Introduction

A mid-sized European software development company delivering bespoke solutions and SaaS products for B2B clients set out to accelerate its adoption of artificial intelligence. Operating across finance, healthcare, and manufacturing, the organization faced increasing competitive pressure from larger vendors rapidly embedding AI into their offerings. Following a period of rapid growth through acquisitions, leadership recognized the need for a structured, value-driven approach to AI investments that could deliver tangible business impact without overstretching internal capabilities.

The Story

AI ideas were emerging organically across the organization, driven by motivated teams and customer demand. However, without a unified framework, initiatives varied widely in maturity, scope, and expected value. Some experiments showed promise, while others risked adding complexity without clear returns.

Kibit Solutions partnered with the company to bring clarity and structure to its AI ambitions. Through an eight-week Use Case Landscape & Business Value Assessment, the focus shifted from isolated experimentation to a strategic, organization-wide view of where AI could deliver measurable value.

The engagement combined business-process analysis, technology evaluation, and collaborative workshops with stakeholders across departments. This approach enabled the organization to identify, assess, and prioritize AI initiatives based on impact, feasibility, and strategic alignment, while maintaining momentum and innovation across teams.

The Challenge

Several challenges hindered the organization’s ability to move forward confidently with AI adoption.

AI initiatives lacked strategic direction, emerging ad-hoc without consistent evaluation or prioritization. The company’s diverse technology landscape created uncertainty around tools, platforms, and build-versus-buy decisions. At the same time, uncoordinated “shadow AI” experiments introduced governance risks and potential technical debt.

Limited in-house data science capacity further constrained execution, increasing the risk of overcommitting to complex initiatives. Most critically, leadership lacked clarity on which AI features would genuinely enhance products, improve efficiency, or create customer value—making investment decisions difficult and high-risk.

The Result

By applying a structured, evidence-based assessment framework, the organization achieved clear and actionable outcomes:

Strategic visibility: A centralized inventory of more than 30 AI opportunities enabled transparent, data-driven prioritization across the business.

Shadow AI integration: Previously undocumented initiatives were consolidated, reducing duplication while strengthening governance without stifling innovation.

Quick wins delivered: Two early initiatives—an AI-powered code review assistant and an automated ticket-classification model—were delivered within four months, reducing support response times by 35%.

Business value clarity: Each AI initiative was linked to measurable KPIs across cost reduction, efficiency, revenue potential, or customer satisfaction.

Risk reduction: Objective evaluation prevented investment in low-value, high-complexity projects and minimized vendor lock-in risks.

Technology alignment: A phased roadmap clarified future infrastructure and platform investments.

Organizational momentum: Early successes and structured planning built internal confidence and strengthened cross-team collaboration around AI adoption.