Did you know, as many as 85% of generative AI deployments fail? Businesses all over the world recognize the importance of this transformative technology, and yet so many are struggling to make it work for them and justify their investment. The reasons are many, with unclear scope, poor data infrastructure and generic AI models all too commonplace.
Whether it’s driving new levels of productivity and efficiency, or finding new insights from customer data, generative AI’s potential is such that it’s essential for businesses to get it right. And if they don’t have the resources to achieve that in-house, then teaming up with the right generative AI development partner is one of the biggest decisions they’ll make in the coming years.
In this blog, we'll explore the main considerations to factor into AI partner selection as part of your enterprise AI strategy.
The choice between keeping things in-house or outsourcing to an external partner is always tricky, but especially so when it comes to generative AI development. Having guided numerous businesses through this complex decision-making journey, there are several critical factors that deserve careful consideration before committing to your AI development approach.
Staying internal allows an organization to maintain full and direct control over the development process, keeping things aligned with company vision and culture, and seamlessly integrating into existing systems and workflows. However, procuring the skills and expertise needed can be difficult, especially with the current AI talent shortage, and also expensive in terms of salary, benefits, training and recruitment.
Working with an external partner can ease the process of accessing expertise and skills, and can be more cost-effective at the same time. But on the other hand, it does mean relinquishing at least some control of project alignment, and can sometimes lead to challenges around communication and collaboration.
The third option is to take the best of both worlds, and use hybrid project management methodologies to blend building some parts of solutions and buying or customizing others. According to the Project Management Institute, the uptake of these methodologies rose to more than 30% in 2023; from our experience, covering governance internally but relying on external partners for execution represents a solid way forward.
Learn more about perfecting AI deployment in this blog: Unleashing the Power of AI: Best Practices for Enterprise Strategy and Deployment
When an organization decides to work with an external partner, there are several considerations to take into account when looking for the right one. Important questions to ask include:
Any organization will already have some of the skills and resources they need in place. Some partners will be better-placed than others to fill the gaps and ensure the combined effort covers every base; assessing partner skill sets should therefore always be considered in this context.
Every organization has different use cases and requirements for generative AI, and in a competitive global marketplace, generic solutions won’t cut it. A good AI partner will be able to assess an organization's specifics and select appropriate AI models as part of custom AI solutions; they should be able to demonstrate a proven track record of success with this approach.
A good example of this would be choosing retrieval-augmented generation for knowledge bases, or opting for a fine-tuned large language model (LLM) to create personalized marketing content.
Similar to the previous point, a good partner will be able to advise on the best way forward in terms of AI infrastructure requirements, whether that be cloud, on-premise or a combination of the two. There are major differences in terms of cost, operations, security, data volumes and performance, so understanding which option best fits specific requirements is crucial.
Generative AI deployments can get even more complex when having to accommodate multiple languages simultaneously, and/or accommodate legacy code. At Ciklum, we recognize that language isn't just a UX layer—it's fundamentally connected to context, compliance, and legacy systems.
For example, one multinational client came to Ciklum for support with a GenAI-driven assistant that needed to work in five languages while maintaining consistent domain-specific outputs across regions. This required true localization with prompt engineering and model fine-tuning to handle industry-specific jargon across languages.
Our teams enabled these multilingual GenAI tools to pull seamlessly from legacy knowledge bases, delivering end-to-end integration that ensures effective deployment in all operating languages.
Industry-specific expertise is vital, so that a partner understands typical pain points and business objectives, and knows how generative AI can solve those challenges.
For example, at Ciklum, we’ve been able to cut development time with AI-driven engineering in healthcare, and use personalized AI to improve customer experiences in retail.
With ethical and responsible AI practices in the spotlight, a partner should be able to show in practice that they have an ethical development framework in place. Alongside this, they should also have stringent data privacy and security protocols that can keep sensitive and personal data safe. Not only are these areas important for compliance, but they can actually drive competitive advantage through better user trust and brand perception.
If the partner is a leader in their field, then they will be able to show that they can attract and retain AI specialists with the very latest skills, including AI researchers, ML engineers, domain experts, data scientists, and UI/UX specialists. They will also be able to provide flexible collaboration models that mix and match different roles to seamlessly fit with the organization’s existing skill set and structure.
Getting answers to the questions above is only the first part of the journey towards a successful partnership for generative AI. The partner will also need to work closely with you all the way through to the launch of your platform - and continue to do so with ongoing support and monitoring.
At Ciklum, we take a fully-integrated approach to GenAI implementation, covering not only the platform itself, but also the monitoring, retraining, fine-tuning and scaling that comes with it. We don't stop at delivering an MVP - we focus on building sustainable AI capabilities that evolve with your business. Our operational playbooks include performance monitoring frameworks, feedback loops to refine model behavior, and change management strategies to ensure internal adoption.
For one global payment provider, we provided six months of embedded support post-launch, helping their in-house team build confidence while gradually transitioning ownership. This approach exemplifies how we enable clients to deploy GenAI models that perfectly fit their workforce and organizational needs today - while establishing the foundation and capabilities to easily adapt as those needs change in the future.
Learn more about how generative AI can work for your organization, whether it’s AI agents transforming automation and productivity, or practical examples of how GenAI can be applied in the business world.