SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “Fortifying the Future: Ensuring Secure and Reliable AI”

As AI becomes further integrated into various domains, from infrastructure to defence, ensuring its robustness will become a matter of national security. An AI system managing power grids, security apparatus, or financial networks could present a single point of failure if compromised or manipulated. Historical incidents, such as the Stuxnet cyberweapon, illustrate the physical and cyber damage that can be inflicted. When considering AI’s complexity, the potential for a cascade of both physical and digital harm increases dramatically.

As such, we should ask: How do we fortify AI?

AI systems must be designed to withstand attacks. From decentralisation to layering, these systems should be constructed so that control points can seamlessly enter and exit the loop without disabling the broader system. Thus, building redundancy and backup at various control points within the AI systems. For example, suppose a sensor or a group of sensors is deemed to have failed or been corrupted. In that case, the broader system must be capable of automatically readjusting to stop utilising data and intelligence gathered from said sensors.

Another strategy for strengthening AI systems involves simulating data poisoning attacks and training AI systems to detect such threats. By teaching the systems to recognise and respond to attacks or failures, they can automatically reconfigure without the need for human intervention. If an AI can learn to identify tainted data, such as statistical anomalies or inconsistent patterns, it could flag or quarantine suspect inputs. This approach leans heavily on machine learning’s strengths: pattern recognition and adaptability. However, it’s not a failsafe; adversaries could evolve their attacks to more closely mimic legitimate data, so the training would need to be dynamic, constantly updating to match new threat profiles.

Maintaining a human in the loop to enable oversight and override is considered one of the most crucial elements in the rollout of AI in various industries. Allowing humans to oversee AI decision-making and restricting autonomy can prevent potentially harmful actions taken by these systems. Whilst critical in the early stages of AI deployment as capabilities scale and evolve, there may come a point where human oversight inhibits these systems and, in itself, causes more harm than good.

Finally, AI sovereignty may prove to be the most critical element in ensuring companies and governments fully control essential algorithms and hardware powering their operations. Without this control, these systems could be vulnerable to foreign interference, including cyberattacks, espionage, or sabotage. As the use of AI increases, the sovereignty of AI systems and their components will become increasingly important. At its core, AI sovereignty is about control, whether exercised by governments, corporations, or individuals. Through the control of data, infrastructure, and decision-making power, those who build and deploy AI systems and sensors gain control of AI.

Fortification will involve integrating resilience, adaptability, and sovereignty into AI’s DNA, ensuring it is not only intelligent but also resilient and unbreakable. It can provide technological advantages, but it may also expose systems to disruption and vulnerability exploitation. As organisations race to harness AI’s potential, the question looms: Will AI enable organisations to gain a strategic advantage, or will it undermine the very systems it was designed to strengthen?

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

Der Beitrag Fortifying the Future: Ensuring Secure and Reliable AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “A New Era of Intelligent Robots”

Imagine a world where humanoid robots cook for you, care for your loved ones, and streamline your workday – all powered by AI smarter than ever before. The global AI in robotics market, projected to surpass $124 Billion by 2030, is set to make this vision a reality. As the capabilities of AI evolve, these machines will become our companions, caregivers, and coworkers, they’ll make mobility more affordable, transform access to services, and redefine the value of human effort.

From Amazon’s fleet of 750,000 warehouse robots to Tesla’s ambitions to build 10,000 humanoid Optimus robots this year, the age of robots is upon us. Dependent on sensors and actuation systems to navigate and interact with the physical environment, this new age of robotics hinges on the developments of AI, designed to mimic and learn from its biological makers. Equipping these robots with intelligence, engineers working across various domains of expertise, utilise AI to enable vision, natural language processing, sound processing, pressure sensing, and more.

Beyond sensing, AI also enables robots to reason, adapt, and learn, using approaches including—but not limited to—reinforcement learning, neural networks, and Bayesian networks. These models and methods enable robots to assess risks and determine actions, and by learning from experience, robots can adapt to new tasks and environments. Thus, AI enables robots to perceive, act, learn, and adapt, allowing them to perform tasks with greater autonomy and precision.

