AI in Technology: Safely Increasing Value
- James McGreggor
- May 27
- 4 min read
Updated: 2d
Overview
This use case demonstrates how AI can dramatically accelerate software development cycles by enhancing productivity, code consistency, and system scalability. Beyond no-code tools or consumer-facing AI apps, enterprise leaders are leveraging AI to transform how engineering teams deliver value—from prototyping to production.
The Challenge
Picture a software team under pressure to ship fast. Engineers face repetitive setup tasks, context switching, and integration bottlenecks. Documentation is outdated. Backlogs grow while technical debt accumulates. Despite skilled teams, the speed of innovation slows—and product timelines slip.
A recent report from McKinsey notes that developers spend up to 40% of their time on repetitive, low-level tasks, many of which can be automated with AI. Without automation, organizations risk developer fatigue, inconsistent codebases, and growing technical debt.
The Opportunity
AI can supercharge development by acting as a co-engineer, capable of:
Scaffolding frontend interfaces or backend APIs from structured inputs.
Generating test cases and synthetic data on demand.
Translating legacy code to new standards or languages.
Creating middleware, authentication libraries, and abstraction layers.
Optimizing SQL queries and logic sequences based on performance patterns.
Assisting with documentation, meeting summaries, or onboarding content.
Identifying and estimating technical debt across repositories—highlighting outdated patterns, redundant code, and areas lacking test coverage.
By embedding AI into your CI/CD workflows and code review pipelines, it can flag risky architecture, estimate refactor costs, and prioritize what to fix based on business impact; however, integration of AI does not have to start there, it can simply start by allowing your teams to leverage AI, in a safe and controlled way, so that they can leverage the power of AI.
For anyone concerned about data security and privacy, accessing LLM's, on the internet will clearly be a risk to look into; however, there are some solutions that exist which allow you to create local copies. To create and run local copies of large language models (LLMs)—meaning the model runs entirely on your own infrastructure with no internet/data transmission to external servers—you’ll need an open-source or self-hostable model and the right tooling.
Here are some of the top AI tools and frameworks that allow for this (as of the publication date of this article):
Ollama
What it is: A super simple tool to run LLMs locally with just a command like ollama run llama3.
Best for: Prototyping or personal use without cloud dependencies.
Website: https://ollama.com
LM Studio
What it is: A GUI-based desktop app that lets you download and run LLMs locally.
Best for: Non-technical users or educators exploring LLMs safely offline.
Website: https://lmstudio.ai
Hugging Face
What it is: Programmatic way to load models using Python or C++ and quantized formats for memory-efficient use.
Best for: Developers who want max flexibility.
Website: https://huggingface.co
NVIDIA NeMo
What it is: Production-grade server deployment tools for powerful GPUs or clusters.
Best for: Enterprises needing private, high-performance LLM inference.
Website: https://www.nvidia.com
⚠️ The tools and frameworks provided are done so without any guarantee or warrant, or official endorsement; these tools are provide purely for educational and informational purposes. Blue Forge Digital has no affiliation with the companies listed. Please evaluate the capability and security of each tool following your own corporate guidelines.
Enabling AI within your development and design teams empower them to maintain velocity without compromising code quality or long-term scalability.
According to Boston Consulting Group, AI-assisted development tools have the potential to increase engineering throughput by 20%–45%, especially when integrated into early-stage development planning and quality assurance.
The Approach
Start by aligning with engineering leadership on where AI can offer immediate gains—whether accelerating feature builds or reducing toil.
Define clear governance and goals: quality, speed, or maintainability.
Pair engineering leads with AI architects to create secure, productive workflows.
For most teams, a pilot can begin with internal tooling or low-risk modules—such as generating internal dashboards, testing logic, or templated UI components. From there, success metrics can be established to guide broader adoption.
What to Watch Out For
Code quality and technical debt: Poorly guided AI can produce brittle or bloated code—strong architectural review remains critical.
Security and compliance gaps: Ensure AI-generated components meet your org’s security protocols and regulatory expectations.
Tool sprawl and fragmentation: Integrating too many AI tools without a unifying approach can reduce clarity and increase overhead.
Over-dependence on AI: Engineers must remain in control—AI should augment decision-making, not replace architectural thinking.
Unauthorized exiting data: data that is being sent outside yoru organizations control or being accessed by external systems without proper security measures in place - just because the company of the system touching your data is secure and "trustworthy" does not mean that they can not be breached or that you are not in violation of some data privacy or compliance expectation.
The Impact
Engineering teams that adopt AI responsibly gain more than speed—they gain agility. Faster development cycles, cleaner codebases, early technical debt detection, and improved team morale translate into reduced cost of delay, better customer outcomes, and greater return on innovation investment.
Partner With Us
Blue Forge Digital leverages AI in areas that are logical. We keep our clients informed about the use of AI within their projects and only leverage AI in areas that create value for our clients (e.g., quality, creativity, efficiency) while not reducing security, stability, or maintainability.
Whether you’re building the next product or modernizing the engine behind it, we’re ready to help you build it smarter.
References
"Developer Productivity: How Software Teams Deliver Business Value." McKinsey & Company, 2023, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/developer-productivity-how-software-teams-deliver-business-value. Accessed 27 May 2025.
"AI Is Rewriting the Software Development Playbook." Boston Consulting Group (BCG), 2023, https://www.bcg.com/publications/2023/ai-is-rewriting-the-software-development-playbook. Accessed 27 May 2025.