Canada National AI Artificial Intelligen

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Is a Chief Data Officer also a Chief AI Officer?

Organizations can use the 2 titles in a broad way to gain agility, but if clarity in the mission and focus is the priority, the use of different role names and performance objectives is the right tool. While The Data Officer is in charge of the quality and value of the data usage, the AI Officer is in charge of the quality and competitive advantage of the prediction.

What are the responsibilities of a Chief AI Officer?  

1. Manage the Value of AI

  • Generating and Proving Cognitive Solution Hypothesis For Value;

  • Lower TCO and Speed Of Prediction;

  • Increase Precision/Accuracy Of Prediction and Liability Cost of Errors;

  • Machine Learning Standards, Vendor Management, RFP Writing, and Awarding ML contracts

2. Secure Governance

  • New Business Cognitive Models On Governance with IT/Cloud and LOB(Line Of Business);

  • Data Science/AI Talents Leadership, On-Boarding, Career Management/Path;

  • Orchestrating Cross-Divisional Collaboration with Scientists;

  • Change Management AI.

3. AI-Driven Data Strategy

  • Vision And Strategy On Capitalizing on Company Data;

  • Data Explorations and Sharing;

  • Generating New Data With External Data Acquisition 

4. Liabilities Oversight

  • AI Ethics, Privacy, and Legal Compliance Accountability;

  • Policy-based on Researcher Access;

  • Training path and career model for scientists.

Canada National AI Artificial Intelligen


We build, operate, then transfer AI expertise inside your organization.

For many businesses scaling AI programs and building internal teams is the next frontier. We work to help organizations develop internal competency while embracing AI in a scalable, sustainable way. We manage many efforts simultaneously: creating a strategic vision, taking stock of current capabilities, building AI-supporting processes and platforms, instilling AI expertise into the business, and cultivating AI-related activities.

Canada National AI Artificial Intelligen

Managed Services

AI managed services is about continuous data collection and learning. Models can progress and increase in capability over time when nurtured. If left alone, they can decay.


Collecting data and building initial models is the easier part. We help graduate pilot projects into operational deployment.


Continuous monitoring, training, and tuning services to ensure your AI programs increase in value over time.

  • Debug and test models in production

  • Monitoring and substitution of new models if the older model fail

  • Process development for continuous AI experimentation, integration, and deployment

  • Continuous learning, and feedback loop to improve its operations.

AI Operations

Organize for AI. AI requires more than data and technical mastery. Sustainable adoption requires integration between operations, data, infrastructure, and expertise. We help establish systems that provide easy access to your data. Then, we provide the resources to train the models. DAIMLAS enhances your capability and capacity to execute AI projects with an efficient system for data, technology, and expertise.

AI Operations Services

Canada National AI Artificial Intelligen
Canada National AI Artificial Intelligen

Fractional Chief AI Officer

Most organizations need technology visionaries at the executive level but struggle with finding the correct expertise or the investment. Some just need part-time leadership. Think of fractional expertise like leadership-as-a-service. We offer our experienced CTO’s as part-time leadership and consultants for hire. Our fractional Chief AI Officers help organizations build AI competency, strategy, and oversee project implementation.

Canada National AI Artificial Intelligen

AI Center for Excellence

DAIMLAS help organizations operationalize around AI and establish their own center of excellence. We provide team training, process development, AI DevOps, strategy consulting, and change management, as a bridge until internal operational excellence is established. Core features of an AI Center of Excellence includes the integration of: Business, Project, Learning, and Technology.

1. Business:

  • Align AI strategy to corporate strategy

  • Develop a business justification for AI investment

  • Build systems for measurement

2. Technology:

  • Assess digital maturity

  • Assess build versus buy infrastructure requirements

  • Establish requisite compute and storage needs

3. Learning:

  • Establish AI Operations

  • Human in the loop

  • Model management

4. Culture and People:

  • Staffing plans

  • Provide fractional leadership

  • Establish governance

  • Change management

  • Process & collaboration

Canada National AI Artificial Intelligen

Fractional Chief AI Officer

  • Assess data gaps and needs

  • Data aggregation

  • Dictionaries and ontologies

  • Data acquisition strategy

  • Consolidate data silos

  • Establish tooling (Docker, KubeFlow, Jupyter)

Awards & Recognition

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