However, integrating AI into robotics isn’t seamless, it comes with hurdles. Robots struggle with real-time processing delays, adapting to messy unpredictable environments, squeezing efficiency from limited hardware, and understanding human quirks like vague commands or gestures. These challenges constrain capabilities and the pace at which robots enter and dominate markets.

So, how can these challenges be addressed?

Some developments in addressing these challenges include:

1. Parallel computing

Parallel computing involves dividing larger tasks into smaller, independent tasks that can be processed simultaneously rather than sequentially. This enables increased computational efficiency, reduced latency, and improved cost efficiency. In robotics, parallel computing allows robots to process inputs from LIDAR, radar, and cameras simultaneously, enabling them to navigate environments more effectively and efficiently.

2. Transfer learning

Transfer learning leverages pre-trained models to solve new, but similar, problems. In this approach, a model trained on one task or dataset is reused and fine-tuned for a related task. For example, in machine vision for defect detection in manufacturing, fine-tuning a pre-trained model on a smaller dataset of images allows it to quickly adapt to detect specific defects, such as cracks or dents, without needing to train a model from scratch.

3. Self-calibrating AI

Self-calibrating refers to AI systems that autonomously adjust their parameters, models, or processes to maintain optimal performance without manual intervention. In robotics, self-calibrating AI enables robots to adapt to changes in their environment, hardware, or tasks, ensuring they operate with optimized accuracy and efficiency over time.

4. Federated learning

Federated learning is a technique that enables AI systems to learn from distributed data sources whilst ensuring privacy and security. It allows AI to collaboratively train a shared model without transferring sensitive data, preserving privacy and reducing reliance on centralised storage. For example, delivery robots use federated learning to optimise pathfinding without sending raw data, such as sensor inputs or location, to a central server. Instead, they locally update their models and share improvements, preserving both privacy and security.

These developments indicate a key focus on efficiency, adaptability, and learning – all of which are essential for the continued evolution of robotics in complex, real-world environments. Additionally, these advancements contribute to a future where robots collaborate with humans, leveraging their ability to learn from experience and improve over time.

So, what’s next for AI in Robotics?

Just as AI agents are taking over the digital realm, they are about to flood robotics too. AI agents embedded in robotics will supercharge the autonomy and flexibility of robots, enabling them to communicate with humans and even interpret intentions by analysing gestures and potentially emotional cues. Crucial to human-robot interactions, AI agents may prove highly effective in assisted care, hospitality, and other service industries.

Additionally, as technologies like federated learning and edge computing evolve, robots will share knowledge without compromising privacy or relying on centralised data. This will improve scalability and efficiency by reducing the need for costly centralised storage and processing, and enable additional robots to integrate rapidly into existing networks.

So, where does this leave us?

Although there are abundant market opportunities for AI in robotics, the pace at which different markets adopt robotics will vary; with AI being a key factor driving this adoption. Crucial for overcoming challenges related to autonomy, adaptability, and decision-making, AI will empower robots to perform tasks once considered too complex or risky for automation. As AI continues to evolve, it will not only raise important concerns about safety, ethics, and integration but help address them; ensuring robots can work seamlessly alongside humans and contribute to a more productive future.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

Der Beitrag A New Era of Intelligent Robots – AI and Robotics erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Eleanor Wright – “Navigating the Adoption of AI by the Public Sector”

Perfectly positioned to transform government efficiency and public services, governments globally are investing heavily in AI. From the UK’s plan to ramp up AI adoption to the Emirati investment in project Stargate, no government wants to be left behind.

AI however has more to offer governments than transforming public services, and government contracts will accelerate AI companies to industry dominance.

The public sector adoption of AI will require infrastructure, expertise, and a risk appetite. Data centers will be built, and vast amounts of energy will be used. Beyond the financial and material investment, engineers will be needed to code and develop these systems, and government expertise will be required to procure and integrate AI into antiquated legacy systems.

AI, however, has more to offer governments than transforming public services, and governments have the power to transform the business of AI. By gatekeeping access to data and procuring long-term contracts, public sector contracts can rapidly accelerate AI companies into big businesses and deliver the capital needed to beat out the competition, enabling a new wave of incumbents.

