything else is just repainting the data center.

Q: Is AI Shoring the new trend? Are we entering the era of “AI Shoring”?

Twomey: Yes – and it’s arriving faster than most boards and c-suite executives realize for two reasons:

First, the economics have flipped. Wage inflation is erasing the labor-arbitrage that justified traditional off-shoring. When a Manila support center costs within 15 % of a Midwestern one, the real question becomes: Why are we moving the work overseas at all? The second is a security wake-up call. Distributing sensitive data across a web of vendors now carries a nine-figure downside when breaches or regulatory fines land. Bringing processes back inside the enterprise AI control isn’t just prudent—it’s mandatory.

AI-Shoring solves both problems by repatriating critical workflows and letting autonomous software—not armies of contractors—handle the repetitive, rules-driven tasks that previously went overseas. But the point isn’t “replace human with bot.” It’s to:

● Restore control. Data stays on approved systems, under existing entitlement models, and is audited by the company’s own security team.

● Compress cycles. An invoice that used to ping-pong across continents for 48 hours is reconciled in 4 minutes.

● Lower true cost of service. You pay once for the model, not forever for headcount, turnover, and training.

● Create an innovation dividend. Freed headcount can shift from keyboard-driven work to higher-order design, governance, and customer experience.

We see AI-Shoring as the natural successor to off-shoring, allowing enterprises to keep their strategic IP at home, slash latency, and future-proof operations against an increasingly hostile threat landscape. Enterprises that adopt it first won’t just cut costs; they’ll own the speed and resilience curve for the next decade.

Q: As a leader, how do you ensure your AI initiatives are ethical and responsible, and what safeguards are in place?

Twomey: Responsible AI isn’t a marketing slide for us – it’s the acceptance criteria that determines whether a deployment goes live. My own rule of thumb is simple: every automated decision must have a human who can explain it, reverse it, and be accountable for it. That philosophy shapes four concrete safeguards at AI One:

1. Sovereignty by Design AI-Shoring keeps data inside the customer’s existing perimeter. Our agents run on-prem or in a dedicated VPC, inherit the AI enterprise’s entitlements, and never pipe data to third-party LLM endpoints. That removes an entire breach vector and keeps us aligned with HIPAA, FINRA, and GDPR residency rules from day one.

2. Transparent, Auditable Models Every workflow ships with a “model card” that documents training data boundaries, known limitations, and bias checks. We log every prompt, decision, and upstream data source into the client’s SIEM so security teams can replay and audit any action—no black boxes, no surprises.

3. Integrated Guardrails and Red-Team Loops We embed policy engines (think ABAC for AI) that enforce role-based access and block disallowed instructions in real time. Before production cut-over, each agent is stress-tested by an internal red-team that tries to induce leakage, bias, and rogue actions. Fail a test, fix the agent, repeat.

4. Human-in-the-Loop Accountability Critical decisions—claims denial, trade execution, patient triage—require explicit human review until accuracy, fairness, and explainability thresholds are met and signed offby the client’s governance board. Even after auto-approval is allowed, humans can override any result and the override feeds continuous-learning pipelines.

Ethical AI isn’t a cost center; it’s the license to operate in regulated industries. By combining on-prem agentic automation with auditable guardrails and human accountability, executives can have the confidence to scale AI without betting the franchise on an opaque black box.

Q: What is your approach to training your workforce on AI technologies, and how do you plan to foster a culture of enterprise AI literacy within the organization?

Twomey: We treat AI literacy like a trading desk treats risk management—non-negotiable and tied to every role’s performance. Our playbook has three layers:

1. Foundations for Everyone

○ 30-Day AI Bootcamp. Every new hire—engineer, salesperson, or finance analyst—spends their first month in a structured “Promptcraft & Systems Thinking” program. They learn how large-language models reason, how to design guardrails, and how to translate workflow pain points into agent specs.

○ Weekly Hot-Seat. On Fridays, one team demos a live agent they built; peers interrogate its assumptions, security posture, and ROI. It’s part learning lab, part cultural ritual: you show what you’ve learned, not just list certificates.

2. Deep Skills for Builders

○ Paired Development. Domain experts (claims, treasury, pharmacy) pair with our AI engineers to ship a micro-project each week. The rule: if the prototype can’t be measured in time, cost, or risk reduction, it doesn’t graduate.

○ Red-Team Rotation. Engineers cycle through our internal red-team to attack colleagues’ agents for bias, leakage, and safety gaps. Nothing sharpens skill faster than trying to break—and then harden—someone else’s code.

