#enterprise-ai — Public Fediverse posts

Episode Recap: The Three Ingredients That Turn AI Into Value – Wondering what enterprise AI success really looks like? Sophie Dionnet explains that it’s not just tech but the right people, orchestrated workflows, and solid governance. Real‑world examples from Roche to APAC banking show why change management beats shiny models. Watch the full chat on YouTube https://youtu.be/Dt2ZvjxlRCg?ref=analysepodcast.com #AIValue #EnterpriseAI #AnalysePodcast

Episode Recap: The Three Ingredients That Turn AI Into Value – Wondering what enterprise AI success really looks like? Sophie Dionnet explains that it’s not just tech but the right people, orchestrated workflows, and solid governance. Real‑world examples from Roche to APAC banking show why change management beats shiny models. Watch the full chat on YouTube https://youtu.be/Dt2ZvjxlRCg?ref=analysepodcast.com #AIValue #EnterpriseAI #AnalysePodcast

We're joined by Sophie Dionnet from Dataiku for a fantastic discussion on how to unlock real AI value in enterprises. She breaks it down to three core ingredients.

Most AI failures aren't technology problems—they're change management problems. The hardest work is the transformation.

Watch the full conversation on YouTube:
https://youtu.be/Dt2ZvjxlRCg?ref=analysepodcast.com

#AI #EnterpriseAI #BusinessTransformation #AnalysePodcast

We're joined by Sophie Dionnet from Dataiku for a fantastic discussion on how to unlock real AI value in enterprises. She breaks it down to three core ingredients.

Most AI failures aren't technology problems—they're change management problems. The hardest work is the transformation.

Watch the full conversation on YouTube:
https://youtu.be/Dt2ZvjxlRCg?ref=analysepodcast.com

#AI #EnterpriseAI #BusinessTransformation #AnalysePodcast

Copilot saving time for individuals does not mean Copilot saves time for organisations

I thought this was a really interesting finding from Microsoft’s own research on Copilot 365 using usage data from 6000 trial licenses across 56 firms:

The introduction of Copilot has led to significant changes in email-related behaviors among workers. The average worker in the control group spent 2.8 hours each week reading email and licensees spent 12 fewer minutes reading emails each week, a 7% decrease. This time savings reflects a combination of fewer individual emails read each week, 9 fewer per week, and an increase in reading efficiency, as workers with Copilot spend a few seconds less reading each email on average. Copilot users spent 18% less time reading email, saving more than half an hour each week. We hypothesize that some combination of Outlook Summarize and searching for email content in M365 Copilot Chat allows users to spend less time with individual emails.

If individuals are saving time by reading fewer e-mails and reading existing e-mails more quickly, this plausibly increases the likelihood they will take action which causes more work to be accumulated downstream of the ‘saving’. The authors have an optimistic reading based on Copilot users replying to the same number of e-mails, but the point where ‘more effective triage’ becomes ‘inattention’ is rather ambiguous at best. This doesn’t mean that more time spent reading e-mail is necessarily good, only that less time spent on e-mail is plausibly going to create problems just as much as it represents an efficiency gain. If you just measure efficiency in terms of individualised effects then you’re just not going to pick up on the second-order consequences of AI-generated efficiency savings within organisations. The same point holds true for individuals making documents more quickly:

We expect that the generative capacities of M365 Copilot may help workers produce Word documents more quickly. That speed can have additional ripple effects on how people work. They may have time to contribute to more documents; they may finish rough drafts earlier and take the time to elicit feedback from more colleagues; alternatively, they may co-write less often with colleagues, using Copilot as a substitute writing partner.

This is an interesting finding but there’s absolutely no reason to assume it’s intrinsically a positive thing. People producing documents more quickly can just as easily mean the proliferation of ‘workslop’ in which low quality documents circulate which require more downstream work to address their deficiencies. The social costs only show up in this research design when it comes to meetings:

We looked at Copilot’s effect on the number of Teams meetings attended and total time spent in meetings each week. We find that receiving a Copilot license leads to small and statistically insignificant increases in both metrics, but also a (statistically significant) decrease in the proportion of scheduled meeting time during which people actually met, i.e., Copilot licensees are likelier to join meetings later and leave meetings earlier.