This model of public sector procurement from the private sector, however, may not be in the best interest of the citizens and taxpayers who will ultimately fund these large contracts. As AI efficiency and capabilities develop and public sector jobs are replaced, the greater the dependency will be on these companies to maintain critical public services. Thus, it is fair to assume that a critical point will be reached where these companies become too big to fail. If public services become reliant on the capabilities and services of a handful of providers, the balance of power will shift.

This dependency however should not discourage the adoption of AI by the public sector, but shape how contracts are procured and the business model underpinning them. Whether it be public-private partnerships, state-owned or implementing a cooperative structure, the business models underlying the roll-out of AI into the public sector could determine how AI is procured and implemented.

Whilst state-owned assets or companies can be inefficient, open to political interference, and lack a drive for innovation, they offer public-focused interest. Capital saved can be reinvested into the impact of public services and jobs that will have been outsourced to the private sector can be internally generated.

In the same way, state-owned companies operate in the interest of the public, public-private partnerships and cooperative companies may represent a strong middle ground between purely public or privately sourced contracts. Public-private partnerships will limit the amount of control private companies exert, and cooperative companies could enable the development and procurement of AI systems that meet a common economic and social goal.

It should be noted however that neither public-private partnerships nor cooperatives are fully resilient against political or private interference. Decisionmakers will always be susceptible to desiring increased control and securing financial gain.

Finally, another alternative may be to implement an open-source procurement model. By procuring solely from companies utilising open-sourced base models, public service contracts built on open-source models could help mitigate incumbency dominance and level the playing field. These base models could even use university knowledge and expertise to drive and maintain innovation.

No matter how public service agencies and providers choose to procure and maintain AI contracts, the business model underpinning the procurement both internally and externally will heavily shape the future of AI. A carefully thought-out business model could provide a strategic advantage and deliver greater value to stakeholders.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles

Der Beitrag Navigating the Adoption of AI by the Public Sector erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dileep Kumar Pandiya – “AI for Transformative Enterprise Growth: Insights from a Principal Engineer”

You know, it’s amazing to think about. Imagine your sales team closing deals twice as fast. Or your supply chain just adapting on the spot when the market shifts. Honestly, it’s not something from the future—it’s happening now, all thanks to AI.

I have been working in tech for almost 18 years, and I’ve seen how these tools turn ambitious ideas into actual results. I want to show you what that looks like in real life—where AI didn’t just help businesses grow, it completely changed the game.

How AI Unlocks Growth in Enterprises

What if your business could predict customer needs before they even knew them? AI makes this possible. It’s no longer about guesswork or reacting late; it’s about proactive strategies powered by data.
Take a retail chain struggling with overstock issues. By implementing AI to forecast demand using real-time trends, they reduced inventory waste by 20% and increased availability of high-demand items by 15%. It’s a transformation that goes beyond efficiency—it’s about building smarter, more agile businesses.

AI Copilot: Redefining Sales with AI

Sales has always been about timing and relationships. But what if AI could help you focus on the right opportunities at exactly the right moment? That’s the promise of AI Copilot.
When we launched Copilot, the goal was simple: empower sales teams to act smarter and faster. By integrating AI, I built a platform that could analyze millions of data points in seconds to identify high-potential accounts. The result: Sales teams were no longer overwhelmed by data they were driven by insights.
Here’s what stood out most to me: within three months, Copilot wasn’t just saving time—it was generating millions in additional revenue. Seeing the tangible impact on businesses and hearing feedback like “I can’t imagine working without this” made every late night worth it.

Scaling Smarter with AI and Microservices

Think of a system that can process thousands of real-time events every second, with no downtime. That’s what we built with the Phoenix Project, a scalable platform that uses AI and microservices to empower B2B clients.
One client used this platform to optimize marketing campaigns dynamically. Instead of waiting weeks for data analysis, they could adjust strategies on the fly, improving lead quality by 30% and cutting acquisition costs dramatically. It’s proof that scalability isn’t just a technical goal—it’s a business imperative.

