Generative AI (GenAI) continues to provide significant business value across many use cases and industries. But despite the many successful customer experiences, GenAI is also proving to be challenging for some businesses to get right and deploy across their organizations in full production. As a result, plenty of projects are getting stuck in planning, experimentation, or limited pilots without ever delivering enterprise-wide impact.
Enterprises approach generative AI with big expectations – rightly so, as it’s utterly transformative. But for it to have that impact, businesses need help getting it off the ground and scaling beyond initial successes. CIOs and corporate technology leaders are discovering that the GenAI technologies they buy aren’t turnkey solutions. Enterprises can’t just purchase them, turn them on, and watch them go. That works for certain niche use cases, but it won’t cut it for GenAI projects designed to remake a business fundamentally.
Organizations Need Help Moving GenAI Beyond Pilots
Enterprises can’t continue to go it alone; they need guidance to move AI initiatives from experimentation to production. Companies need more than just their own tech experts to use generative AI at large scale successfully. They need strategic planning, data integration, and expertise in navigating complicated business challenges to meet expectations. Most companies lack the in-house resources for this, making it necessary to look for external support.
To thrive with artificial intelligence, companies need to forge strategic partnerships with various external vendors. These partnerships allow them to easily access cutting-edge tech, realize near-term value, and create an edge over their competitors. These partners know how to add different AI models and make data pipelines simpler. They also make sure you follow rules. This helps you use AI faster and avoid common mistakes. With the right support, enterprises can fully leverage their data for faster insights, better decisions, and enhanced competitiveness.
Choosing the Right Partner
The right partner comes in many shapes and sizes. Plenty of options are proving themselves valuable here: traditional consulting firms, individual consultants, MSPs, large AI vendors, and others. Every company has different needs that it must weigh. Generally, you should look for a partner with a proven track record in providing AI services – someone with demonstrated deep tech skills, a focus in your industry, an understanding of AI’s human challenges, can work within AI budgets, and experience in successfully rolling out AI solutions across enterprises.
Overcoming Five Key Challenges
The right partner will help you deploy generative AI by overcoming five key challenges.
Selecting a proper use case
Companies beginning with generative AI should start with use cases that 1) solve ongoing issues that impact the business and 2) are reasonably doable. A good example that fits both criteria is using GenAI to automate a tedious, time-consuming reporting process. It’s a task no one loves doing, but it’s got to get done. Even with these criteria, prioritizing the correct use cases at the beginning is easier said than done. There will still be plenty of candidates to choose from. A good partner provides clarity, leading you to a use case that is both feasible and impactful.
Optimizing KPIs
A GenAI partner will begin by aligning on business objectives to ensure your AI deployments support your organization’s most important key performance indicators (KPIs). Organizations shouldn’t leverage AI without a good plan – deploying an innovative, new technology simply because many of their peers are. They should do it to create new efficiencies that make a demonstrable, positive impact on the business. The right partner will work to map every AI project to specific outcomes, such as sales growth, increased employee retention, or lower customer churn.
The partner will be on point to observe and tweak AI solutions to continually optimize KPIs. This might involve changing algorithms, improving data quality, or adjusting the scope of the AI’s application to ensure ongoing value. By keeping business goals at the forefront, the right partner helps maximize your AI investment and achieve meaningful results across the enterprise.
Preparing data assets
Data is the lifeblood of artificial intelligence. Fortunately, with generative AI, data does not have to be perfect and pristine compared to the requirements for traditional, transaction-based deterministic systems. The key is ensuring AI has sufficient context from your business environment to deliver meaningful outputs – not perfect data, but the right data that’s relevant to the target use case. Don’t make the mistake of making data preparation too complex. Focus on giving AI systems the key information they need to create reliable and meaningful results.
Partners can find your most important data. They help build a practical data base that balances quality and access. They also guide you to add more data as the project grows.
Launching and maintaining AI solutions
AI initiatives are often rife with the most technical challenges when they’re just being launched. From model updates to data inconsistencies, a reliable partner ensures smooth deployment by anticipating and addressing these hurdles. Once these projects have gotten off the ground, they actively monitor performance while troubleshooting issues like AI models drifting or mitigating data security and regulatory compliance challenges to keep the project on track.
After launch, maintenance becomes a huge piece of the puzzle. AI models and capabilities are advancing at a rapid pace. Your partner should help navigate these updates, ensuring stability and minimizing disruptions, while also refining the system to optimize performance over time. Proactive maintenance strengthens AI solutions, keeps them aligned with your evolving business needs, and prepares them for broader AI adoption when the time comes.
Winning people over on AI
It’s not just technical issues that make GenAI hard. There’s also a human challenge. AI adoption requires buy-in among both business and IT leaders and support from actual end users. Employees must actually put new AI capabilities to use for these projects to take off. For that to happen, they have to grasp what exactly their AI can and can’t do and need training on how to properly employ it.
An AI services partner evangelizes the technology across your enterprise to create confidence and buy-in. As part of this effort, they will play an ongoing role driving training, responding to individual troubleshooting issues, and ensuring that employees know how to get the most from the tech. This human-focused way is very important for wide use. People often resist when AI moves from early users to the whole company.
Stepping into the AI-First Era
Enterprises that successfully pilot GenAI but struggle to scale those successes across the organization shouldn’t turn away from the technology. Generative AI is absolutely as revolutionary as it’s been billed. You don’t want your business to fall behind by giving up on the tech after some early struggles. The bottom line is that GenAI is difficult to get right for both small companies and large corporations. Look for a helping hand to transition from pilot to enterprise-wide implementation, and unlock productivity gains and additional benefits of AI throughout your organization.
