From Hype to Impact: Making AI Work for the Enterprise
WELCOME
Welcome to this edition of our BGTS Tech Insights newsletter. This month, we're focusing on practical applications of AI in enterprise environments—cutting through the hype to explore how intelligent systems can deliver real value across operations, decision-making, and service delivery.
As AI continues to reshape how organisations work, scale, and compete, our goal is to provide clarity and structure—backed by engineering expertise and business insight. At BGTS, we help enterprises move from isolated pilots to integrated, outcome-driven AI solutions built for long-term success.
START WITH BUSINESS PROBLEMS, NOT AI EXPERIMENTS
Why prioritising business alignment matters
Many AI initiatives fail because they start with the technology—not the business need. In fact, 88% of AI pilots never reach production, often due to unclear goals or pressure to adopt tools without a defined use case. As Cassie Kozyrkov (former Chief Decision Scientist at Google) puts it, the quickest way to lose value is to apply AI to poorly framed problems.
What is an AI pilot? A limited-scope trial of an AI solution used to evaluate its technical feasibility and business value before full-scale implementation. AI pilots help organisations test specific use cases—such as automation, prediction, or classification—under real conditions, allowing teams to measure performance, identify risks, and build a case for broader deployment.
At the enterprise level, this leads to wasted resources, stalled momentum, and stakeholder fatigue. Organisations that begin with a clearly defined objective—like reducing churn, improving forecasts, or streamlining operations—are far more likely to achieve measurable, scalable results.
How to structure AI initiatives for value
To get meaningful results from AI, enterprises need to start with a well-defined business challenge—framed around outcomes, not technology. Estimating the potential impact in financial or operational terms helps build a strong internal case. The most effective solutions are often the simplest—AI should only be used where it adds real value. A contained pilot with clear KPIs enables fast feedback and informed scaling.
At BGTS, we help engineering-driven teams move from ideas to structured, integrated AI implementations—aligned with business priorities and built for sustainable performance.
PRIORITISE INTEGRATION ACROSS YOUR AI INITIATIVES
Why integration is essential
AI models alone don’t deliver value unless they’re integrated into the systems where work happens. Without proper integration into enterprise infrastructure, even the most advanced models remain isolated and ineffective. In fact, 86% of enterprises face tech stack limitations when deploying AI agents, with many needing access to multiple data sources. Surveys from Deloitte and Databricks also show that poor data architecture and governance are major barriers—making integration a foundational requirement for scalable, high-impact AI adoption.
What is an AI agent? An AI agent is a software entity that can perceive its environment, make decisions, and take actions to achieve specific goals—often autonomously. In enterprise settings, AI agents are used to handle tasks like answering customer queries, processing requests, or managing workflows. They can be rule-based or powered by advanced models like large language models (LLMs), and are typically integrated into systems to operate continuously, adapt to context, and support users or teams across functions.
How to Engineer Integration Effectively
Effective AI integration starts with strong infrastructure. Enterprises need real-time pipelines, middleware platforms, and early testing to ensure models connect seamlessly with existing systems.
Integration should be governed end-to-end—with clear data policies, security controls, and scalability built in from the outset. Without this foundation, AI remains isolated. With it, AI becomes a connected, scalable driver of operational value.
FOCUS ON SIMPLICITY IN EARLY WINS
The case for targeted, simple AI applications
AI projects that start too big often stall. While most organisations use AI in some form, only a small fraction achieve measurable returns at scale. The most effective implementations focus on simple, repeatable tasks with clear KPIs. Studies show that even lightweight tools—like AI-assisted email, document summarisation, or helpdesk triage—can boost productivity by over 10% in weeks. Early wins build momentum and prove value faster.
Why simplicity pays off
Simple, focused AI pilots deliver value quickly, reduce implementation complexity, and lower risk. By starting small, organisations gain measurable results early, avoid unnecessary overhead, and create a clear path to scale with confidence.
Examples of High-Impact, Low-Complexity AI Pilots
• Transaction Categorisation in Banking: AI can automatically classify customer transactions into categories like utilities, groceries, or subscriptions—enhancing personal finance tools and reducing manual data handling for support teams.
• Email and Ticket Summarisation: AI-powered summarisation helps reduce manual triage workload by 12–15% in just a few weeks, accelerating response times and improving support efficiency—for example, by extracting key details from claim emails in insurance or customer queries in banking.
• AI-Assisted Development: Developer tools powered by AI can speed up configuration, testing, or routine coding tasks—reducing overall workload by more than 20% while improving delivery time.
• Product Description Generation in Retail: Retailers can streamline content creation by using AI to draft product descriptions from basic item data, helping teams keep up with high-volume product uploads and seasonal updates.