Again not all meetings are valuable. But if Copilot users are likelier to join meetings later and leave earlier it suggest that the AI is being leveraged to support personal time saving, on the assumption the automated features will protect against any determinant to the shared work. It’s not clear to me why this would be the case.

Copilot saving time for individuals does not mean Copilot saves time for organisations

I thought this was a really interesting finding from Microsoft’s own research on Copilot 365 using usage data from 6000 trial licenses across 56 firms:

The introduction of Copilot has led to significant changes in email-related behaviors among workers. The average worker in the control group spent 2.8 hours each week reading email and licensees spent 12 fewer minutes reading emails each week, a 7% decrease. This time savings reflects a combination of fewer individual emails read each week, 9 fewer per week, and an increase in reading efficiency, as workers with Copilot spend a few seconds less reading each email on average. Copilot users spent 18% less time reading email, saving more than half an hour each week. We hypothesize that some combination of Outlook Summarize and searching for email content in M365 Copilot Chat allows users to spend less time with individual emails.

If individuals are saving time by reading fewer e-mails and reading existing e-mails more quickly, this plausibly increases the likelihood they will take action which causes more work to be accumulated downstream of the ‘saving’. The authors have an optimistic reading based on Copilot users replying to the same number of e-mails, but the point where ‘more effective triage’ becomes ‘inattention’ is rather ambiguous at best. This doesn’t mean that more time spent reading e-mail is necessarily good, only that less time spent on e-mail is plausibly going to create problems just as much as it represents an efficiency gain. If you just measure efficiency in terms of individualised effects then you’re just not going to pick up on the second-order consequences of AI-generated efficiency savings within organisations. The same point holds true for individuals making documents more quickly:

We expect that the generative capacities of M365 Copilot may help workers produce Word documents more quickly. That speed can have additional ripple effects on how people work. They may have time to contribute to more documents; they may finish rough drafts earlier and take the time to elicit feedback from more colleagues; alternatively, they may co-write less often with colleagues, using Copilot as a substitute writing partner.

This is an interesting finding but there’s absolutely no reason to assume it’s intrinsically a positive thing. People producing documents more quickly can just as easily mean the proliferation of ‘workslop’ in which low quality documents circulate which require more downstream work to address their deficiencies. The social costs only show up in this research design when it comes to meetings:

We looked at Copilot’s effect on the number of Teams meetings attended and total time spent in meetings each week. We find that receiving a Copilot license leads to small and statistically insignificant increases in both metrics, but also a (statistically significant) decrease in the proportion of scheduled meeting time during which people actually met, i.e., Copilot licensees are likelier to join meetings later and leave meetings earlier.

Again not all meetings are valuable. But if Copilot users are likelier to join meetings later and leave earlier it suggest that the AI is being leveraged to support personal time saving, on the assumption the automated features will protect against any determinant to the shared work. It’s not clear to me why this would be the case.

Copilot saving time for individuals does not mean Copilot saves time for organisations

I thought this was a really interesting finding from Microsoft’s own research on Copilot 365 using usage data from 6000 trial licenses across 56 firms:

The introduction of Copilot has led to significant changes in email-related behaviors among workers. The average worker in the control group spent 2.8 hours each week reading email and licensees spent 12 fewer minutes reading emails each week, a 7% decrease. This time savings reflects a combination of fewer individual emails read each week, 9 fewer per week, and an increase in reading efficiency, as workers with Copilot spend a few seconds less reading each email on average. Copilot users spent 18% less time reading email, saving more than half an hour each week. We hypothesize that some combination of Outlook Summarize and searching for email content in M365 Copilot Chat allows users to spend less time with individual emails.

If individuals are saving time by reading fewer e-mails and reading existing e-mails more quickly, this plausibly increases the likelihood they will take action which causes more work to be accumulated downstream of the ‘saving’. The authors have an optimistic reading based on Copilot users replying to the same number of e-mails, but the point where ‘more effective triage’ becomes ‘inattention’ is rather ambiguous at best. This doesn’t mean that more time spent reading e-mail is necessarily good, only that less time spent on e-mail is plausibly going to create problems just as much as it represents an efficiency gain. If you just measure efficiency in terms of individualised effects then you’re just not going to pick up on the second-order consequences of AI-generated efficiency savings within organisations. The same point holds true for individuals making documents more quickly:

We expect that the generative capacities of M365 Copilot may help workers produce Word documents more quickly. That speed can have additional ripple effects on how people work. They may have time to contribute to more documents; they may finish rough drafts earlier and take the time to elicit feedback from more colleagues; alternatively, they may co-write less often with colleagues, using Copilot as a substitute writing partner.

