What Does it Take to Deploy AI Successfully?

AI use is growing rapidly. Research from Stanford University found that 78% of organizations reported using the technology in 2024, up from 55% the previous year.

Unfortunately, however, that speed of implementation often comes at the cost of security and the problem is that, in a mad dash to adopt AI and remain competitive, organizations are chasing innovation faster than they can secure and govern it.

Anyone can start using AI models, but it takes the right combination of technology, skills, and strategy to ensure it actually becomes a strategic advantage, and not another risk.

The Foundations of a Successful AI Deployment

Technology: Integration, Not Isolation

For AI to be effective, it must connect with the systems, workflows, and data sources that power your business. That means clean, well-structured data and interoperable platforms that can share intelligence across your enterprise ecosystems.

For operations, this might mean linking AI-powered analytics tools to ERP systems; for customer service, it might mean integrating chatbots with CRM data. The bottom line here is that the more context an AI tool has, the more effective it will be.

Policy: Governance, Guardrails, and Security

However, because you’re connecting AI to some of your most critical assets and sensitive information, governance is crucial. You must define who owns AI outputs, how data is managed, and what boundaries exist for automation. Without those guardrails, you risk compromising your compliance status, brand reputation, and bottom line.

Security is a huge part of effective AI governance, and that means:

• Protect the Data: Secure the information that trains and informs models through strong encryption, data classification, and loss-prevention tools. Validate inputs to prevent data poisoning and bias.
• Secure the Infrastructure: Apply zero-trust principles to AI systems. Limit access, enforce the principle of least privilege, and monitor APIs and integration points where data moves in or out of models.
• Govern the Model: Maintain full traceability of inputs, outputs, and changes. Continuous monitoring, red-teaming, and explainability testing keep models auditable and aligned with policy.
• Establish Oversight and Accountability: Build cross-functional governance that includes technical, legal, and ethical perspectives. Every deployment should have a clear owner responsible for its integrity and compliance.

Frameworks like the NIST AI Risk Management Framework, ISO/IEC 23894, and the SANS Critical AI Security Controls provide practical guidance to operationalise this approach.

Remember: AI security isn’t something you can bolt on at the end; it needs to be top of mind from the beginning of your implementation journey.

Skills: Preparing Your Workforce for AI

Despite what some news headlines and vendors might like you to think, AI doesn’t replace humans; it merely changes how they work. If you do your implementation right, it will be for the better.

Successful AI adoption relies on investing in AI literacy. This involves training staff to work with automation, interpret AI-generated insights, and maintain oversight of outcomes, as well as how to identify the security risks associated with AI and reinforce the responsible use of AI tools and data.

Strategy: Aligning AI with Business Goals

According to MIT, a staggering 95% of generative AI pilots are failing. Gartner estimates that over 40% of agentic AI projects will be canceled by the end of 2027. A lack of intent is a big reason for that.

Too many organizations deploy tools for the sake of it, before they’ve even defined the problem they want to solve. Whether your goal is faster decision-making, operational efficiency, or improved customer experience, measure AI against specific business-metrics.

Is AI Necessary for Security?

Yes, but with caveats.

Attackers now move at machine speed, so defenders need to follow suit. And, with attack surfaces expanding, manual defense simply can’t keep up. Put simply, AI can correlate signals, identify anomalies, and conduct investigations far faster than humans can.

However, it can only do that when organizations balance automation with strong governance and trusted data. Fortra’s approach reflects that balance.

Fortra Threat Brain connects indicators across attack chains using shared intelligence to cut false positives, while Data Classification and DLP ensure accuracy by protecting sensitive data at the source. Integrated into the Fortra platform and XDR, automated playbooks then prioritise and investigate alerts to reduce analyst fatigue and speed remediation.

Internally, Fortra governs AI adoption through a cross-functional Generative AI Council that oversees the rollout of third-party generative tools, from coding assistants and sales co-pilots to customer support augmentation. Pilots are underway across engineering, marketing, and service teams, each grounded in auditability, human oversight, and measured implementation.