3. Continuous, Context-Driven Learning

○ Just-in-Time Micro-Lessons. Whenever we update our agentic framework, a three-minute Loom and a hands-on lab drop into Slack. People learn in the flow of work, not in quarterly seminars.

○ ROI Leaderboard. We publish the operational impact of every production agent—minutes saved, dollars reclaimed—so staffcan see which ideas are winning and replicate the patterns. Visibility breeds motivation.

The outcome is a workforce that sees AI not as a black box but as a colleague they can debug, direct, and hold accountable. That mindset spills over to our customers: we don’t just deliver automation; we leave behind teams who understand – and can extend – the systems we build.

Q: Can you provide an example of a significant technological challenge you faced and how you overcame it?

Twomey: A global insurer asked us to streamline a policy-change process that spanned 14 separate systems – everything from modern microservices to 25-year-old mainframes. A single amendment required 11 hand-offs and a quarter-end reconciliation team of 30 offshore analysts. Re-platforming was scoped at three years and $40 million – time and money they didn’t have.

We solved it in ten weeks without moving a byte of data. Our first step was to deploy an agentic crawler that reverse-engineered each system’s schema – APIs, databases, even green-screen screens – and generated a living ontology. Translation agents then wrapped the legacy apps, normalising entities in real time and exposing them through a single graph endpoint. Finally, orchestration agents automated the end-to-end workflow, flagging humans only when confidence dropped below 95 %.

The outcome: policy-change cycle time plunged from five days to 18 minutes, the quarterly reconciliation head-count fell from 30 to 4, and the project recouped its cost in under 100 days. The lesson is clear: you don’t beat legacy by ripping it out; you beat it by making it instantly intelligible to AI and automating the value-creating steps on top. That’s AI-Shoring in action.

Q: AI automation is indeed gaining traction and can automate many tasks previously done by humans. Do you think AI will replace humans in the future?

Twomey: History shows that every major wave of automation—steam, electricity, the microprocessor—re-defined jobs rather than erased them. AI is no different; it will absorb the repetitive “keyboard work” and expand the surface area where people exercise judgment, creativity, and empathy.

At AI One we design for that shift explicitly:

● Automation as capacity, not head-count reduction. Our deployments target workflows where machines can deliver near-instant precision – reconciliations, claim triage, entitlement checks – then redeploy the recovered hours into product design, governance, and customer conversations. The very point of our three-step model is to free teams to focus on strategy and innovation rather than administration.

● Human accountability remains non-negotiable. Every agent ships with guardrails, audit logs, and a named business owner who can explain or reverse any AI-made decision. That rule—that a human is ultimately responsible—is the cultural bedrock I brought from fifteen years in regulated markets.

● Skills over roles. We train every employee, from sales to security, to think in prompts, policies, and probabilities. When you understand how to steer AI, you don’t fear replacement; you wield leverage. The ROI leaderboard we publish internally turns “AI literacy” into a friendly competition rather than a compliance exercise.

So no, AI won’t replace humans; it will replace the parts of jobs that sap human potential. The leaders who reinvest those efficiency gains into higher-order work—rather than head-count cuts—will define the next decade of market outperformance.

Q: Considering AI is the way forward, what advice would you give to future leaders?

Twomey: Treat AI like a profit-and-loss project, not a science experiment. Four rules will keep you on the front foot:

1. Start with results, not infrastructure. . Before you fund another “data-modernisation” slide deck, pick a workflow where seconds matter—claims triage, trade booking, patient intake. If you can’t promise a time-to-impact of < 90 days, you’re chasing vanity architecture, not value.

2. Bring your critical work home. Data sprayed across half a dozen outsourcers is a breach waiting to happen. AI-Shoring lets you pull critical processes back inside your own perimeter—same cost advantage, exponential security upside, total governance control.

3. Turn employees into AI power users, not spectators. Models alone won’t differentiate you; the human-AI handshake will. Make promptcraft, policy writing, and agent debugging core skills for every function—finance, ops, even legal. Publish an ROI leaderboard so people see exactly how much value their automations create.

4. Move at the cadence of innovation, not audit cycles. We’re in a “mobile-in-2007” moment—six-month delays today equal multi-year gaps tomorrow. Build lightweight guardrails (role-based access, model cards, red-team loops) that enable weekly releases instead of annual checkpoints.

The winners won’t be those with the biggest cloud bills or the largest language models; they’ll be the leaders who combine surgical enterprise AI deployments with relentless human ingenuity—and who start now, while the window for leapfrogging remains wide open.