This is an interesting finding but there’s absolutely no reason to assume it’s intrinsically a positive thing. People producing documents more quickly can just as easily mean the proliferation of ‘workslop’ in which low quality documents circulate which require more downstream work to address their deficiencies. The social costs only show up in this research design when it comes to meetings:

We looked at Copilot’s effect on the number of Teams meetings attended and total time spent in meetings each week. We find that receiving a Copilot license leads to small and statistically insignificant increases in both metrics, but also a (statistically significant) decrease in the proportion of scheduled meeting time during which people actually met, i.e., Copilot licensees are likelier to join meetings later and leave meetings earlier.

Again not all meetings are valuable. But if Copilot users are likelier to join meetings later and leave earlier it suggest that the AI is being leveraged to support personal time saving, on the assumption the automated features will protect against any determinant to the shared work. It’s not clear to me why this would be the case.

Copilot saving time for individuals does not mean Copilot saves time for organisations

I thought this was a really interesting finding from Microsoft’s own research on Copilot 365 using usage data from 6000 trial licenses across 56 firms:

The introduction of Copilot has led to significant changes in email-related behaviors among workers. The average worker in the control group spent 2.8 hours each week reading email and licensees spent 12 fewer minutes reading emails each week, a 7% decrease. This time savings reflects a combination of fewer individual emails read each week, 9 fewer per week, and an increase in reading efficiency, as workers with Copilot spend a few seconds less reading each email on average. Copilot users spent 18% less time reading email, saving more than half an hour each week. We hypothesize that some combination of Outlook Summarize and searching for email content in M365 Copilot Chat allows users to spend less time with individual emails.

If individuals are saving time by reading fewer e-mails and reading existing e-mails more quickly, this plausibly increases the likelihood they will take action which causes more work to be accumulated downstream of the ‘saving’. The authors have an optimistic reading based on Copilot users replying to the same number of e-mails, but the point where ‘more effective triage’ becomes ‘inattention’ is rather ambiguous at best. This doesn’t mean that more time spent reading e-mail is necessarily good, only that less time spent on e-mail is plausibly going to create problems just as much as it represents an efficiency gain. If you just measure efficiency in terms of individualised effects then you’re just not going to pick up on the second-order consequences of AI-generated efficiency savings within organisations. The same point holds true for individuals making documents more quickly:

We expect that the generative capacities of M365 Copilot may help workers produce Word documents more quickly. That speed can have additional ripple effects on how people work. They may have time to contribute to more documents; they may finish rough drafts earlier and take the time to elicit feedback from more colleagues; alternatively, they may co-write less often with colleagues, using Copilot as a substitute writing partner.

This is an interesting finding but there’s absolutely no reason to assume it’s intrinsically a positive thing. People producing documents more quickly can just as easily mean the proliferation of ‘workslop’ in which low quality documents circulate which require more downstream work to address their deficiencies. The social costs only show up in this research design when it comes to meetings:

We looked at Copilot’s effect on the number of Teams meetings attended and total time spent in meetings each week. We find that receiving a Copilot license leads to small and statistically insignificant increases in both metrics, but also a (statistically significant) decrease in the proportion of scheduled meeting time during which people actually met, i.e., Copilot licensees are likelier to join meetings later and leave meetings earlier.

Again not all meetings are valuable. But if Copilot users are likelier to join meetings later and leave earlier it suggest that the AI is being leveraged to support personal time saving, on the assumption the automated features will protect against any determinant to the shared work. It’s not clear to me why this would be the case.

Copilot saving time for individuals does not mean Copilot saves time for organisations

I thought this was a really interesting finding from Microsoft’s own research on Copilot 365 using usage data from 6000 trial licenses across 56 firms:

The introduction of Copilot has led to significant changes in email-related behaviors among workers. The average worker in the control group spent 2.8 hours each week reading email and licensees spent 12 fewer minutes reading emails each week, a 7% decrease. This time savings reflects a combination of fewer individual emails read each week, 9 fewer per week, and an increase in reading efficiency, as workers with Copilot spend a few seconds less reading each email on average. Copilot users spent 18% less time reading email, saving more than half an hour each week. We hypothesize that some combination of Outlook Summarize and searching for email content in M365 Copilot Chat allows users to spend less time with individual emails.

