By Spence Green, CEO and Co-Founder of LILT. LILT were finalists in the ‘AI Startup of the Year‘ category, and shortlisted in four other categories at The 2024 A.I. Awards.

It’s well understood that AI will bring massive efficiency gains to organizations of every stripe, but most storylines fall short in fully explaining the changes enterprises will experience as they scale their adoption of AI.

Not only will AI streamline enterprise work streams, but it will also fundamentally transform people’s jobs and critical business operations.

At LILT, we enable AI-powered enterprise content translation and creation; in this space, we expect a complete transformation of localization roles as well as a dramatic shift in the way enterprises engage their audiences worldwide. Below we’ve shared our thoughts on how AI will reshape business operations, job scope and function, workflows for global content translation and creation, and more.

Prediction 1: Organizations Will Develop Purpose-Built Domain Models

As AI continues to advance, we expect every enterprise and government agency to create a model or suite of language models that have been built and trained for specific organizational workflows and domains. These models will serve as a strategic asset, enabling organizations to manage and utilize their data more effectively. By harnessing their proprietary data, organizations can train models that understand their specific context, culture, and customer interactions, leading to more tailored solutions and communications from their AI-enabled applications.

We recognize that the expertise required to build and operate these models will remain a scarce resource, and thus will likely require third-party support. AI model management is likely not a core competency of most functional business teams (such as marketing or localization), and thus must be made more accessible. By implementing a solution that simplifies the building and management of domain-specific models, non-technical teams will still be able to leverage advanced AI capabilities.

Prediction 2: There Will Be a Shift to AI Interoperability

Currently, many enterprises focus on vendor interoperability, which can be a labor-intensive, inefficient use of organizational resources. As enterprises integrate AI more fully across their workflows, model interoperability will begin to take precedence. Organizations will require orchestration of varied AI agents and models tailored to specific use cases. In this new paradigm, businesses will evaluate model performance, and the optimal deployments of these models to enhance operational efficiency.

At LILT, we’re already seeing our customers fundamentally change their translation and content management workflow architectures. More and more, they’re moving away from reliance on a single, monolithic AI model toward a more strategic multi-model approach. This empowers our customers to create more engaging and relevant content, fostering stronger connections with their global audiences.

Prediction 3: Major LLM Providers Will Reduce Their Vertical Focus

While major cloud service providers like Google, Microsoft, and Amazon have historically created specific applications within vertical markets, we expect major LLM providers to shift their attention elsewhere. Much of the current efforts of these providers are dedicated to enhancing model layers and developer experiences; as a result, we expect to see a slowdown in the attention they dedicate to development of vertical applications.

Instead, businesses will look for ways to integrate powerful AI models into their existing processes, including content creation and localization. This will drive a shift towards modular solutions, allowing companies to customize their AI capabilities according to their unique operational needs. By leveraging pre-built components and APIs, enterprises can create bespoke solutions without the lengthy development cycles typically associated with vertical applications.

Prediction 4: Demand for AI-Generated Quality Control Will Increase

As AI-generated content becomes more prevalent, quality expectations will rise. Over the past several years, the proliferation of user-generated content has not lowered the bar for quality; instead, it has heightened it. As businesses seek to differentiate themselves in a competitive landscape, the demand for verification and customization will become increasingly important. This will require a hybrid approach, blending human oversight with AI capabilities to ensure content meets the highest standards.

Moreover, the rise of AI in content generation will necessitate the development of new quality control frameworks. Organizations will need to establish clear metrics and standards for evaluating AI-generated outputs, fostering a culture of continuous improvement. By doing so, they will be better equipped to maintain brand integrity and ensure that their messaging resonates with diverse audiences around the globe.

Prediction 5: The Role of Human Involvement Will Change, But Remain Critical

Despite advancements in AI, human involvement will remain crucial, as the need for human judgment in decision-making processes will persist. At LILT, we advocate for a collaborative model where AI manages the heavy lifting of data processing, while humans make critical decisions based on AI outputs. Not only will humans drive critical decision making, but they will also continue to drive the quality of data that trains AI systems for continuous improvement.

As AI manages the large majority of routine, repetitive tasks, the role of human workers will shift towards more strategic functions. Employees will need to develop new skills that complement AI capabilities, such as critical thinking, emotional intelligence, and cultural awareness. By focusing on these skill sets, organizations can create a workforce that is not only adept at leveraging AI tools but also capable of driving innovation and fostering relationships with clients and partners.

Prediction 6: The Velocity of Solution Centralization Will Increase

We are currently witnessing a trend of centralization in response to economic pressures, including inflation and rising costs, with companies opting to consolidate diverse use cases on a single unified platform. AI is facilitating an even greater unification by vastly increasing the use cases that can be managed by and developed within a single platform. LILT is embracing this approach by offering an all-in-one AI solution for translation and localization, allowing businesses to minimize integration efforts and focus on delivering impactful content within their own products, marketing and customer engagement channels.

Centralized solutions also enable organizations to streamline operations, reduce redundancies, and improve data accuracy and leverage across departments. As companies adopt these unified AI platforms, they are able to allocate resources more effectively, focusing on growth and innovation rather than administrative burdens.

Prediction 7: Internal Tooling Will Remain a “Buy, Not Build”

As organizations engage in building and experimenting with AI models, many will find that the costs and complexities of developing internal tools outweigh the benefits. For global content creation and translation, the majority of businesses will opt for a “buy, not build” strategy, choosing to partner with specialized providers like LILT to access the expertise and technology needed for effective localization.

This decision will not only allow these organizations to save time and resources, but also enable them to tap into cutting-edge technologies that they may not be capable of developing in-house. For example, by collaborating with AI localization specialists, companies can focus on their core competencies while ensuring that their localization efforts are supported by the latest advancements in AI. This approach ensures that processes are future-proofed and allows teams to exponentially increase their time-to-market with high-quality localized content.

Conclusion

At LILT, we’re excited about the future of enterprise AI and its potential to transform global content. Across industries, we anticipate an ongoing focus on AI model orchestration and deployment, an escalating demand for quality in AI-generated content, and an increased requirement for human oversight. As we navigate this dynamic landscape, we look forward to helping organizations leverage AI to enhance their global communication strategies.

Let’s continue to shape the future of global content together.