As organizations look to declutter outdated tech stacks and scrub away inefficiencies, generative AI offers a powerful way to reset how businesses work, automate, and grow. But like any good cleaning project, success starts with a methodical plan and the right tools.
Two years after ChatGPT kicked off the AI boom, generative AI has gone from futuristic to foundational. According to McKinsey, 65% of organizations now use gen AI in at least one business function, up from just 33% the year prior. Yet despite the surge in adoption, many leaders still aren’t sure how to move from experimentation to enterprise-scale execution.
Like any seasonal reset, this is a moment to clear out what no longer serves the business—and make room for smarter, more scalable ways of working. The organizations making headway share a common approach: they start with a sharp focus on high-value opportunities, invest in the right data infrastructure, upskill their teams, and stay grounded in business outcomes.
Here’s a four-step framework to accelerate AI adoption:
1. Identify, assess, and prioritize opportunities
Successful gen AI implementations start by pinpointing business-level problems the technology can help solve. Generating ideas is easy; the challenge is defining value and determining scalability. Not every use case justifies investment. Teams need to focus on opportunities where gen AI can drive measurable impact—through efficiency gains, better decision-making, or improved customer experience.
Think of this as a kind of digital spring cleaning: it’s a chance to take stock of where AI can truly make a difference, and clear out the noise of low-impact ideas. From there, feasibility must be assessed. Do you have the right data, infrastructure, and internal skills? Will employees adopt the solution—and trust its output? Only the most promising ideas should move forward.
Prioritize initiatives that align with strategic goals, offer clear returns, and can be executed within current time, budget, and talent constraints. This early rigor lays the groundwork for long-term success.
2. Lay the foundation with data and integration
Businesses need to enable seamless access to high-quality, relevant data and applications to provide gen AI with a trusted and clean data set. This access is the backbone for actionable and accurate insights that drive business success.
Data quality, relevant data sources, and interoperability are among the most important integration factors fueling large language models and enabling accurate, context-aware outputs. Data needs to be clean, complete, and accurate, which may require data cleansing to fix incorrect, incomplete, or irrelevant data.
Organizations should prioritize data sources that directly impact their gen AI use case—for example, data from support ticket logs and call transcripts to support a customer service chatbot—and choose integration tools and platforms that support interoperability among legacy systems, modern applications, and AI frameworks.
3. Align roles, skills, and organizational capabilities
Gen AI is reshaping roles within IT and data teams. Companies are beginning to see roles like data and application integrators morph into AI-specific titles, like AI engineer, which focuses on integrating AI features into existing enterprise software systems, applications, and processes.
These new emerging roles require a blend of software architecture, model understanding, and prompt engineering skills. Organizations need to invest in upskilling their teams and providing access to training, certifications, and external expertise when necessary. Supporting cross-functional collaboration also helps share knowledge and accelerate innovation.
4. Build for long-term value and track success
AI initiatives must be sustainable to deliver ongoing value. That means considering security, governance, and error tolerance from the start. Understand where AI can operate with some margin of error and where precision is non-negotiable. Integration plays a key role in scaling AI agents to automate workflows, enhance decision-making, and deliver real-time insights. Success depends not just on the technology but also on the people and processes surrounding it.
Measuring and tracking success is essential. Establish KPIs tied to business outcomes—like time saved, productivity gains, or cost reductions—to measure impact and guide future investment. Regular reviews allow teams to refine strategies and scale what works. Gen AI is moving fast, and organizations that measure, learn, and adapt will be best positioned to lead.
Once gen AI projects were deployed, Cooperative Benefits Group focused on measuring results. The team tracked time savings in service interactions and configuration processes, as well as reductions in software development errors—demonstrating real cost savings and performance improvements. These outcomes helped secure additional budget and support for scaling gen AI efforts. Meanwhile, Aptia treated early projects as test cases, using them to build internal confidence and refine its investment strategy.
Time to clean the clutter
Gen AI is reshaping how organizations innovate, make decisions, and deliver value at scale. The opportunity is enormous—but seizing it takes more than optimism. It demands strategy, focus, and the right foundation.
The organizations seeing early wins are those taking a deliberate, methodical approach: identifying high-impact use cases, grounding them in clean, relevant data, integrating seamlessly with existing systems, and empowering skilled teams to execute.
Treat this opportunity like a strategic clean-out: focus on what drives impact, retire what doesn’t, and rebuild with intent.
The speed of change isn’t slowing. Gen AI is getting better by the day. Now is the time to experiment.