If individuals are saving time by reading fewer e-mails and reading existing e-mails more quickly, this plausibly increases the likelihood they will take action which causes more work to be accumulated downstream of the ‘saving’. The authors have an optimistic reading based on Copilot users replying to the same number of e-mails, but the point where ‘more effective triage’ becomes ‘inattention’ is rather ambiguous at best. This doesn’t mean that more time spent reading e-mail is necessarily good, only that less time spent on e-mail is plausibly going to create problems just as much as it represents an efficiency gain. If you just measure efficiency in terms of individualised effects then you’re just not going to pick up on the second-order consequences of AI-generated efficiency savings within organisations. The same point holds true for individuals making documents more quickly:

We expect that the generative capacities of M365 Copilot may help workers produce Word documents more quickly. That speed can have additional ripple effects on how people work. They may have time to contribute to more documents; they may finish rough drafts earlier and take the time to elicit feedback from more colleagues; alternatively, they may co-write less often with colleagues, using Copilot as a substitute writing partner.

This is an interesting finding but there’s absolutely no reason to assume it’s intrinsically a positive thing. People producing documents more quickly can just as easily mean the proliferation of ‘workslop’ in which low quality documents circulate which require more downstream work to address their deficiencies. The social costs only show up in this research design when it comes to meetings:

We looked at Copilot’s effect on the number of Teams meetings attended and total time spent in meetings each week. We find that receiving a Copilot license leads to small and statistically insignificant increases in both metrics, but also a (statistically significant) decrease in the proportion of scheduled meeting time during which people actually met, i.e., Copilot licensees are likelier to join meetings later and leave meetings earlier.

Again not all meetings are valuable. But if Copilot users are likelier to join meetings later and leave earlier it suggest that the AI is being leveraged to support personal time saving, on the assumption the automated features will protect against any determinant to the shared work. It’s not clear to me why this would be the case.

Most orgs chasing the latest AI model are missing the point entirely.

Sophie Dionnet from Dataiku puts it plainly: the transformation you need today doesn't require the newest model. It requires the hard, unglamorous work of change management.

The real trap? Preferring the new toy over actually putting it to use.

Watch on YouTube: https://youtube.com/shorts/1iSO634q-Lw

#EnterpriseAI #Dataiku #ChangeManagement

Most orgs chasing the latest AI model are missing the point entirely.

Sophie Dionnet from Dataiku puts it plainly: the transformation you need today doesn't require the newest model. It requires the hard, unglamorous work of change management.

The real trap? Preferring the new toy over actually putting it to use.

Watch on YouTube: https://youtube.com/shorts/1iSO634q-Lw

#EnterpriseAI #Dataiku #ChangeManagement

DraftKings reportedly built an AI model to identify and target users who are likely to lose money, raising questions about AI's role in gambling platforms. The story highlights how companies are using AI for customer targeting in the gaming industry. https://gizmodo.com/draftkings-built-an-ai-model-to-target-profitable-losers-report-claims-2000814536 #AIagent #AI #GenAI #EnterpriseAI

DraftKings reportedly built an AI model to identify and target users who are likely to lose money, raising questions about AI's role in gambling platforms. The story highlights how companies are using AI for customer targeting in the gaming industry. https://gizmodo.com/draftkings-built-an-ai-model-to-target-profitable-losers-report-claims-2000814536 #AIagent #AI #GenAI #EnterpriseAI

DraftKings reportedly built an AI model to identify and target users who are likely to lose money, raising questions about AI's role in gambling platforms. The story highlights how companies are using AI for customer targeting in the gaming industry. https://gizmodo.com/draftkings-built-an-ai-model-to-target-profitable-losers-report-claims-2000814536 #AIagent #AI #GenAI #EnterpriseAI

DraftKings reportedly built an AI model to identify and target users who are likely to lose money, raising questions about AI's role in gambling platforms. The story highlights how companies are using AI for customer targeting in the gaming industry. https://gizmodo.com/draftkings-built-an-ai-model-to-target-profitable-losers-report-claims-2000814536 #AIagent #AI #GenAI #EnterpriseAI

DraftKings reportedly built an AI model to identify and target users who are likely to lose money, raising questions about AI's role in gambling platforms. The story highlights how companies are using AI for customer targeting in the gaming industry. https://gizmodo.com/draftkings-built-an-ai-model-to-target-profitable-losers-report-claims-2000814536 #AIagent #AI #GenAI #EnterpriseAI

Salesforce is considering charging for AI outcomes. The company is devising a new pricing structure. Traditional licensing is dragging on growth.

