According to a report by Bloomberg, the upgrade will arrive with iOS 27, iPadOS 27 and macOS 27, expected later this year.

People familiar with the plans said the feature will allow Siri to process multi-step commands in a single query, instead of handling them one at a time.

Right now, Siri responds to one instruction per request. That has left it trailing competitors that can manage more complex tasks in a single interaction. With this change, a user could ask Siri to get directions and share them with a contact in one sentence.

Apple is also working on a comprehensive redesign of Siri. The company is said to be building a more advanced version of the assistant using technology linked to Alphabet Inc.’s Gemini model. Apple has not responded to requests for comment.

The upgrade is expected to feature at the Worldwide Developers Conference on June 8, 2026, where Apple usually previews its next software updates.

Beyond handling multiple requests, Apple is testing a new Siri app with both voice and text input. Users may also be able to revisit past conversations, a feature already common with tools like ChatGPT.

There are also plans for an “Extensions” system that would allow third-party services to plug directly into Siri.

At the same time, Apple is looking at opening Siri to other AI providers. Reports say users could choose between different assistants, including those from Anthropic, alongside existing integrations.

This changes the tech giant’s approach. Apple’s earlier Apple Intelligence rollout in 2024 did not gain strong traction, and the company has been under pressure to close the gap with competing systems.

Internally, the project to overhaul Siri into a full chatbot is said to carry the codename “Campos”. The plan is to embed it across the iPhone, iPad and Mac, replacing the current interface with something more interactive and capable.

The post Apple Tests Smarter Siri With Multi-Request Feature Ahead of iOS 27 Launch appeared first on Tech | Business | Economy.

It was the result of several innovations inside the app economy that finally lined up at once.

The $85 billion spent globally on non-game apps last year did not come from a surge in new users, because we see that downloads across mobile are largely reduced. 

But then, time spent has stabilised. So what changed was how people pay, and why they keep coming back.

Building habits

For years, while reach or downloads were used to describe how successful mobile apps were, games thrived because they could attract millions of casual players, monetise a small fraction of them, and repeat the cycle. Non-game apps didn’t match that efficiency before 2025.

That gap has now closed. Generative AI apps flipped the model and instead of focusing on new installs, they focused on becoming useful enough to open daily, sometimes dozens of times a day. 

The result is visible in Sensor Tower’s latest State of Mobile findings, which show global app spending rising 21% year-on-year. Sessions in AI apps crossed one trillion in 2025, growing faster than downloads. That tells us engagement is now the main engine.

This is a shift from scale-first to habit-first design.

Why AI assistants won, not just AI tools

Not all AI apps benefited equally. Assistants took over because they helped with multiple needs. Writing, search, coding help, image creation, planning, all in one place. That breadth reduced churn and increased willingness to pay.

ChatGPT’s $3.4 billion in in-app revenue is less important than how quickly it got there. No app has crossed $3 billion in annual consumer spending this fast. 

That speed is commendable because it shows that users accepted subscriptions and premium tiers without years of conditioning.

Others followed, with Google, Microsoft and X not just building similar features, but embedding assistants into daily workflows. Image and video generation became turning points, not side features. Once users could create, not just ask, time spent jumped.

Big tech’s return reshaped the field

Early AI growth came from smaller, fast-moving developers. That phase is over.

By 2025, OpenAI and DeepSeek controlled nearly half of all AI app downloads. Large technology firms expanded speedily, taking close to a third of the market. Together, they crowded out earlier competitors who lacked capital, distribution, or ecosystem access.

This concentration shows that AI on mobile is entering a maturity phase faster than previous app categories. Winners are pulling away early, leaving limited room for mid-tier challengers.

Mobile became the default AI gateway

One of the most underappreciated findings in the data is where AI usage happens.

More than half of AI assistant users in the United States now access these services only on mobile. A year earlier, that group barely existed. Phones are no longer secondary screens for AI, they are the main ones.

This has implications beyond apps. It explains why voice, camera input, and real-time image generation are advancing so quickly. Mobile limitations forced AI products to become faster, simpler, and more responsive.

Games did not collapse, they were overtaken

It is tempting to describe this as a loss for gaming. It isn’t.