Lessons for Enterprises Ready to Embrace AI

Here’s a story I often share: A small business hesitant to invest in AI started with a single pilot project—automating customer inquiries with AI chatbots. Within six months, they expanded the system to handle order tracking, inventory checks, and even personalized product recommendations. Today, they credit AI for a 25% increase in customer retention.
My takeaway is to start small, but think big. AI’s value compounds over time, so even small steps can lead to significant transformations.

Future Trends in AI and Enterprise Growth

The future isn’t just about doing things faster—it’s about doing them smarter. Imagine systems that can explain their decisions clearly or tools that work alongside humans to tackle complex problems.
One trend I’m particularly excited about is real-time decision-making. For example, picture a global logistics company rerouting shipments during a storm, avoiding delays and cutting costs. This kind of agility is becoming the new standard, and businesses that embrace it early will set themselves apart.

Final Thoughts

AI is the foundation for building the future of business. Whether it’s transforming sales strategies, driving efficiency, or enabling agility, the opportunities are immense. My advice: Don’t wait for the perfect moment to start. Take a step, learn, and grow with AI.

About the Author:

Dileep Kumar Pandiya is a globally recognized Principal Engineer with over 18 years of groundbreaking work in AI and enterprise technology. He has pioneered transformative AI-driven platforms and scalable systems, driving innovation for Fortune 500 companies like ZoomInfo, Walmart, and IBM. His leadership has redefined sales technology and digital transformation, earning him prestigious awards and international acclaim for his contributions to business growth and industry advancement. Known for his ability to blend visionary thinking with practical solutions, Dileep continues to shape the future of enterprise technology.

Der Beitrag AI for Transformative Enterprise Growth: Insights from a Principal Engineer erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “Agentic AI: 6 promising use cases for business”

AI agents will play a vital role in software programming and cybersecurity, but they will also change enterprise workflows and business intelligence, experts say.

Agentic AI is having a moment, as proponents see the benefits of using autonomous AI agents to automate manual tasks across organizations.

Agentic AI, which Forrester named a top emerging technology for 2025 in June, takes generative AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory already pursuing the technology.

CRM leader Salesforce has since centered its strategy around agentic AI, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.

With AI agents popping up in so many situations and platforms, organizations interested in the technology may find it difficult to know where to start. A handful of use cases have so far risen to the top, according to AI experts.

Agentic AI will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.

“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”[…]

Read more: www.cio.com

Der Beitrag Agentic AI: 6 Promising Use Cases for Business erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: intelligencebriefing.substack.com – “The Art Of Selling AI—Without Selling AI”

On October 17, Nicole Wieberneit (Sales & GTM Executive) joined me on “What’s the BUZZ?” and shared how you can scale go.-to-market (GTM) for enterprise Generative AI. Software companies often struggle to cut through marketing hype and focus on practical applications that truly add value. Product organizations need effective strategies that avoid pilot paralysis, adopt customer-focused go-to-market approaches, and scale AI projects effectively. But, where should you start? Here is what we’ve talked about…

Addressing Real Customer Problems

AI’s potential isn’t fully realized when the focus remains on novelty rather than on solving pressing, real-world issues. Identifying and addressing specific problems, especially those that keep business decision-makers up at night, is the most effective way to ensure AI projects don’t lose momentum.

Many companies get caught up in exploring AI’s “art of the possible,” brainstorming endless applications without enough focus. This approach often leads to analysis paralysis and delays in tangible results. By zeroing in on practical, high-value use cases, teams can ensure that AI initiatives are grounded in solving core business needs.

A more successful approach is to prioritize high-impact use cases within key verticals. For example, in the financial services industry, where compliance and data management are crucial, AI can be especially effective in automating document processing or handling regulatory workflows. These types of applications address significant operational pain points while allowing teams to start small and build momentum.

Instead of attempting to reinvent processes across the board, organizations can concentrate on areas where AI has a clear, measurable impact. Focusing on these areas allows for early successes that can build organizational support and provide a foundation for broader AI adoption over time.[…]

Read more: www.intelligencebriefing.substack.com

Der Beitrag The Art Of Selling AI—Without Selling AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “Is Now the Right Time to Invest in Implementing Agentic AI?”