Source: The Register
https://www.theregister.com/software/2026/09/21/salesforce-wants-to-charge-for-ai-outcomes-but-first-it-needs-to-figure-out-how/5297402

#EnterpriseAI #Salesforce

Salesforce is considering charging for AI outcomes. The company is devising a new pricing structure. Traditional licensing is dragging on growth.

Source: The Register
https://www.theregister.com/software/2026/09/21/salesforce-wants-to-charge-for-ai-outcomes-but-first-it-needs-to-figure-out-how/5297402

#EnterpriseAI #Salesforce

AI isn’t magic. In our conversation with Sophie Dionnet (Dataiku), she cuts to the real enterprise AI trap: not models, but change management.

“Taking a decision is one hour, implementation is two years.”

That gap is where AI succeeds or stalls.

Watch on YouTube: https://youtube.com/shorts/Rh2kZ4P_ib4

#EnterpriseAI #ChangeManagement #Dataiku #AI

AI isn’t magic. In our conversation with Sophie Dionnet (Dataiku), she cuts to the real enterprise AI trap: not models, but change management.

“Taking a decision is one hour, implementation is two years.”

That gap is where AI succeeds or stalls.

Watch on YouTube: https://youtube.com/shorts/Rh2kZ4P_ib4

#EnterpriseAI #ChangeManagement #Dataiku #AI

Anthropic Mulls New AI Model Amid Investors’ Pre-IPO Worries https://www.byteseu.com/2383806/ #AI #AISafety #Anthropic #ArtificialIntelligence #EnterpriseAI #InitialPublicOffering #IPO #News #OpenAI #PYMNTSNews #What'sHot

Anthropic Weighs New Model Launch to Counter GPT-6 Astra

Reuters reports Anthropic may release a new model before its IPO, days after CEO Dario Amodei called for the industry to slow down.

https://pulseofnations.lol/anthropic-weighs-new/

#Anthropic #Claude #EnterpriseAi #Gpt6 #Ipo #OpenAI

Infinito.Nexus 14.0: Turn Local AI into a Secure Digital Workforce

https://blog.infinito.nexus/blog/2026/09/19/infinito-nexus-14-0-turn-local-ai-into-a-secure-digital-workforce/

Infinito.Nexus 14.0: Turn Local AI into a Secure Digital Workforce

https://blog.infinito.nexus/blog/2026/09/19/infinito-nexus-14-0-turn-local-ai-into-a-secure-digital-workforce/

Episode Recap: The Three Ingredients That Turn AI Into Value – we ask what enterprise AI success really looks like. Sophie Dionnet says it’s people, orchestration and governance, not just flash tools. She shares how a Roche patent lawyer built his own agents and why controls accelerate scale. Listen on Spotify 👉 https://open.spotify.com/episode/5VHvVxpF3IeM0bNLpuDYFN?si=YPFY_zumTT-Z8h7zO-l8_g&ref=analysepodcast.com #EnterpriseAI #AnalysePodcast

Episode Recap: The Three Ingredients That Turn AI Into Value – we ask what enterprise AI success really looks like. Sophie Dionnet says it’s people, orchestration and governance, not just flash tools. She shares how a Roche patent lawyer built his own agents and why controls accelerate scale. Listen on Spotify 👉 https://open.spotify.com/episode/5VHvVxpF3IeM0bNLpuDYFN?si=YPFY_zumTT-Z8h7zO-l8_g&ref=analysepodcast.com #EnterpriseAI #AnalysePodcast

Kai-Fu Lee forecasts that AI will unleash radical changes inside companies. Lee says CEOs must take personal charge of their technology strategy.