Games still generate enormous revenue and attention. But their growth has slowed as user acquisition costs rose and playtime competed with social media, streaming, and now AI. Meanwhile, non-game apps learned how to monetise without friction.

Subscriptions, tiered access, and clear value exchanges worked. Users paid because they understood what they were getting back; saved time, better output, or creative control.

What this means for 2026

The mobile market has entered a monetisation-first era. Growth will not come from more downloads but from better use, clearer value, and products that are used in daily routines.

AI went beyond adding a new category to resetting expectations across the app ecosystem. Productivity, creativity, and even entertainment apps are now judged by how quickly they produce results, not how long they keep users scrolling.

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With great functionalities, Scout and Maverick arrive at a time when the world is drowning in chatbots that mostly sound the same. Meta’s new duo is different—and the differences are technical, strategic, and very human.

Let’s start with the basics. Llama 4 Scout is a 17-billion active parameter Mixture-of-Experts (MoE) model. It’s built with 16 experts, and it stretches context memory to 10 million tokens. That’s not a typo. 

While others are stuck in the 128K lane, Scout is processing entire libraries of context at once—ten million tokens is enough to summarise a hundred books without breaking a sweat. It has already outperformed rivals like Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across multiple benchmarks.

Maverick, on the other hand, is its flashier sibling—same 17B active parameters, but with 128 experts working behind the scenes. It’s a huge innovation in image-text grounding, outshining GPT-4o and Gemini 2.0 Flash, and holding its own against DeepSeek v3 on complex tasks like reasoning and coding—all while using fewer active parameters. 

According to Meta, its chat version scored an ELO of 1417 on LMArena, a benchmark that pits models head-to-head in user-voted matchups.

What Makes These Models So Good?

The real trick lies in what’s behind Scout and Maverick: a still-in-training model called Llama 4 Behemoth. It has 288 billion active parameters and 16 experts. Meta hasn’t released it yet, but it’s already beating GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro in STEM-focused benchmarks. 

That’s what’s powering the distilled intelligence in the smaller models—and that distillation process is what makes them unusually sharp for their size.

Scout and Maverick don’t just spit out answers. They understand multimodal inputs. They interpret long text chains, images, and even videos with surprising fluency. 

This was made possible by a redesigned architecture that fuses text and visual tokens early in the process, letting the model “think” about them together rather than switching back and forth. The result is a far more fluid, natural performance in tasks that involve both reading and seeing.

Meta’s Strategy Is Bold and Global

These models aren’t locked behind a paywall or hidden in a lab. They’re already available in more than 40 countries, including Nigeria, Ghana, South Africa, and Zimbabwe, through WhatsApp, Instagram, Messenger, and the Meta.AI web app. 

Multimodal features are only available in English and in the US for now—but Meta says they’re working on expanding access.

As for performance versus cost, Maverick brings what Meta calls a “best-in-class performance-to-cost ratio.” Translation? It’s really good, and it doesn’t take a data centre to run. That matters in a world where developers want high-performing models that won’t bankrupt them.

It’s Beyond Technical—It’s Personal

Meta is also tweaking the way these models respond to people. They’re more “steerable”—meaning you can tell them exactly how to behave and they’ll follow instructions without inserting moral judgments or personal bias. They’re also better at formatting responses, structuring replies clearly, and offering actionable suggestions. 

According to Meta:

“Thanks to model improvements, Meta AI with Llama 4 is the assistant you can count on to provide helpful, factual responses without judgment. It responds conversationally and shares informative answers to more requests on a range of topics like personal advice, opinions and recommendations, and more.”

That’s a subtle but important shift. Rather than trying to be all-knowing or opinionated, Llama 4 models aim to be useful without being preachy.

What’s Coming Next?

Meta’s vision with Llama 4 isn’t just about releasing models—the company setting up an ecosystem. At the heart of this is the belief that openness fuels innovation. 

Scout and Maverick are open-source. Anyone can download and experiment with them via llama.com or Hugging Face. That opens the door to new applications, personalised AI agents, and enterprise tools—all built on the same tech powering Meta’s consumer apps.

