We keep our ear to the ground for the interesting stats, insights and discussion points you need to feel in the know to shape the future with confidence.
AI may run on code, but getting people to actually use it takes a lot more. Good data, clear rules, human oversight, the right skills and partners all have to work together. That gets even more important as AI moves out of pilot mode and into healthcare, education, humanitarian aid and other parts of everyday life. You can build the smartest model in the world, but if people don’t understand how it works, trust how their data is used or feel represented by it, adoption can stall. Turns out, scaling AI is as much about the ecosystem around the technology as the technology itself.
Trust isn’t coded – it must be designed into the ecosystem
Blockchain payments are ready to get down to business. Faster cross-border payments and smoother cash flow are giving companies reasons to use them now. And banks have a head start: 63% of corporates would likely adopt blockchain payments from their current provider. But familiarity only gets them so far. Clients want these services woven into systems they already use, while just 9% of financial institutions plan to build everything themselves. Partnerships will matter. As the rails fall into place, programmable payments could push the opportunity further, automating more of the work around moving and managing money.
Seven signals shaping the next phase of blockchain payments
Regulators want insurers to show their work: the numbers add up, the risks are accounted for and the business has enough capital behind it. Fair enough. What’s less reasonable is how much time highly skilled finance and actuarial teams still spend wrestling with spreadsheets, reconciliations and repetitive checks to make that happen. As reporting requirements pile up, AI and automation offer a chance to hand more of the grunt work to machines and give people back the work that actually needs a human brain. There are better uses for an actuary than checking another spreadsheet.
Why regulatory reporting has become a CFO-led operating model decision
Trust in AI is getting harder to read. As AI spreads through businesses, certifications and trust marks are following close behind. They may look similar at first glance, but the level of assurance behind them can vary. That matters when boards, customers and regulators want confidence that AI is being governed responsibly. Even with formal policies in place, urgent deployments can sometimes move faster than governance processes. A label can offer reassurance, but understanding what sits behind it matters more. As AI scales, being able to clearly explain how trust is earned could become just as important as the technology itself.
The rise of AI trust signals: what leaders need to know
India can’t just flip the switch on its energy future. Factories still need dependable power. Heavy transport still runs largely on liquid fuels, while renewables need stronger grids behind them. So old and new energy will be sharing the load for a while. Move too quickly and reliability could wobble. Hang back and growth could go elsewhere. Technology can make the mix smarter, but there’s no neat handover. India has to keep the lights on while rewiring what powers them.
Powering growth in India’s evolving energy ecosystem
Ask what really earns your trust in AI.
