Most enterprises are adopting AI, but few see sustained value. McKinsey reports that 71% of organizations now use generative AI regularly, up from 65% last year. BCG finds that 74% struggle to scale measurable outcomes.
Your board wants progress, not another rushed initiative that drains time or adds technical debt. A mess forms when teams run overlapping pilots, adopt tools without coordination, or send sensitive data to vendors without clear contracts. It also shows up when AI-generated code slips into production without review.
Gabe Harris, Vice President of Engineering at Axian, sees two common extremes:
- Leaders who think AI can replace engineers
- Leaders who avoid AI entirely
Most organizations sit in the middle and need a safer path forward.
In this article, we’ll look at some tips on how to incorporate AI into your business by blending it with the systems you already run, without creating chaos.
Tip #1: Start with Clear Goals So AI Doesn’t Drown You in Noise
Choose two or three goals that matter, like faster release cycles, fewer incidents, or shorter support queues. Having clear outcomes keeps AI work from drifting into novelty or competing with other priorities.
Then pick workflows that directly affect those goals. Pull request review and test failure triage influence release speed. Incident triage shapes incident count and time to resolve. Initial ticket classification and response drafting affect support queues. These are concrete places where AI can reduce friction without adding confusion.
Axian saw this when a senior engineer used AI tools to deliver a custom enterprise application three times faster than expected. That speed came from clarity. The team knew what they were building and how it needed to behave.
This prevents scattered pilots and misaligned investments.
Tip #2: Fix Your Data and Platform Reality Before Layering on AI
Many AI models in business now use generative tools that sit on top of existing systems and data. When that
- Which systems hold our critical data, and who owns them?
- Can teams reach privacy-safe data through stable interfaces?
Unchecked growth and tightening controls create data handcuffs. Compliance hardens, environments multiply, and production data becomes so restricted and inconsistent that teams stop trusting it.
Focus first on the platforms behind your key workflows. From there, define a small set of reliable datasets and the integration paths that feed AI, so you lower the risk of AI acting on incomplete or misleading information.
Tip #3: Redesign Workflows and SDLC so AI Fits Your Existing Systems
AI works best when it lives inside the systems your teams already use. Many organizations add tools on the side and end up with uneven adoption, fragile workflows, and code that no one can explain. The better path is to decide where AI assists and how those actions fit into your current development and operational rhythms.
At Axian, we see AI support on a spectrum, from answering targeted questions about the code in front of a developer to generating full functions from a prompt.
Each mode demands a different level of review, testing, and ownership. AI can increase engineering throughput only when paired with senior oversight and a mature SDLC. Strong tests, steady review, and reliable pipelines keep AI-generated changes from adding hidden complexity or risk.
Tip #4: Put Guardrails Around AI – Policy, Risk, and Vendor Posture
Banning AI does not stop its use. If people cannot access safe tools, they will find workarounds that leadership cannot see or control. A clear AI in business strategy gives teams approved options and limits unnecessary exposure.
Start with simple boundaries. Define where AI is allowed, what data is restricted, and how approved tools must operate. Log high-impact activity, especially anything touching customers, production systems, or financial decisions.
Vendor posture matters as well. Your logs and stored prompts may be treated as assets if providers fail or change ownership. Those risks shape where AI can safely run. Some teams can use public cloud tools. Others need private or local models that keep sensitive code inside the network.
Strong guardrails prevent shadow AI, data exposure, and unpredictable decisions in high-risk areas.
Tip #5: Pilot with Intent, Measure Hard, and Scale Patterns
The last step to incorporate AI into your business is to treat AI work the same way you treat any strategic capability. Test it, measure it, and standardize what proves durable.
Choose one or two use cases tied to the outcomes you set early. Establish a baseline for metrics that matter, like lead time, ticket resolution time, and incident frequency. Run a time-boxed pilot using the guardrails in tip 4 and the workflow rules in tip 3. When the pilot moves the numbers, turn them into a pattern that other teams can reuse.
Axian uses a pattern-first approach to AI integration. Once you solve authentication, data access, logging, and oversight for one application, you can reuse that structure across the rest of your portfolio. This avoids one-off builds and keeps AI from spreading unevenly.
Bring Your AI Strategy to Life with Axian
You now have a frame for outcomes, data, workflows, guardrails, and pilots. The hard part is applying it inside your actual systems, constraints, and politics. That is where AI work usually stalls.
Axian’s senior consultants can work alongside your teams to map high-value use cases, prepare the data and platforms behind them, set guardrails that match your risk posture, and turn one solid pilot into patterns the rest of the portfolio can reuse.
Ready to apply this strategy inside your environment? Contact Axian today.