Why Gen AI adoption among businesses will look radically different in 2024
Here are the four reasons product and marketing leaders have been hesitant to jump on Generative AI, and why 2024 is going to be very different.
“Should I wait and see how Gen AI is evolving and risk being left out, or should I jump on board now and try to build in uncharted territories?” Although every leader is facing this same question, not everyone has the same mindset when it comes to implementing Generative AI capabilities.
The attitude toward AI depends on multiple factors: does the company have past experience with traditional AI and Machine Learning (ML)? Is the company managing sensitive data? Is the culture of innovation, experimentation and intrapreneurship being encouraged? Is competition already moving ahead with Gen AI investments? Is the organization highly siloed or at the opposite, encouraging cross-discipline collaboration?
Given these variations, here are the dichotomies that currently exist and why next year, we will see more convergence and less friction.
1. Customer experience benefits vs. internal efficiencies. There are two types of conversations happening in organizations, often running in parallel by different groups of people. The product and marketing leaders driven by audience research, innovation and experimentation, are mostly looking at how Gen AI can make a visible impact on customer experience. This means redefining the value proposition of parts of their business, looking at opportunities for new features, new solutions, and new services. For example, customizing content and boosting product search in their e-commerce experience.
The other mindset focuses on operational efficiencies, looking at how AI will streamline processes, accelerate workflows, change teams and roles, or what types of talents need to be recruited to drive the next generation of AI-powered solutions. Conversations in this group of leaders revolve around how to assess often fragmented legacy systems, how to automate the data and information LLM (Large Language Models) can be trained on and how to sanitize data for security for legal and privacy reasons.
In 2024 expect the existence of these competing priorities to become unsustainable. Organizations will need to seek to create synergistic AI strategies between external and internal priorities to benefit both value to customer experience and business operation efficiencies. This will only happen when businesses create transversal strategies that bring multi-disciplinary teams together. Organizations that can successfully operate hybrid teams will outpace the competition in implementing AI.
2. Focusing on long-term AI foundations vs. immediate solutions. We are witnessing a classic organizational divide between technology departments and business and customer-oriented teams. The former wants to make sure they’re making the right choices in developing broad and deep Gen AI capabilities and infrastructure. This includes setting up, training and tuning the right LLMs, developing the right security protocols, and even assessing pre-existing traditional AI and ML infrastructure in the face of the next generation of AI. In a landscape that is experiencing a rapid evolution of both proprietary and open-source models, Meta’s Llama 2 being one recent example, it is critical to invest in the right models for ensuring future-proof scalability and adaptability.
The latter mindset is reacting to immediate, visible innovations that are burst everywhere, every day. These product and customer experience leaders feel the pressure of competition and are adopting aggressive strategies for solving challenges and customer pain points immediately. This ranges from customer support to content marketing automation to music and art generation to e-commerce assistance.
In 2024 expect larger organizations to still be a step behind. As the battle between proprietary and open-source LLMs won’t settle down anytime soon, the foundational and infrastructure decisions won’t be made immediately. Technologically-driven teams will still need time to consider their options and the right architecture, while business and customer-focused teams will push to offer new services and solutions. In this context of very high competitive pressure, larger organizations will suffer more from this situation as they will move slower than smaller competitors that can make decisions and launch new services faster.
3. Staying in control vs. jumping in the sandbox. Organizations that have been building traditional AI and ML capabilities are hesitant to suddenly disrupt legacy technology and adopt new LLM technology right away. Some technology leaders want to stay in control of the systems at play in the organization to perform upgrades towards Gen AI and are reluctant to let other teams experiment with large language models (LLMs) and AI agents while they make decisions regarding security, protocols and workflows. This will be a very common mindset among digital and technology leaders. Priority will be given to safety against experimentation not only to the detriment of speed and innovation but also to team training and culture of AI.
Other organizations, smaller and more agile, or simply more culturally attuned with shared innovation, will be jumping faster into an iterative learning and development mindset, focusing on speed and impact of new capabilities. The companies that create their AI sandbox, let teams play and learn by trial and error, run experiments on specific use cases to learn faster, will benefit.
In 2024 expect the gaps between those two mindsets to narrow quickly. Seasoned traditional AI/ML teams will start implementing and fine-tune commercial-use LLMs to build up capacities, while new players in the field will implement processes and guardrails for Gen AI experimentations. In large organizations where both mindsets exist, expect them to more closely collaborate. Setting the right protocols and workflows for teams is as important as experimentation and innovating rapidly.
4. Wait and see vs. building muscle. It is clear that some leaders prioritize a conservative approach when it comes to implementing Gen AI, seeking assurance that AI technologies are well-established, and the pace of advancements has stabilized. There’s an obvious advantage to this approach: avoid rushing into potentially risky ventures and let other organizations pave the way. These businesses will seek well-established products from recognized proprietary models at sticker price. The disadvantage of this approach is it will only allow the company to upgrade their systems along with the mainstream market and won’t offer any competitive edge to their customers. Internally, teams won’t learn much along the way and AI will be seen as another capability.
The other group of leaders are embracing Gen AI experimentation and learning from the outset. They believe that AI development is an ongoing journey, and the sooner they start building their capacities, the better positioned they will be against competitors. By creating an environment that encourages experimentation and innovation, not only organizations can iteratively develop their AI capabilities, they will also attract AI talent early on, allowing them to stay ahead of the curve.
In 2024 expect to buckle up. The conservative and safe mindset will be challenged and forced to move ahead with accelerating implementation and take calculated risks. AI’s momentum will not slow, and everyone will feel the pressure to get onboard.
Overall, the hesitancy among product and marketing leaders to fully jump into generative AI is understandable. The pace of AI evolution is unprecedented, and uncertainties about the landscape abound. However, it is evident that 2024 will usher in a significant shift, marking a departure from the cautious approach we witness today.
Leaders will bridge the gaps between conservative and innovative attitudes, recognizing that waiting for certainty may lead to missed opportunities and simply create a steeper learning curve. Product and marketing leaders are going to need to get out of their comfort zones and into the AI execution mode faster than ever.
Matthieu Mingasson is Head of Design Transformation at Code and Theory.