And then there’s Llama 4 Behemoth, still in training, still growing. When it drops, it could very well reset expectations again.

If this is the beginning, it’s already a big one. Scout and Maverick are Meta saying the future of AI is fast, efficient, multimodal, and more open than ever before.

The post Llama 4 Models: What Sets Meta’s Scout & Maverick Apart in a World Full of AI Assistants? appeared first on Tech | Business | Economy.

However, along with the numerous benefits they offer, AI assistants also present various challenges and issues that organizations must address to ensure their responsible development and deployment.

From concerns around privacy, security, bias, accuracy, and ethical considerations, organizations across diverse industries are grappling with complex dilemmas in leveraging AI technology to enhance customer interactions and operations.

In this context, organizations need to understand the weight of the impact these issues can have on their reputation, user engagement, regulatory compliance, innovation, and risk mitigation.

By prioritizing ethical AI practices, data governance, bias audits, user experience enhancements, and collaboration with industry stakeholders, organizations can navigate these challenges and unlock the full potential of AI assistants for their businesses and society at large.

AI assistants, such as Siri, Alexa, and Google Assistant, have become increasingly popular and have changed the way we interact with technology. These assistants use natural language processing and machine learning to understand and respond to user commands and queries.

However, several issues have arisen with AI assistants. One issue is the lack of privacy and security measures in place to protect user data.

AI assistants often collect and store personal information about users, raising concerns about data breaches and misuse of information.

Another issue is the potential for bias in AI assistants. As these assistants are programmed by humans, they may inadvertently reflect biases present in society, leading to discriminatory or inaccurate responses.

In terms of resolution, companies developing AI assistants are working on implementing stronger privacy and security measures to protect user data. This includes encryption of data, secure storage methods, and transparent data policies.

To address bias, developers are working on creating more diverse training datasets and implementing algorithms that can detect and correct biases in real time.

Additionally, there is a growing awareness of the importance of ethics in AI development, leading to the creation of guidelines and standards for responsible AI use.

In the short term, efforts are being made to improve the accuracy and reliability of AI assistants, as well as to enhance user trust through increased transparency and control over personal data.

In the long term, the goal is to create AI assistants that are truly unbiased, ethical, and privacy-conscious. This will require ongoing research and development in the fields of AI and machine learning, as well as collaboration between industry, government, and academia to ensure that AI technology is used responsibly and ethically.

Navigating the complexities of AI assistants involves considering a range of factors, from data privacy and security to transparency and bias.

By addressing these issues, organizations can improve efficiency and productivity, enhance customer experiences, and drive innovation in their operations.

1. Privacy and Security:

Issues: Lack of adequate privacy and security measures in AI assistants can lead to data breaches, unauthorized access to personal information, and misuse of user data.

Benefits of resolution: Implementing strong encryption, secure storage methods, and transparent data policies can enhance user trust and confidence in AI assistants, ensuring their personal data is protected and used responsibly.

2. Bias:

Issues: AI assistants may unknowingly perpetuate biases present in society, leading to discriminatory or inaccurate responses.

Benefits of resolution: Creating more diverse training datasets, implementing bias detection and correction algorithms, and promoting ethical AI development practices can help reduce bias in AI assistants, ensuring fair and unbiased interactions with users.

3. User Experience:

Issues: Inaccurate responses, misunderstanding of user commands, and lack of contextual understanding can lead to frustration and decreased user satisfaction.

Benefits of resolution: Improving the accuracy and reliability of AI assistants, enhancing natural language processing capabilities, and providing more personalized, context-aware responses can enhance the overall user experience, making interactions more efficient and effective.

4. Ethics and Accountability:

Issues: Lack of ethical guidelines and standards in AI development can lead to unintended consequences, ethical dilemmas, and potential harm to users.

Benefits of resolution: Promoting responsible AI use, developing ethical guidelines for AI development, and fostering transparency and accountability in AI systems can help ensure that AI assistants are developed and used in a manner that aligns with ethical principles and societal values.

5. Data Protection and Consent:

Issues: Users may not be fully aware of how their data is being collected, stored, and used by AI assistants, leading to concerns about privacy and data protection.

