Dramatic shifts in how we approach and conceptualize work are always met with an initial tidal wave of skepticism and detractors. This is a good thing, even if those initial detractors may turn out to be wrong. Thinking critically about the tools we deploy and how we manage our workforce is paramount.

In today’s competitive business environment, data-driven decision-making is necessary for sustainable success. Companies are leveraging data to gain insights, improve efficiency and increase profitability. Data-driven decision-making enables businesses to understand customer needs, target the right markets, develop better products and services, and optimize operations.

Let’s dispense with this myth right away. If you’re a Product Manager and you’re worried that the new wave of “generative AI” tools is coming for your career, you can relax. Yes, software programs like ChatGPT can analyze gazillions of data points and even produce some clever insights. But successful product management is still very much a human profession that requires empathy, lived experience, and a passion for innovation and problem-solving. Can a Tesla drive itself?

In the face of AI disruption, teams that embrace a growth mindset will fare better than those who cling to the status quo.

Thinking about the impact of artificial intelligence (AI) on the job market, are you on the apocalyptic or utopian side? Maybe something in between? In any case, one point is certain: artificial intelligence is becoming more and more developed. And the question still is, will AI replace jobs? The launch of ChatGPT has sparked several discussions, including the effect AI will have on employment. In some careers, it can improve efficiency.

One of the most important factors of customer satisfaction is the quality of the interaction between service desk agents and users. At InvGate, we know this is key, and we’re developing new AI-powered response suggestions to empower agents’ capabilities further. Users expect empathic-written responses that solve their requests in the shortest period. Meanwhile, agents are required to solve hundreds of tickets every day, following defined best practices and according to SLAs.