The Last Mile of AI: Bridging Tech and Humanity

Explore the profound impact of AI’s last-mile journey with insights from John Licata, chief innovation foresight strategist of SAP New Ventures and Technologies (NVT). Discover how technology meets humanity, ensuring accessibility and ethical use of AI for everyone.

New beginnings. They are all around us. Whether it’s birth, New Year’s Day, graduation, starting a business, or even retirement, new beginnings can be found in many aspects of life and human history.

In technology, some of that history was advanced in one way or another through the invention of the personal computer, the creation of the World Wide Web, smartphones, cloud computing, blockchain, and VR/AR. While each of those notable new beginnings has transformed many industries and our way of life, it’s important to note that they have had journeys that have evolved from the initial wave to the point of connecting consumers to specific tasks or goals.

Today, much like the World Wide Web making the Internet accessible to everyone in the 1970’s, we are seeing unbridled enthusiasm for Artificial Intelligence (AI). Similarly, AI is not new because it’s being made more accessible. There is more enthusiasm for the beginning of large language models. 

Just as the telecom sector developed infrastructure to deliver the internet to businesses and peoples’ homes, the “last mile of AI” holds a vast opportunity to transform our work. Let’s explore this. 

Success Relies on Data

It takes work to train large language models, develop embeddings, and find new ways to deliver content in multi-modal forms. But how can enterprises get the most out of these models and AI investments? How can business leaders maintain, without utmost confidence anyway, that they are using the right data at the right time or that it is relevant? How can we leverage AI to tackle human problems, maybe even problems we don’t know exist? 

These are all pertinent questions that the last mile of AI must focus on. These questions underline the importance of using trustworthy data while maintaining high sensitivity to security protocols meant to protect individual rights and remove bias. 

Let us not forget that training systems to deliver this type of data is not a one-time occurrence. These systems require consistent training as new data increasingly becomes available.

See More: The Future of AI in Mobile App Development

Cultivating AI-ready Talent

There is a tsunami of data forthcoming that humans need help navigating to be more effective in their roles. This is where humans can become augmented, not replaced. Of course, specific jobs will change due to advancements in AI. However, new jobs will emerge as companies seek new ways to leverage generative AI models. 

I’m not downplaying the potential for job loss. Still, I am hopeful we will see exciting new roles created because of deeper explorations of generation AI and its role in adding value for business users.  

I say this because I’m comforted by historical examples. History has shown time and time again that new jobs can emerge because of advancements in technology – a topic that often seems to be downplayed. 

Whether it is renewable energy, online commerce, cybersecurity, robotics, or advanced manufacturing, significant technological advancements have contributed to job creation. AI will be no different if employers can do more to boost timelier AI literacy training, reskilling, and upskilling, creating a progressive skills shift. This would create a healthy demand for positions such as AI specialists, prompt engineers, data scientists, AI developers, and content creators. 

According to McKinseyOpens a new window , generative AI features add up to $4.4 trillion to the global economy annually, so discovering new use cases should create new opportunities for humans. While this is fascinating, the training to use AI must be embraced now so humans can prepare to capitalize on these opportunities and benefit from the technology’s mainstream evolution.

This Is the Last Mile of AI

As large language models evolve and greater focus is allocated towards human-led training and re-training of models, we must stretch the lens of possibility by thinking of ways to boost human value and make data more actionable. 

 This differs from what’s readily available today in generative AI aimed primarily at B2C consumption. Therefore, the growing enterprise appetite for generative AI should create new employment opportunities. 

These opportunities should positively impact performance monitoring, change management, business alignment, and scaling needed for AI-driven solutions to become more integrated into the modern enterprise DNA. This will help enterprises adapt to handling increased workloads as businesses grow and more AI use cases emerge. 

As I investigate what’s next for generative AI, I’d be remiss not to mention that we simply don’t live in a one-size-fits-all world. This means large language models must be tailored, customized, and deployed through a trusted and secure open, ecosystem-friendly platform powered by emerging technologies with built-in business context enabling partners, customers, and individuals. 

This business platform must help humans make better decisions through personalized business workflows and more contextualized AI applications that leverage large language models in new ways. 

Thus, the last mile of AI, which unquestionably is a super challenging growth phase for AI, will pay greater attention to integrating AI into the enterprise ecosystem through the combination of structured data and the untapped value of unstructured data, which collectively can drive real impact and help bridge the gap between AI and real-world examples. 

These examples will show that the development of AI is a journey, and the last mile is needed to successfully navigate AI from technical experts to mainstream users regardless of their abilities so ALL people can benefit from the AI models that, in the beginning, were trained in principle to serve people somehow. 

AI is not new by any stretch of the imagination, but it is evolving into the mainstream right before our very eyes. This is why I’m so excited about the last mile of AI – helping people through technology and moving AI from architecture to architecture. Think of the last mile as the means to show the impact of AI in very tangible ways – like making complex AI models more accessible and a means to use clean data for human productivity. 

Imagine using AI to make better decisions, work more collaboratively, and drive tangible positive change that helps businesses be more ethical and responsible. In that sense, the last mile of AI is another new beginning where technology meets humanity. 

How can technology and humanity converge, making AI accessible and ethical? Let us know on FacebookOpens a new window , XOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

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