Benefits of resolution: Providing clear information about data collection practices, obtaining explicit user consent for data processing, and giving users control over their personal data can help build trust and confidence in AI assistants, fostering a more transparent and user-centric approach to data privacy.

Diverse organizations face a myriad of considerations when utilizing AI assistants, including data privacy, transparency, and bias. By proactively addressing these challenges, companies can optimize productivity, enhance customer satisfaction, and foster innovation within their operations.

The impact of resolving the issues related to AI assistants can vary for diverse organizations, depending on factors such as their size, industry, target audience, and use cases. However, there are several key ways in which organizations of all sizes and backgrounds can benefit from addressing these issues:

1. Improved Reputation and Trust: By prioritizing privacy, security, and ethical considerations in the development and deployment of AI assistants, organizations can build trust with their customers and stakeholders.

This can enhance their reputation and differentiate them from competitors in the market.

2. Enhanced User Engagement and Satisfaction: Resolving issues such as bias, accuracy, and user experience can lead to more engaging and satisfying interactions with AI assistants.

This can result in increased user retention, loyalty, and positive word-of-mouth recommendations.

3. Regulatory Compliance: Many countries and regions have introduced data protection and privacy regulations, such as the GDPR in Europe and the CCPA in California.

By addressing data protection and consent issues in AI development, organizations can ensure compliance with these regulations and avoid potential legal and financial consequences.

4. Innovation and Competitive Advantage: By investing in ethical AI development practices and responsible use of AI technologies, organizations can foster innovation and differentiation in their products and services. This can help them stay ahead of the curve in an increasingly competitive market.

5. Risk Mitigation: Addressing issues related to privacy, security, bias, and ethics in AI development can help organizations mitigate risks associated with data breaches, reputational damage, regulatory fines, and legal challenges.

This proactive approach can safeguard the organization against potential liabilities and crises.

Ways for diverse organizations to address these issues include:

1. Prioritize Ethical AI: Organizations should establish clear ethical guidelines and principles for AI development, ensuring that AI systems are designed and used responsibly and ethically.

2. Invest in Data Governance: Implement robust data governance policies and practices to ensure data privacy, security, and compliance with regulations. Organizations should also provide transparency and control to users over their data.

3. Conduct Bias Audits: Regularly audit AI systems for bias, implement bias detection and correction measures, and diversify training datasets to reduce bias in AI outputs and decisions.

4. Enhance User Experience: Continuously improve the accuracy, reliability, and usability of AI assistants to enhance the user experience and drive engagement and satisfaction.

5. Collaborate and Share Best Practices: Engage with industry peers, regulatory bodies, and experts in AI ethics and governance to exchange knowledge and best practices, and contribute to the development of ethical AI standards and frameworks.

In conclusion, as organizations continue to harness the power of AI assistants to transform their operations and customer experiences, they must prioritize ethical considerations, data governance, bias mitigation, user experience enhancements, and collaboration to address the complex issues that come with AI technology.

By proactively resolving these challenges, organizations can build trust with their customers and stakeholders, drive engagement and satisfaction, ensure regulatory compliance, foster innovation and competitive advantage, and mitigate risks associated with AI deployment.

As AI technology continues to evolve and permeate various aspects of our lives, organizations must remain vigilant and committed to responsible AI development practices to harness its full potential for a positive impact on individuals, businesses, and society as a whole.

[Featured Image Credit]

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The Writer, Prof. Ojo Emmanuel Ademola is the first Nigerian Professor of Cyber Security and Information Technology Management, and the first Professor of African descent to be awarded a Chartered Manager Status.

The post Navigating the Complex Issues of AI Assistants: A Strategic Guide for Organizations appeared first on Tech | Business | Economy.

In fact, new research from Slack finds that 80% of employees using generative AI tools are experiencing a boost in productivity — and that’s just the beginning.

And, with the introduction of AI assistants — including Salesforce’s own Einstein Copilot — the potential for businesses is only growing.

AI assistants can already answer questions, generate content, and dynamically automate actions. And someday, these assistants will become digital sales and service agents, anticipating our needs and operating on our behalf.