4 Steps to Launch a High-Impact AI Pilot
Early AI success starts with small, repeatable use cases. By targeting high-frequency tasks, setting clear metrics, and using lightweight prototypes, organisations can validate impact quickly. These focused pilots build trust, deliver measurable results, and lay the groundwork for scaling AI across the enterprise.
ESTABLISH GOVERNANCE BEFORE YOU SCALE AI
Why robust AI governance matters
AI adoption is growing fast, but governance isn’t keeping pace. While 83% of organisations in Europe use generative AI, only 31% have formal policies in place. Even fewer—just 18%—have invested in safeguards. This gap creates risks around compliance, bias, and security. Without proper oversight, AI can undermine trust and limit long-term business value.
Core elements of effective governance
Effective AI governance relies on five key elements: clear ownership, lifecycle integration, human oversight, regular policy reviews, and employee training. McKinsey reports that only 28% of organisations assign AI governance to CEOs, and just 17% to boards—highlighting a gap in strategic oversight. Deloitte recommends embedding governance throughout the AI lifecycle to ensure fairness and compliance, especially in high-risk scenarios where human intervention remains essential.
Yet many organisations lack the foundational support for responsible AI use. Only 31% of leaders expect generative AI to significantly transform their business within a year, suggesting a lag in readiness. Meanwhile, ISACA reports that a large number of firms lack role-specific training and internal clarity, which undermines safe and responsible AI usage across teams.
4 Key Benefits of Enterprise Governance AI Initiatives
Strong AI governance delivers four key benefits: it protects brand reputation through regulatory compliance, mitigates bias and ethical risks, strengthens security against threats like deepfakes, and lays the groundwork for scalable, coordinated deployment.
To achieve these outcomes, governance must be embedded from the start—with defined accountability, human oversight in high-risk areas, regular policy updates, and training across roles.
At BGTS, we help enterprises implement governance frameworks that support ethical, resilient, and scalable AI adoption—fully aligned with business strategy.
TREAT AI LIKE ANY OTHER SCALABLE INVESTMENT
The challenge of scaling AI effectively
Scaling AI remains a major challenge for enterprises. While many invest in pilots, few treat AI as a long-term capability. As a result, 85% of AI projects never reach production, and only 1% of U.S. companies have scaled AI successfully. The real barrier isn’t experimentation—it’s building a clear, sustainable path to scale.
What leaders do differently
Organisations that succeed with AI treat it as a long-term investment. These leaders are 3× more likely to scale projects, achieve double the ROI, and invest heavily—averaging $8.7 million annually. Over 50% allocate budget for AI portfolio tools, and more than a third dedicate $1 million+ to governance and tracking. Their success stems from deliberate, well-structured investment—not experimentation.
Scaling AI as a Strategic Investment
means allocating dedicated budgets for governance, infrastructure, and operations—not just experimentation. Success also depends on tracking performance across the full lifecycle, using consistent metrics to evaluate speed, accuracy, and ROI. Reusable infrastructure such as shared pipelines and MLOps platforms accelerates delivery while building trust in AI systems.
Strategic oversight is equally critical. Leading organisations bring together CFOs, CIOs, and CSOs to align funding, execution, and business goals. Measuring both upfront and ongoing ROI—across CapEx and OpEx—is essential, with continuous tracking beyond initial deployment. AI should be treated as a scalable capability, not a one-off initiative. When done right, it becomes a long-term asset that drives meaningful, measurable business value.
At BGTS, we guide enterprises through this transition: from isolated pilots to resilient, ROI-driven AI portfolios—supported by engineered infrastructure, leadership alignment, and measurable outcomes.
WHY CHOOSE BGTS?
Choosing the right partner is critical to making AI work at scale. Here’s how BGTS delivers measurable impact across every stage of your AI journey:
Engineering-Led Approach: Our AI solutions are built with technical rigour, strategic alignment, and measurable business outcomes in mind.
Tailored to Your Business: We design and implement solutions that integrate seamlessly with your existing systems and workflows—no off-the-shelf compromises.
Scalable, Integrated AI Systems: We help you move from isolated pilots to enterprise-ready AI—supported by governance, infrastructure, and long-term clarity.
Custom Development & Integration: From platform extensions to complex integrations, we solve real problems with purpose-built, future-ready solutions.
24/7 Managed Services: Our support teams ensure maximum uptime and productivity, giving you confidence and continuity at every stage.
Trusted by Industry Leaders: We’re known for proactive delivery, long-term partnerships, and a commitment to results—not just implementation.
LOOKING AHEAD TOGETHER
Thank you for exploring this edition of BGTS Tech Insights newsletter.
We hope the insights shared here help you cut through the noise and move toward real, scalable value with AI. As enterprises continue to adopt intelligent systems, clarity, structure, and the right partners will define long-term success.
Stay tuned for future editions, where we’ll share more perspectives on engineering-led solutions that deliver measurable impact. 👉 GET IN TOUCH
