A CIO who starts from how your company makes money

Takes command of technology when a CIO leaves suddenly, a programme stalls or the company goes through a major change: stabilizes the systems and the team, makes the urgent decisions, sets a plan the business can execute, and stays until the right permanent leader is in place — or takes the role permanently.

Yes. I am based in Dallas, Texas. I work in person with established and family-owned companies in the Dallas–Fort Worth area, and remotely or on site with companies elsewhere in the United States, Europe and Latin America, as a permanent, interim or fractional CIO.

Typically when a company has outgrown its current systems, structure or way of operating; when growth is slowed by fragile technology, poor data or execution bottlenecks; when a traditional or family-owned business needs to modernize but does not know where to start; when legacy platforms must evolve without stopping revenue-generating operations; when an internal team has been stuck on the same problem for months; when a CIO, CTO or senior technology leader has left suddenly and someone has to take command immediately; when a company is being acquired, acquiring technology, or preparing for an exit and needs technical due diligence; or when a startup needs to move from MVP to a scalable, investable platform.

When technology decisions start to determine whether the business can grow, stay compliant or keep operating — and nobody senior owns them. Typical signals: growth outpacing systems, a stalled transformation, rising technology costs without a clear return, AI being adopted ad hoc, an acquisition or exit, or a technology leader leaving. Depending on the situation, the right answer is a permanent executive, an interim leader who stabilizes and hands over, or fractional leadership.

Yes. I have served as CIO and member of the board of family-owned companies, including a bakery and pastry business in Spain that had to compete with supermarkets and 24-hour gas stations, and a large company in the payments sector in Canada. Family businesses usually need to modernize without losing the knowledge and relationships that made them successful.

Yes, hands-on if needed. Small companies often depend on one supplier or one key person, with no plan for security, backups or growth. I can put suppliers, infrastructure, security and support in order myself, and then decide with the owners what the company needs next.

Mostly in unglamorous places: classifying and routing documents and requests, extracting data from unstructured inputs, helping staff search and draft, flagging anomalies, and supporting forecasts and decisions. The gains come when AI is built into an existing workflow with a measurable target — time per case, error rate, cost per transaction — rather than deployed as a stand-alone demo. If the underlying process or data is broken, AI usually amplifies the problem, so fixing that comes first.

By treating AI like any other data flow and deciding deliberately what it may access: classify the information, minimise what is sent, enforce access control and authorization at the integration layer, review each provider's contractual terms on retention, training use and processing location, manage keys and credentials properly, log usage for audit, and give employees approved tools and clear rules so sensitive data does not end up in whatever tool is at hand. No single measure guarantees confidentiality; the combination of technical controls, contracts and governance is what brings the risk to an acceptable level.

It depends on the sensitivity of the data, contractual and regulatory obligations, the quality each option delivers for the task, the cost at expected volume, and whether the organization can actually operate its own models. Enterprise agreements with major providers can offer meaningful contractual and technical protections; private deployment gives more control but moves security, patching, capacity and model quality onto your own team. Many organizations end up hybrid: hosted models for low-sensitivity work, tighter options for regulated or proprietary data.

Usually without replacing them. Expose the existing system through well-defined APIs or integration services, give the AI component only the data and actions it needs, and keep the system of record authoritative. It is the same discipline as any legacy modernization — incremental change, clear boundaries, observability and a fallback when something fails — and it is how I have modernized platforms that could not be switched off.

Like any other investment: a baseline measured before starting, a specific metric the initiative must move, the full cost — licences or usage, integration, data preparation, monitoring, human review and ongoing maintenance — and a decision point to stop if the numbers do not hold. A pilot that cannot name the metric it is meant to improve rarely produces a return.

Experimentation shows that something can work. Enterprise adoption makes it work reliably, securely and accountably inside real operations: integration with existing systems, access control, monitoring, cost management, output validation, support, and a named owner. Most of the effort — and most of the risk — lives in the second part.