Source: Semafor Africa
https://www.semafor.com/article/09/18/2026/why-kai-fu-lee-thinks-companies-need-an-ai-boss

#EnterpriseAI

Kai-Fu Lee forecasts that AI will unleash radical changes inside companies. Lee says CEOs must take personal charge of their technology strategy.

Source: Semafor Africa
https://www.semafor.com/article/09/18/2026/why-kai-fu-lee-thinks-companies-need-an-ai-boss

#EnterpriseAI

Sophie Dionnet of Dataiku had a great point on today’s episode: the real AI problem isn’t the model — it’s the change. It's easier to chase the new toy than to do the hard work. The Harsh Truth About the Enterprise AI "Gold Rush" is out now. Watch on YouTube and share your thoughts below.

https://youtube.com/shorts/-Zg9XEgq2bQ
#AnalysePodcast #AIDiscussion #EnterpriseAI

Sophie Dionnet of Dataiku had a great point on today’s episode: the real AI problem isn’t the model — it’s the change. It's easier to chase the new toy than to do the hard work. The Harsh Truth About the Enterprise AI "Gold Rush" is out now. Watch on YouTube and share your thoughts below.

https://youtube.com/shorts/-Zg9XEgq2bQ
#AnalysePodcast #AIDiscussion #EnterpriseAI

Measuring "useful intelligence per dollar" is a far better metric for AI than tracking raw cost per token, but it still carries a critical flaw: you don't control the pricing.

If your AI ROI relies on today's API rates, you are betting on someone else's infrastructure costs and margins remaining favorable. That leaves many business cases vulnerable to sudden market re-pricing.

Read the full analysis on TechFinitive:
https://www.techfinitive.com/opinions/your-ai-roi-is-built-on-a-price-you-dont-control/

#AI #EnterpriseAI #ROI

Measuring "useful intelligence per dollar" is a far better metric for AI than tracking raw cost per token, but it still carries a critical flaw: you don't control the pricing.

If your AI ROI relies on today's API rates, you are betting on someone else's infrastructure costs and margins remaining favorable. That leaves many business cases vulnerable to sudden market re-pricing.

Read the full analysis on TechFinitive:
https://www.techfinitive.com/opinions/your-ai-roi-is-built-on-a-price-you-dont-control/

#AI #EnterpriseAI #ROI

Measuring "useful intelligence per dollar" is a far better metric for AI than tracking raw cost per token, but it still carries a critical flaw: you don't control the pricing.

If your AI ROI relies on today's API rates, you are betting on someone else's infrastructure costs and margins remaining favorable. That leaves many business cases vulnerable to sudden market re-pricing.

Read the full analysis on TechFinitive:
https://www.techfinitive.com/opinions/your-ai-roi-is-built-on-a-price-you-dont-control/

#AI #EnterpriseAI #ROI

Measuring "useful intelligence per dollar" is a far better metric for AI than tracking raw cost per token, but it still carries a critical flaw: you don't control the pricing.

If your AI ROI relies on today's API rates, you are betting on someone else's infrastructure costs and margins remaining favorable. That leaves many business cases vulnerable to sudden market re-pricing.

Read the full analysis on TechFinitive:
https://www.techfinitive.com/opinions/your-ai-roi-is-built-on-a-price-you-dont-control/

#AI #EnterpriseAI #ROI

Measuring "useful intelligence per dollar" is a far better metric for AI than tracking raw cost per token, but it still carries a critical flaw: you don't control the pricing.

If your AI ROI relies on today's API rates, you are betting on someone else's infrastructure costs and margins remaining favorable. That leaves many business cases vulnerable to sudden market re-pricing.

Read the full analysis on TechFinitive:
https://www.techfinitive.com/opinions/your-ai-roi-is-built-on-a-price-you-dont-control/

#AI #EnterpriseAI #ROI

Ever wondered why some companies can't scale their AI efforts? In this episode, Sophie Dionnet explains that tight, centralized AI governance often hinders growth. Trust and the right controls are key to scaling. Watch on YouTube to learn how organizations can foster the right governance environment. https://youtube.com/shorts/tnZRW8u5vDQ #dataiku #aigovernance #ai #enterpriseai #llmsecurity

Ever wondered why some companies can't scale their AI efforts? In this episode, Sophie Dionnet explains that tight, centralized AI governance often hinders growth. Trust and the right controls are key to scaling. Watch on YouTube to learn how organizations can foster the right governance environment. https://youtube.com/shorts/tnZRW8u5vDQ #dataiku #aigovernance #ai #enterpriseai #llmsecurity

Organisations already possess most of the answers employees need. The problem is finding them.

