Welcome back to our series on the Impact and Future of AI. In our opening article, we tackled the challenges of the data fueling AI systems, the ongoing need to reinvent and readapt due to AI advancements, and the crucial role of AI in complementing human capabilities. We also highlighted the transformative role of tools like ChatGPT in software development, and considered how developers might take on new roles in an AI-driven world. If you missed it, you can catch up here.
In this follow-up, our experts Juan Franco, Juan Alfonso, Agustín Pisano, and Johnny Sosa present their insights on AI's future, reflecting their roles at Techie Talent. We’ll focus on more niche applications of AI, highlighting its influence in areas such as digital twins, product development, robotic process automation, data visualization, and the People function.
Our goal is to give you a clear understanding of AI's potential and challenges, drawing from our team's expertise. Let's continue our exploration of AI's ongoing impact.
Juan Franco, Product Manager
“Besides all the benefits we already know AI has, the impact on “digital twins” technology will be a game changer across companies, industries, and society itself.
A digital twin is a digital representation of a physical object, person, or process contextualized in a digital version of its environment. Digital twins can help to simulate real situations and their outcomes, ultimately allowing them to make better decisions, optimize resources, improve product quality, ensure compliance, and increase revenue.
The benefits of digital twin technology can be magnified exponentially when paired with AI, machine learning, and deep learning algorithms by adding a layer of cognitive decision-making. This layer allows the generation of futuristic digital scenarios to test any events (launching new products in the market, climate change, process updates, etc.) and compare them against historical patterns to identify improvement opportunities.
There are several potential use cases for AI-driven digital twins technology. Strategizing process workarounds to avoid downtime in a production line, streamline logistics and operations in retail, or plan an entirely new production line to ramp up vaccine production are just a few examples of the future of this promising technology.”
How do you envision the integration of AI in analyzing product metrics, especially in predicting and understanding user behavior in digital products, and how might this shape our product development strategies moving forward?
“In the Product field, AI can accelerate the time to value (and revenue) by predicting how the user will react to attempts to nudge them to take specific actions. A predictive analytics tool helps to test different scenarios to make a cost-effective decision with a higher probability of success.
The system uses machine learning to analyze historical data and build an algorithm, which is applied to current data to predict what will happen next.
While these predictions can’t foretell the future with 100 percent accuracy, they can reveal trends and patterns that offer you data-backed clues about the best way to accomplish goals, including conversions.
As use case examples, these tools can be used to experiment with how users' profiles will react to:
- Cross-sell and upsell: the algorithm may alert that gamers who buy gems in-game to level up also like to buy new items. This can be an opportunity to create a bundle for in-game power-ups when customers purchase a certain number of gems.
- Subscriptions: The algorithm can help determine whether users have a high, medium, or low likelihood of signing up for a monthly subscription. Information can be used to place users into three cohorts and tailor follow-up accordingly.
- Pricing: discover which user profiles are abandoning their shopping carts at which price, and take action to send a follow-up email with a discount offer.
While the math underlying predictive customer analytics may be complex, the process for creating a prediction doesn’t have to be; many tools, such as Amplitude and Kameloon, are already providing it.
The predictive analytics market is expected to grow to $41.5 billion by 2028. Companies that don’t start using these forecasting tools now will risk falling behind the competition.”
Juan Alfonso, Robotic Process Automation Lead
“While recent developments in AI have left us amazed and dazzled by their capabilities, we must not forget that this is akin to dipping our fingertips into a lake of endless possibilities. What we have witnessed so far is merely a small glimpse of what we can create in the future as humanity. It's even challenging to think about what will happen in 5 or 10 years because a product we couldn't even imagine might emerge tomorrow.
However, drawing from AI's infinite possibilities, I can envision at a macro level within our company that AI will enhance our capabilities in various ways. It will enable us to make more informed decisions, optimize processes, and deliver enhanced customer experiences. AI-powered systems will streamline our operations, increase efficiency, and allow us to deliver better products and services to our customers.
In the industry, AI will drive innovation and transform business models. It will enable the development of more advanced and intelligent products and services. AI-powered technologies such as machine learning, natural language processing, and computer vision will revolutionize not only tech but various sectors, including healthcare, finance, manufacturing, and transportation.
Beyond our company and industry, AI will change the way we live, how we communicate, how we work, the tools we use, and the things we can create. AI will become a customized copilot/assistant for everyone. Undoubtedly, it will reshape the job market, replacing and automating many roles, but thousands of new ones we can't imagine will emerge. It will be challenging for society to adapt and readapt to these changes constantly.”
How will advancements in AI enhance the capabilities of RPA in the near future?
“A commonly heard expression in the RPA field underscores that automation aims for robots to manage repetitive and low-value tasks, allowing humans to focus on genuinely significant challenges. RPA entails automating repetitive and rule-based tasks, but with AI, RPA can transcend mere automation. AI algorithms can be trained to identify patterns, comprehend unstructured data, and make intelligent decisions. This implies that RPA bots can handle more intricate tasks that demand cognitive abilities, such as natural language processing, sentiment analysis, and image recognition.
In conclusion, by integrating AI with RPA, organizations can attain higher levels of automation, tackle more complex tasks, and extract valuable insights from data. The synergy between AI and RPA harbors immense potential for transforming industries and propelling innovation. In the near future, Intelligent Process Automation (IPA) will undoubtedly become a more prevalent topic of discussion.
