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Robotic Automations

Building a viable pricing model for generative AI features could be challenging | TechCrunch


In October, Box unveiled a new pricing approach for the company’s generative AI features. Instead of a flat rate, the company designed a unique consumption-based model.

Each user gets 20 credits per month, good for any number of AI tasks that add up to 20 events, with each task charged a single credit. After that, people can dip into a company pool of 2,000 additional credits. If the customer surpasses that, it would be time to have a conversation with a salesperson about buying additional credits.

Box CEO Aaron Levie explained that this approach provides a way to charge based on usage with the understanding that some users would take advantage of the AI features more than others, while also accounting for the cost of using the OpenAI API, which the company is using for its underlying large language model.

Meanwhile, Microsoft has chosen a more traditional pricing model, announcing in November that it would charge $30 per user per month to use its Copilot features, over and above the cost of a normal monthly Office 365 subscription, which varies by customer.

While it became clear throughout last year that enterprise software companies would be building generative AI features, at a panel on generative AI’s impact on SaaS companies at Web Summit in November, Christine Spang, co-founder and CTO at Nylas, a communications API startup, and Manny Medina, CEO at sales enablement platform Outreach, spoke about the challenges that SaaS companies face as they implement these features.

Spang says, for starters, that in spite of the hype, generative AI is clearly a big leap forward, and software companies need to look for ways to incorporate it into their products. “I’m not going to say it’s like 10 out of 10 where the hype meets the [current] reality, but I do think there is real value there and what’s really going to make the difference is how people take the technology and connect it to other systems, other apps and sort of drive real value in different use cases with it,” she said.


Software Development in Sri Lanka

Robotic Automations

As AI becomes standard, watch for these 4 DevSecOps trends | TechCrunch


AI’s role in software development is reaching a pivotal moment — one that will compel organizations and their DevSecOps leaders to be more proactive in advocating for effective and responsible AI utilization.

Simultaneously, developers and the wider DevSecOps community must prepare to address four global trends in AI: the increased use of AI in code testing, ongoing threats to IP ownership and privacy, a rise in AI bias, and — despite all of these challenges — an increased reliance on AI technologies. Successfully aligning with these trends will position organizations and DevSecOps teams for success. Ignoring them could stifle innovation or, worse, derail your business strategy.

From luxury to standard: Organizations will embrace AI across the board

Integrating AI will become standard, not a luxury, across all industries of products and services, leveraging DevSecOps to build AI functionality alongside the software that will leverage it. Harnessing AI to drive innovation and deliver enhanced customer value will be critical to staying competitive in the AI-driven marketplace.

From my conversations with GitLab customers and monitoring industry trends, with organizations pushing the boundaries of efficiency through AI adoption, more than two-thirds of businesses will embed AI capabilities within their offerings by the end of 2024. Organizations are evolving from experimenting with AI to becoming AI-centric.

Harnessing AI to drive innovation and deliver enhanced customer value will be critical to staying competitive in the AI-driven marketplace.

To prepare, organizations must invest in revising software development governance and emphasizing continuous learning and adaptation in AI technologies. This will require a cultural and strategic shift. It demands rethinking business processes, product development, and customer engagement strategies. And it requires training — which DevSecOps teams say they want and need. In our latest Global DevSecOps Report, 81% of respondents said they would like more training on how to use AI effectively.

As AI becomes more sophisticated and integral to business operations, companies will need to navigate the ethical implications and societal impacts of their AI-driven solutions, ensuring that they contribute positively to their customers and communities.

AI will dominate code-testing workflows




Software Development in Sri Lanka

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