Will the world of artificial intelligence (AI) ever slow down?

For a while now, OpenAI’s ChatGPT has been able to understand and discuss any image shared with it. For example, you can take a photo of a menu in a different language and ask ChatGPT to translate it, learn about the food’s history, and even ask for recommendations based on your preferences.

Concentrix launched its own AI assistant, iX Hello™, with new multimodal capabilities. Our chatbot sounds friendly, is fast to respond, and users can engage with it in life-like ways, giving it near human-level communication skills.

Google announced Project Astra, the tech giant’s vision for the future of AI assistants, with the promise of being able to turn “any input into any output.” What followed was a massive number of additional reveals across Trillium, Gemini Nano, Imagen 3…the list goes on.

All of this is truly remarkable, but even for the brightest minds, the pace of change can be bewildering. It also places a lot of pressure on organizations to adapt, respond, and understand how they can apply these advances in a business context.

It’s easy to get swept up in the excitement of AI innovation and feel compelled to act. Yet, given this climate, it feels more relevant than ever to focus on keeping things simple and focused—in other words, stay true to your customers’ needs.

Here are a few thoughts and ideas to take advantage of AI in a way that improves the customer experience and aligns to your business’s AI readiness.

Where to Start with AI Adoption

As with all technologies, the main questions and starting point remain the same:

• What problem are we trying to solve? Organizations must understand their direction of travel when considering technology solutions.
• Is generative AI really the right solution? Or would more straightforward automation or a more established AI solution be more appropriate?
• What can we do that doesn’t involve AI? Things like process engineering and data cleaning are ripe for improvement before AI is ever needed.
• Do we have the right foundations in place? If generative AI is the clear solution to your problems, what are the right benchmarks and ingredients for success?

Whenever this much hype surrounds a new technology, many decision-makers quickly leap to the conclusion that everyone else is doing this, so they need to jump on the bandwagon. Then, they take on expensive, unscalable proof-of-concepts with limited thought to the longer-term challenges and scalability. We saw this with robotic process automation at the peak of its hype cycle, and we’re seeing it with AI today. So, how can you avoid the pitfalls of the AI bandwagon and actually tap into the massive benefits that a robust generative AI solution can bring?

Road to AI Success

Most organizations will need to establish—or transform—several foundational elements to assess their AI readiness and ensure implementation success.

• Data is crucial: Generative AI models are most effective and impactful when trained on clean, accurate data, so the most crucial investments to make are in data organization, accessibility, security, and maintenance. Powering an AI model with bad data will result in incorrect answers and hallucinations—“garbage in, garbage out.”
• Vision, goals, and mission: Once your data strategy is in order, you need to develop a company-wide AI vision that aligns closely with your overall mission. Determine how your vision for generative AI aligns with your business goals, and make sure you have a clear roadmap outlined that takes you from point A to point B without leaving anyone behind—the more people who support your AI mission, the better.
• Tech investments: With everyone on board, evaluate and understand your investments in the right technology. Look at your existing infrastructure, platforms, and tools, along with your capability to develop, deploy, and maintain generative AI capabilities effectively. We like to think of it as a “digital core” from which to build, encompassing everything from cloud and compute power to legacy integration and security.
• AI governance: Are there clear ethical guidelines, principles, and governance structures in place for your shiny, new AI tools? If not, it’s crucial to develop some flexibility in your approach that allows for alignment between your controls and the level of risk presented by your use case. It’s common for brands to overlook their alignment between AI adoption plans and ESG commitments, too, so be sure to avoid falling into that category.
• Customer and employee journeys: Any implementation of generative AI will result in a transformation of processes, journeys, and workflows, both for customers and employees. Reimagining these journeys to integrate generative AI seamlessly will not happen overnight, so find your motivation from the desired result, reducing effort and removing friction for everyone at every touchpoint.
• People and talent: Assess your employees’ existing skills and knowledge of generative AI tools. Be sure to develop plans alongside your people to address any gaps in knowledge and concerns over AI adoption, as well as addressing any negative impressions of AI adoption that could slow progress and minimize its benefits.
• Operating model: Consider blueprinting a robust management framework with defined roles, responsibilities, and leadership to oversee the adoption and governance of your generative AI initiatives. There are many different models out there, and your needs might evolve as you scale, so having a dedicated team to keep you on the right path is essential.
• Change management: A comprehensive change management strategy will enable you to effectively test, scale, and embed generative AI capabilities across the organization. Your leaders should also step up as role models for the change you want to see company-wide. Buy-in from the very top will ensure it trickles down.