Agustin Pisano, Data Analyst & Business Intelligence Specialist
“Technology is advancing so rapidly that it's very uncertain to predict what will happen in the next five or ten years. Every day, we can see advancements related to AI in many fields and in every aspect of our daily lives. Food, health, transportation, travel, and how we meet new people, among others, are increasingly influenced by some kind of AI-generated algorithm. As for my role, what I see is that tools and languages related to data visualization are constantly being updated and transformed. Each update of these tools includes new features that change how we perform our tasks.
Some examples of how AI is transforming data visualization are:
- automated generation of charts based on a dataset;
- automated recommendations when choosing a chart type;
- generation of insights based on our existing work;
- detection of inconsistencies or errors in the datasets;
- recommendations on the design and look and feel of dashboards.
These continuous improvements in the products and languages we use for our tasks have two key aspects:
- We need to be constantly aware of these new changes. It takes time, but staying up to date with the latest trends is necessary.
- We need to understand how these changes can transform our daily lives. We shouldn't see it as a threat but as an opportunity to be more productive and use it to our advantage.
I believe this phenomenon is no longer a novelty or just a few science fiction news; it's becoming part of our daily lives. We don't need to be data scientists or experts to use it. More and more applications and software allow us to interact with AI without writing a single line of code. This will revolutionize how we work in IT. Programming languages will become much more accessible through artificial intelligence. We won't need to be technical for the software to understand what we want to achieve. I believe this will be key in people adapting to this new technology.
However, those who understand and truly master AI to make these kinds of changes in software development will be the most valued profiles in the market. On the other hand, knowing how to effectively use AI as a co-pilot can make us much more productive for ourselves and the companies and projects we work for.
In terms of data accuracy and decision-making, what potential benefits and challenges might AI bring to BI?
As I explained earlier, the AI-based models being generated are not only helping us create more effective and faster visualizations, but they are also understanding the data we work with. This means that AI engines can provide different types of opinions, recommendations, and results that can enrich data-driven decision-making. They can also provide tools to detect errors, inaccuracies, and data quality issues. Something that used to be a highly manual and meticulous task, such as data validation or QA, is becoming more agile and easier to verify through controls and tools provided by AI. This will leave more time for analyzing insights from the data, as we can increasingly trust their quality and won't waste as much time on preliminary validation tasks.
Furthermore, AI is helping us generate databases from unstructured data (such as images, sounds, etc.). This opens up a significant opportunity in terms of the data we can analyze and visualize, creating a much broader environment for decision-making.
The main challenges in these significant changes in how we work are:
- Understanding and mastering these new tools.
- Being open to incorporating these technologies to transform our processes and routines within projects.
- Leveraging them to increase our productivity, reach, and the quality of our deliverables.
- Communicating to our clients the benefits of incorporating these new functionalities into our projects.
Johnny Sosa, Head of People Experience
“In today's rapidly evolving digital landscape, the integration of artificial intelligence (AI) into the People function promises transformative benefits. However, assessing AI's potential risks and rewards is crucial as we venture into this new era.
One of the foremost concerns is the perpetuation of biases through AI algorithms. Studies have shown that biases in algorithmic decision-making can exacerbate racial and gender inequalities, mirroring and magnifying offline disparities. For instance, AI systems may unintentionally discriminate against certain groups, further deepening existing inequalities.
To mitigate this risk, decision-makers must advocate for locally relevant training data and analytical approaches that reflect the diversity of society. By developing AI systems that are sensitive to these nuances, we can take a step towards addressing digital inequalities and ensuring fairness in People processes.
Another critical risk associated with AI is data security. As AI systems handle sensitive personal information, the potential for data breaches and cyber threats looms large. It's vital to work closely with legal and business leaders to ensure responsible implementation, identifying and addressing any security vulnerabilities apparent in AI systems.
Moreover, there's a pressing need for global cooperation to establish norms and standards that safeguard against these biases. In the process of setting these standards, it's essential to include the perspectives of developing countries, ensuring that digital transformation benefits all, regardless of their capabilities.”
How are advancements in AI shaping the future of workforce reskilling, and what strategic shifts are companies making in their AI objectives, particularly in the People function?
The tendency is clear: McKinsey's recent report, "The State of AI in 2023: Generative AI's Breakout Year," reveals that leading companies are over three times more likely to commit to reskilling more than 30 percent of their workforce over the next three years due to AI adoption.
Companies are shifting their AI objectives away from cost reduction. Instead, they are focusing on more strategic applications of generative AI. This shift underscores the evolving role of AI in enhancing HR practices, including reducing bias in decision-making and influencing employee sentiment positively. AI offers immense potential in the People function. Conversational AI, for instance, can enhance self-service capabilities, automate onboarding, and streamline transactional interactions. AI can also act as a "co-pilot" in guiding employee careers in real time. HR professionals are freed from repetitive tasks, enabling them to focus on meaningful, engaging interactions with employees.
In conclusion, AI's fusion with the People function presents challenges and opportunities. The People area can create a more equitable, efficient, and secure workplace by embracing responsible AI practices. The key takeaway is that AI in the People function is not merely about automation and efficiency; it's about balancing risks and rewards, promoting inclusivity, and making strategic decisions that benefit organizations and employees alike.