Could one trustworthy corporate AI assistant connect scattered systems, documents and expertise—without compromising security or governance?

https://hackernoon.com/the-enterprise-already-knows-the-answer-ai-can-help-employees-find-it

#EnterpriseAI #KnowledgeManagement #TTMO

Databricks plans to invest more than US$350 million in Singapore over the next three years. The company will double its local workforce and open a new regional headquarters.

Source: Fintech News Singapore
https://fintechnews.sg/137356/ai/databricks-singapore-investment-350-million/

#EnterpriseAI

Databricks plans to invest more than US$350 million in Singapore over the next three years. The company will double its local workforce and open a new regional headquarters.

Source: Fintech News Singapore
https://fintechnews.sg/137356/ai/databricks-singapore-investment-350-million/

#EnterpriseAI

TotalEnergies partners with AI firm Mistral to speed up exploration by building AI models that analyze geological data and generate exploration scenarios.

Source: Ecofin Agency
http://www.ecofinagency.com/news-digital/1609-58939-french-oil-major-totalenergies-partners-with-ai-firm-mistral-to-speed-up-exploration

#EnterpriseAI #Mistral

TotalEnergies partners with AI firm Mistral to speed up exploration by building AI models that analyze geological data and generate exploration scenarios.

Source: Ecofin Agency
http://www.ecofinagency.com/news-digital/1609-58939-french-oil-major-totalenergies-partners-with-ai-firm-mistral-to-speed-up-exploration

#EnterpriseAI #Mistral

Sumsub has launched a workforce verification solution that adds identity document checks, biometric liveness detection and background screening to existing IAM and HR platforms.

Source: Fintech News Singapore
https://fintechnews.sg/137348/security/sumsub-workforce-verification-ai-fraud/

#EnterpriseAI

Sumsub has launched a workforce verification solution that adds identity document checks, biometric liveness detection and background screening to existing IAM and HR platforms.

Source: Fintech News Singapore
https://fintechnews.sg/137348/security/sumsub-workforce-verification-ai-fraud/

#EnterpriseAI

OpenAI CEO Sam Altman said the recent Hugging Face hack shows the need for enterprise AI defence solutions.

Source: The Hindu Science and Technology
https://www.thehindu.com/sci-tech/technology/after-hugging-face-hack-openai-ceo-sam-altman-bats-for-enterprise-ai-defence-solutions/article71471146.ece

#EnterpriseAI #HuggingFace #OpenAI

OpenAI CEO Sam Altman said the recent Hugging Face hack shows the need for enterprise AI defence solutions.

Source: The Hindu Science and Technology
https://www.thehindu.com/sci-tech/technology/after-hugging-face-hack-openai-ceo-sam-altman-bats-for-enterprise-ai-defence-solutions/article71471146.ece

#EnterpriseAI #HuggingFace #OpenAI

"The real challenge isn't the latest model, but the change management. It’s easier to get excited by the new toy than to actually use it." – Sophie Dionnet of @Dataiku.

Spot-on thoughts on the enterprise #AI 'gold rush' and doing the hard work. Where do you think this is heading?

Watch our full chat on YouTube: https://youtube.com/shorts/-Zg9XEgq2bQ
#EnterpriseAI #ChangeManagement #AnalysePodcast

"The real challenge isn't the latest model, but the change management. It’s easier to get excited by the new toy than to actually use it." – Sophie Dionnet of @Dataiku.

Spot-on thoughts on the enterprise #AI 'gold rush' and doing the hard work. Where do you think this is heading?

Watch our full chat on YouTube: https://youtube.com/shorts/-Zg9XEgq2bQ
#EnterpriseAI #ChangeManagement #AnalysePodcast

Consistency is the floor. Accountability goes further.

Building AI for high-stakes, regulated environments?

Prompt logs & model versions aren’t enough. True auditability requires explaining why an input produced an output - and tracing it directly to policy.

🎬 Watch Alex Porcelli’s full talk on #InfoQ - https://www.infoq.com/presentations/decision-models-agentic-ai/

#AI #AgenticAI #EnterpriseAI #SoftwareArchitecture

Consistency is the floor. Accountability goes further.