While vendors say their current agentic AI-based offerings are easy to implement, analysts say that’s far from the truth.

Software vendors’ pitches are evolving, with  agentic AI beginning to supplant generative AI in their marketing messages. Rather than just generating code or content for human review agentic AI will, they say, follow instructions, make decisions, and take actions much as a human worker would, without human intervention.

It’s more than just a smarter RPA

Agentic AI isn’t just a better version of robotic process automation (RPA): It promises to take enterprises places RPA never could.

“Think of RPA as a train on tracks — it can only go where the tracks are laid. Agentic AI is more like a self-driving car — it can navigate different routes and situations adaptively,” said Paul Chada, co-founder of agentic AI-based software providing startup Doozer AI.

What makes agentic AI autonomous or able to take actions independently is its ability to interpret data, predict outcomes, and make decisions, learning from new data — unlike traditional RPA, which falters when encountering unexpected data, said Cameron Marsh, senior analyst at Nucleus research.

Der Beitrag Is Now the Right Time to Invest in Implementing Agentic AI? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: insidehighered.com – “Teaching Entrepreneurship Students to Self-Teach With AI”

Increasingly, employers are indicating that there’s a need for students to be trained in generative artificial intelligence tools as more businesses integrate the tech’s capabilities into the workplace.

Some instructors have implemented AI into their classes to demonstrate prompt engineering and showcase AI’s research and writing abilities. Entrepreneurship professor Mark Lacker at Miami University in Ohio encourages students to use generative AI tools to complete projects, inspiring creative and critical thinking skills that can prepare them for careers.

How it works: Whether it was talking with employers or with students as they engaged in internships or reading recent research, the theme of generative AI in the workplace was made exceedingly apparent to Lacker this past summer.

So, on the first day of classes, Lacker told his students what he’d learned: That a majority of graduates wished they’d been taught AI, that businesses were aggressively investing in AI tools and that if they didn’t board the AI train, it would leave the station without them. The students bought in immediately, he says.

The rapid evolution of AI has shown Lacker that it’s not enough to teach students how to use tools, but to teach them to use these tools while learning. “It’s gotta be in the workflow,” he explains.

The course, which covers start-up marketing and finance, is a 200-level course predominately taken by first-semester sophomores who represent a variety of majors and disciplines.

Lacker considers his course similar to an internship, where he fulfills the role of supervisor and his students act as interns, receiving project requests and submitting them.[…]

Read more: www.insidehighered.com

Der Beitrag Teaching Entrepreneurship Students to Self-Teach With AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Muhammad Irfan – “AI in Investment: How AI is Transforming the World of Financial Portfolios”

Key Takaways:

– Analyze past and current data to give a clearer picture of portfolio risks for better management.
– Integrating AI provides insights that help investment managers make data-driven decisions and maximize returns.
– Helps in portfolio management by automatically deciding how to distribute assets, rebalancing, and managing risks.
– Personalized investment strategies are just a click away with AI.
– Offer insights into market behavior to capitalize on investment opportunities.

Artificial intelligence (AI) has been making tides in every sector—be it finance or healthcare. The use of AI in finance has caught the eye of all stakeholders. According to Statista, the market size of AI in fintech is $44.08 billion in 2024, which is expected to exceed $50 billion in 2029.

At this point, you might be asking yourself what significant changes AI brings to finance. The short answer is that it improves efficiency, supports informed investment decisions, maximizes returns, and more. The long answer? Well, for that, you have to stick around till the end.

AI has completely changed the way investment professionals operate. It automates various administrative tasks and helps to make well-informed investment decisions by analyzing vast amounts of data. In today’s article, we will be understanding the role of AI in investment, financial portfolio management, and making smart decisions.

The Impact of AI on Finance: AI in Investments

AI isn’t just a catchy term. It offers numerous benefits to the finance industry, especially in smart investment, risk assessment, and portfolio management. It aids investment professionals in decision-making by analyzing huge amounts of data.

