Will AI mean the end of call centres? Ask ChatGPT whether AI will replace humans in the customer service industry, and it will offer a diplomatic answer, the summary of which is they will work side by side. Humans though, are not so optimistic. Last year, the chief executive of Indian technology firm Tata Consultancy Services, K Krithivasan, told the Financial Times that AI may soon mean that there is minimal need for call centres in Asia. Meanwhile, AI will autonomously resolve 80% of common customer service issues by 2029, predicts business and technology research firm Gartner.

Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies have seeped into the operations of several top companies, and have enabled them to launch advanced products and services that drive their growth. Talent acquisition (TA) and HR companies too, have built their identities around these technologies, catering to companies and individuals through upskilling, hiring, exam proctoring and bias-free interviews. Gartner's Hype Cycle for Human Capital Management 2022 research note found that "AI drives automation of the recruitment process and provides decision-making support to TA professionals, hiring managers and candidates, during talent sourcing, screening, marketing, interview scheduling and onboarding." Other solutions such as chatbots and virtual assistants are also available, but according to Gartner's inquiry, AI-enabled sourcing and screening currently represents the most concentrated demand. InstaHyre, an AI-based hiring platform, is an example of how AI is used to screen and match candidates to suitable companies.

Nearly half of human resource leaders polled by consulting firm Gartner said they're in the process of formulating guidance on employees' use of OpenAI's artificial intelligence chatbot ChatGPT. What those policies will look like may end up varying widely. Some Wall Street firms, like Bank of America and Goldman Sachs Group, have banned the chatbot, while hedge fund giant Citadel has embraced it. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

For enterprises, chatbots such ChatGPT have the potential to automate mundane tasks or enhance complex communications, such as creating email sales campaigns, fixing computer code, or improving customer support. Research firm Gartner predicts that by 2025, the market for AI software will reach almost $134.8 A large part of that market will be chatbot technology, which uses artificial intelligence (AI) and natural language processing to respond to user queries. The human-like answers are in the form of prose; more sophisticated programs allow for follow-up questions and responses, and they can be modified for specific business purposes. In a report last week, Gartner spelled out possible uses for ChatGPT and its base language model GPT-3 (GPT 3.5 and 4 also exist), which can be customized.

According to the Gartner hype cycle, the least amount of time a product takes to hit the'plateau' of expectations is two years. The hot chatbot has shattered all records of a product lifecycle, going through all stages of the cycle within 3 months. Launched in November-end last year, ChatGPT has already been through the innovation trigger, inflated expectations, disillusionment, enlightenment, and is now reaching a mature period of measured expectations, leading to industry adoption. A contributing factor to this might be the bot's meteoric growth, which scaled to 10 million users within 40 days. For contrast, Instagram took almost a year to reach the same milestone.

Since its release in late November, ChatGPT has taken the world by storm. The chatbot's advanced AI abilities allow it to do tasks completely on its own, such as composing essays, emails and poems, writing and debugging code, and even passing exams. Now that a chatbot can do what humans do so well in a matter of seconds, what does that mean for our future? If you have had the chance to chat with the AI chatbot, you were probably impressed with how much it can understand and its ability to respond in a conversational manner. However, the chatbot is capable of doing much more, and its technical capabilities are tested every day.

AI will form a key component of cyber defense strategies in 2023, allowing companies to move to an entirely new approach to cybersecurity. Because of this, companies look to innovative tools to respond to threats and--even better--prevent them in the first place. Previously, Gartner outlined its top seven cybersecurity trends for last year. With each one, it becomes more apparent that humans will need the support of artificial intelligence and machine learning tools to stay ahead of the curve. These predictions for 2022 are becoming even more potent for this year.

When we started AI Expo Africa here in South Africa back in 2018, it would be fair to say the atmosphere was one of excitement with a fair degree of hype mixed with solid doses of reality. There was still talk of "AI Winters" and that adoption would be slow. Well, 5 years on, the landscape has radically changed. Tools and techniques that were once the exclusive domain of "the developer" are now freely accessible via zero cost platforms / apps / APIs allowing business users to leverage all kinds of AI related tech, be that AI generated presentations or logos, to art, videos, music and animations to name but a few. Even in the time we have been running the show, the creativity and use cases have exploded and it would be fair to say, we are now well into the AI Spring!

This is the third part of the blog series. The 1st blog focused on the maturity model and explained how the large monolith data warehouses were created. The 2nd blog focused on metadata driven development or "smart manufacturing" of data ecosystems. This 3rd blog will talk about reverse engineering or how existing data assets can be discovered to accelerate the development of new data products. Companies have increasing pressure to start addressing the data silos to reduce cost, improve agility & accelerate innovation, but they struggle to deliver value from their data assets. Many companies have hundreds of systems, containing thousands of databases hundreds of thousands of tables, millions of columns, and millions of lines of code across many different technologies. The starting point is a "data spaghetti" that nobody knows well.

"Each line of business is driving digital transformation in its own way," says Naveen Kamat, executive director and CTO of data and AI services at Kyndryl, an IT infrastructure services provider. "They are setting up their own apps in the cloud, which generate data daily. The enterprise data estate is becoming much, much bigger; it's becoming much more complex to manage." The insurance industry provides an example of today's data landscape complexity. One substantial challenge to good data management in insurance is a plethora of legacy systems built up over the years, says Ali Shahkarami, chief data officer at Allianz Global Corporate & Specialty (AGCS).