Building AI for high-stakes, regulated environments?

Prompt logs & model versions aren’t enough. True auditability requires explaining why an input produced an output - and tracing it directly to policy.

🎬 Watch Alex Porcelli’s full talk on #InfoQ - https://www.infoq.com/presentations/decision-models-agentic-ai/

#AI #AgenticAI #EnterpriseAI #SoftwareArchitecture

Consistency is the floor. Accountability goes further.

Building AI for high-stakes, regulated environments?

Prompt logs & model versions aren’t enough. True auditability requires explaining why an input produced an output - and tracing it directly to policy.

🎬 Watch Alex Porcelli’s full talk on #InfoQ - https://www.infoq.com/presentations/decision-models-agentic-ai/

#AI #AgenticAI #EnterpriseAI #SoftwareArchitecture

Consistency is the floor. Accountability goes further.

Building AI for high-stakes, regulated environments?

Prompt logs & model versions aren’t enough. True auditability requires explaining why an input produced an output - and tracing it directly to policy.

🎬 Watch Alex Porcelli’s full talk on #InfoQ - https://www.infoq.com/presentations/decision-models-agentic-ai/

#AI #AgenticAI #EnterpriseAI #SoftwareArchitecture

Consistency is the floor. Accountability goes further.

Building AI for high-stakes, regulated environments?

Prompt logs & model versions aren’t enough. True auditability requires explaining why an input produced an output - and tracing it directly to policy.

🎬 Watch Alex Porcelli’s full talk on #InfoQ - https://www.infoq.com/presentations/decision-models-agentic-ai/

#AI #AgenticAI #EnterpriseAI #SoftwareArchitecture

The real problem with enterprise data? Not multiple definitions - pretending there’s only one.

If Marketing and Finance can’t agree on what an “active customer” is, how can an AI agent?

Enterprise AI agents don’t fail because models are dumb. They fail because enterprise semantics are messy!

🔗 Watch Fabiane Nardon’s full #QConAI Boston talk: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/

#AI #DataEngineering #EnterpriseAI #AIAgents #InfoQ

The real problem with enterprise data? Not multiple definitions - pretending there’s only one.

If Marketing and Finance can’t agree on what an “active customer” is, how can an AI agent?

Enterprise AI agents don’t fail because models are dumb. They fail because enterprise semantics are messy!

🔗 Watch Fabiane Nardon’s full #QConAI Boston talk: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/

#AI #DataEngineering #EnterpriseAI #AIAgents #InfoQ

The real problem with enterprise data? Not multiple definitions - pretending there’s only one.

If Marketing and Finance can’t agree on what an “active customer” is, how can an AI agent?

Enterprise AI agents don’t fail because models are dumb. They fail because enterprise semantics are messy!

🔗 Watch Fabiane Nardon’s full #QConAI Boston talk: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/

#AI #DataEngineering #EnterpriseAI #AIAgents #InfoQ

The real problem with enterprise data? Not multiple definitions - pretending there’s only one.

If Marketing and Finance can’t agree on what an “active customer” is, how can an AI agent?

Enterprise AI agents don’t fail because models are dumb. They fail because enterprise semantics are messy!

🔗 Watch Fabiane Nardon’s full #QConAI Boston talk: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/

#AI #DataEngineering #EnterpriseAI #AIAgents #InfoQ

The real problem with enterprise data? Not multiple definitions - pretending there’s only one.

If Marketing and Finance can’t agree on what an “active customer” is, how can an AI agent?

Enterprise AI agents don’t fail because models are dumb. They fail because enterprise semantics are messy!

🔗 Watch Fabiane Nardon’s full #QConAI Boston talk: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/

#AI #DataEngineering #EnterpriseAI #AIAgents #InfoQ

AI is getting cheaper, but enterprise spending keeps rising. An AI PM's take on tokenomics, model routing, and why the cheapest token isn't the safest choice. https://hackernoon.com/why-regulated-enterprises-need-risk-tiered-model-routing #enterpriseai

AI is getting cheaper, but enterprise spending keeps rising. An AI PM's take on tokenomics, model routing, and why the cheapest token isn't the safest choice. https://hackernoon.com/why-regulated-enterprises-need-risk-tiered-model-routing #enterpriseai