More specifically, generative AI (GEN AI) AI is becoming increasingly standard within organizations of all sizes.
In many cases, Gen AI deployments and integrations are fragmented, just like with new technologies. Different groups within an organization employ different tools used in a variety of applications.
Individual units and teams may recognize benefits, but fragmenting AI deployments that are not uniform across the organization creates AI silos. For CIOs and their organizations, these silos create overlaps with multiple efforts between the same business unit. There is also a risk of compliance as uncontrolled efforts may not meet the organization's requirements. AI silos could have led to a decline in data quality, which could have missed out on value opportunities.
Why AI Integration is a CIO's priority
A Chief Information Officer has many responsibilities, including setting IT policies and defining technology and its operational strategies across the organization.
AI integration is a CIO's priority for a variety of reasons.
- Board order. Many boards require the use of AI.
- Regulation demands. Privacy and security regulatory requirements are increasingly affecting the use of AI.
- Enterprise strategy. It's important to have an enterprise-wide data AI and data strategy that enables interoperability and cost-effectiveness.
Todd Loiselle, Chief Information Officer at National Food Group, explained that AI is another tool for his organization, helping to increase revenue, reduce costs and make employees more effective.
“Everyone spends part of the day repeating and on less valuable tasks, and AI can help them process them faster and help people focus on work that has a higher impact,” Rousell said.
For Chris Campbell, CIO at Devry University, AI integration is a priority. Because isolated pilots don't provide the necessary value.
“Siloed experiments can generate pockets of insights, so integration is important, but rarely offers durable value,” Campbell said. “We want AI to accelerate outcomes across the agency, not just a single feature.”
CIO Integrated Playbook
While various CIOs tend to have different approaches to integration, the next important pillar in general is the fundamental elements of the AI integration playbook.
1. Enterprise AI Strategy
It's important to align AI with business goals rather than isolated experiments. To do this, the leader must:
- Start with business strategy, not technology.
- Safe CEO sponsorship.
- Start by focusing on fewer, more impactful opportunities and grow from success.
2. Data Integration
Data is important for AI integration efforts. Make sure to build a unified data fabric with clear governance by doing the following:
- Deploy a unified data platform.
- Implement AI-specific governance.
- Create a feature store that enables reusable AI-Ready data products across teams.
3. Platform approach
Take a platform approach by standardizing tools and APIs across the organization to make integration effective.
- It employs platform engineering.
- Expand the API Gateway.
- Build a reusable platform, avoid one-time tools, and instead create shared services, governance, and data pipelines.
4. Collaboration that transcends the scope of work
If the business units don't cooperate, the silos exist. It is essential to break down the silos between it, data science and business units by doing the following:
- Create an interdisciplinary team.
- Integrate AI capabilities into existing workflow tools
5. Management
Integration is also about changing from an existing paradigm to a new one. So change management is essential to teams, and it is important to manage cultural resistance and communicate value.
- Implement multi-level training.
- We convey the value of multi-stakeholders.
6. Governance and Compliance
We embedded risk management, transparency and ethical frameworks by doing the following:
- Manipulate AI transparency.
- Implement a comprehensive AI framework, such as NIST AI risk management.
- Address regulatory requirements.
- Establish monitoring systems such as automated compliance and risk assessment tools.
Examples of other CIO cases
The following example shows how a major CIO has applied integration principles to break down AI silos and promote value across the enterprise.
Flexera: Avoid Shadow Ai
The use of AI often exists as a form of Shadow AI. This is the use of an AI tool or service without formal approval from the company or IT department. There was a case
Chief Information Security Officer, CIO and Flexera. He explained that his organization began an AI implementation plan by first investigating the Shadow AI tools already used by Flexera employees. By voting for staff on which AI tools are helping them with their daily tasks, the company has gained valuable insight into how their teams are integrated into their workflows creatively and organically. The effort helped highlight early use cases and opportunities to support what was already working well.
Gallagher noted that the integration of siloed efforts begins with allowing AI to appear in the pockets of the entire business, whether leadership has a strategy or not.
“Our first step was to publish informal AI use cases by attracting employees,” Gallagher said. “From there we were able to arrange our tools in a wider ecosystem.”
Gallagher emphasized that for his organization, AI's ROI can not only save directly, but also allow teams to make smarter decisions, promote cross-team collaboration, and eliminate waste.
“Identifying the needs of our organization has allowed us to move from reactive cost reduction to aggressive optimization and shift to long-term business goals,” he said.
Devry: Governance is important
In Devry, Campbell explained that real advances have been created after the organization set up a small AI enablement team and a governance model while watching employees experiment with generated AI in their pockets.
“By centralizing intake, we stopped replicating our work and started building reusable agent AI patterns,” Campbell said.
He said the shift has resulted in faster deployment cycles, clearer accountability, measurable time savings and incident response.
“The role of a CIO is to make sure AI is a multiplier of forces across the enterprise, not another silo to manage,” Campbell said.
One: Measuring success
Elizabeth Hoemeke, CIO of Digital Payment Network Company, encourages a bottom-up approach to AI integration, empowering teams to identify, experiment and implement AI-driven tools and services.
Hoemeke said her company's IT team has established a productivity benchmark and now she can see improvements every month.
“The AI features we implement should include usage statistics and cost management, which are essential to promote adoption and responsible use of AI,” she said. “One Inc has recently established the AI Center of Excellence, which is billed for cataloging all efforts across the company to ensure the benefits of investing in AI programs, ensuring minimal overlap in efforts, sharing learning and best practices, and establishing metrics.”
CIO Action Checklist
The success of AI is no longer about experimentation. That's about Integration, scale, governance. The CIO is in its own position to lead this transformation. Use this checklist to evaluate your current AI landscape, identify silos, and implement integration strategies.
|
Action Items |
Key Metric or Target |
|
Audit all AI initiatives across business units |
Map current AI tools and 100% of spending |
|
C-Suite will participate and create an AI steering committee |
Secure Executive Sponsorship and Decisions |
|
Integrate redundant AI tools and vendors |
Reduce tool sprawl by a measurable amount |
|
Establish the AI Center of Excellence |
Create shared services and standards |
|
Define integration of KPIs and success metrics |
Target specific ROIs and time frames within 18 months |
|
Start AI literacy training program |
Achieve a high percentage of employee AI recruitment |
|
Create AI ethics and risk policies |
Verify compliance and responsible AI use |
“Looking ahead, I don't think the real differentiator is the person who uses AI. Who will seamlessly integrate it into your everyday workflow safely and responsibly,” says Loiselle.
Sean Michael Kerner is an IT consultant, technology enthusiast and Tinkerer. He is known to draw a token ring, configure netware, and compile his own Linux kernel. He consults with industry and media organizations about technology issues.