But with each new AI advancement comes new ethical concerns. It’s one thing if an AI assistant offers a bad product recommendation, but if it takes misguided actions on real-world concerns like personal finances or medical information — the stakes suddenly become much higher.

As we enter this new era of human-AI interaction, how can we harness the power of AI without opening ourselves up to dangerous risks?

Keeping a human at the helm

The AI revolution is an evolution. We’re taking quantum leaps forward every day, but we can’t always explain why AI does the things that it does — or eliminate every instance of inaccuracy, toxicity, or misinformation.

For these reasons, it’s important that we keep humans firmly in control of AI systems. But as AI becomes more and more sophisticated, it can be hard to figure out how to layer in that human touch.

We’ve all heard of keeping “humans in the loop,” but with this new generation of AI, it’s sometimes just not realistic for us to engage in every AI interaction or review every AI-generated output.

That’s why, at Salesforce, we believe trusted AI needs a human at the helm. Instead of asking humans to intervene in every individual AI interaction, we’re designing more powerful, system-wide controls that put humans at the helm of AI outcomes and enable them to focus on the high-judgement items that most need their attention. In other words, humans aren’t always rowing the boat — but we’re very much steering the ship.

And with a human at the helm, we can design AI systems that leverage the best of human and machine intelligence.

For example, we can unlock incredible efficiencies by tasking AI to review and summarise millions of customer profiles. And at the same time, we can build trust by empowering humans to lean in and use their judgement in ways that AI can’t.

Making AI a copilot, not an autopilot

There’s a reason this generation of AI products are called copilots and not autopilots. As AI becomes more powerful and autonomous — making decisions and taking actions on individuals’ behalf — keeping a human at the helm becomes even more important.

By combining the capabilities of AI with the strength of human judgement, we can make AI more effective and trustworthy.

Here are three ways we’re keeping humans at the helm of Salesforce AI:

• Prompt Builder Helps Us Automate in Authentic Ways: Prompts, or the instructions we send to generative AI models, are very powerful. A single, human-generated prompt can help guide millions of trusted outputs — but only if it’s constructed thoughtfully. With our newly announced Prompt Builder, we’re helping customers craft effective prompts by seeing the likely output in near real time to help ensure they get the AI outcome they want. We’ve also added different edit modes within Prompt Builder that allow users to tune and revise their prompts so they provide more helpful, accurate, and relevant results.
• Audit Trails Help Us Spot What We’ve Missed: Our Einstein Trust Layer offers a robust audit trail that allows customers to assess AI’s track record and pinpoint where their AI assistant may have gone wrong — but also where AI went right. These features help identify issues across large datasets that humans might not spot; and can empower us to use our judgement to make adjustments based on the needs of our organisation. For example, Audit Trail can alert us when an AI tool’s outputs are flagged as “thumbs down” a certain number of times — a sign that the AI-generated outputs might not be meeting the business’ goals. And by aggregating implicit feedback signals, like how often users edit an output before using it, Audit Trail can give us a bird’s eye view of our systems, allowing us to identify trends and take action.
• Data Controls Help Us Better Guard Our Data: AI is nothing without data. That’s why we’ve designed robust controls in Data Cloud — our fast-growing platform that helps bring siloed customer data together in one place — to help businesses securely action their data. Data Cloud features help organizations harness data for AI-powered insights and intelligence, while longstanding Salesforce core data controls like permission sets, access controls, and data classification metadata fields empower humans and AI models alike to protect and manage sensitive data.

Pioneering a new approach for the AI era

As the AI era continues to unfold, it’s critical that both humans and technology evolve along with it.

The AI revolution is not just about technological innovation — it’s also about empowering humans to sit successfully at the helm of AI, and use it in ways that are trustworthy and effective.

Our approach is evolving, and we are committed to continued research, learning, and multi-stakeholder collaboration on this topic.

But with a human at the helm, we believe we can combine the best of human and machine intelligence for this new AI era — leaning into AI’s capabilities and freeing up humans to do what they do best: be creative, exercise their judgement, and connect more deeply with one another.

With AI and humans working together, we can create more productive businesses, more empowered employees, and ultimately, more trustworthy AI.

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