We all know that it’s highly possible for humans to overlook investment opportunities, but AI systems? Not a chance. Moreover, AI helps in portfolio management by optimizing asset allocations based on market conditions and investor preferences. Enough of the basics; let’s explore the benefits of AI for smart investments in-depth without wasting more time:

Understand Marketing Trends

Will you invest without having any knowledge about the market? Will you be relying on your gut feeling? Surely not. AI plays a pivotal role here and provides investors with insights by analyzing huge amounts of market data.

Utilizing these insights, investors can identify risks and opportunities for investment. It helps professionals make proactive decisions and maximize returns. So when you have a technology like AI, why make guesses? Investors should rely on AI for profitable investment strategies as it is the way forward.

Supports Risk Management

Talking about AI’s role in investment management and not mentioning risk assessment and management is nothing less than a crime. Investment isn’t kid’s play; even the most experienced among us face risks. Manual risk assessment and management is a tedious and time-consuming process that is prone to errors. However, that’s not the case when AI is involved.

AI technology helps to identify potential risks in financial portfolios and provides invaluable recommendations to mitigate them. Curious to know how things work?

AI analyzes large amounts of market data and takes multiple scenarios into consideration to provide an accurate assessment of risks like fraud or market decline and their impact on investment portfolios. This way, investment managers can optimize portfolio performance and achieve their investment goals.

Provide Personalized Investment Advice

Imagine having a companion who knows your risk tolerance and goals and provides personalized advice that leads towards your set goals. Isn’t it great? What if I say you have such a companion but aren’t aware of his capabilities? Yes, you heard it right.

Any guesses as to who it could be? Have you guessed AI? Spot on! AI capabilities extend to wealth management, tailoring investment plans to individual preferences and financial goals. AI helps manage wealth by customizing investment plans to fit personal preferences and financial goals.

Improve Portfolio Management

Another benefit of AI in investment is that it helps with portfolio management. It enables investors to easily automate and improve how they allocate and adjust their assets with the changing market. Name a thing that AI can’t do. Bet you won’t find any!

These modern AI systems evaluate market trends, investor profiles, and economic signs to optimize portfolios so they match individual goals and risk levels. What’s more, investors can predict asset performance using AI, which helps in proactive adjustments.

Factors to Weigh When Using AI in Investment Decision-Making

Bias & Fairness

We all know that AI systems are trained on data. It means any biases in data can lead to discrimination or undesired outcomes. To avoid such challenges, cleaning your data and regularly checking your AI systems for fair results is crucial. It helps organizations prevent legal issues and protects their reputation and trust.

Transparency & Explainability

Do you know that most AI systems are black boxes? Didn’t know what a “black box” is? It is a scenario where a system takes a decision without answering the question “how.” The need of the hour is to overcome this challenge by providing explanations on how an AI system draws conclusions.

Data Privacy & Security

Securing data is another crucial factor to consider when using AI. Organizations can overcome privacy and security challenges by complying with international regulations like HIPAA in the US and GDPR in the EU for data protection.

Conclusion

The recent adoption of modern technologies like artificial intelligence in finance has reshaped the industry completely. It not only helps to improve operational efficiency and save time but also aids in decision-making and financial portfolio management. We can confidently say that utilizing AI isn’t just a basic need; it has become imperative for investment professionals in order to achieve greater returns.

About the Author:

Irfan Malik is pursuing a PhD in artificial intelligence and is the founder and CEO of Xeven Solutions. He’s tech-savvy and always stays one step ahead when embracing new technologies. He is passionate about solving real-world problems and is dedicated to making AI technology accessible and beneficial for all.

Der Beitrag AI in Investment: How AI is Transforming the World of Financial Portfolios erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “Beyond the Hype: Key Components of an Effective AI Policy”

In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a pivotal role in transforming businesses across various sectors. From enhancing operational efficiency to revolutionizing customer experiences, AI offers immense potential. However, with great power comes great responsibility. Creating a robust AI policy is imperative for companies to address the ethical, legal and operational challenges that come with AI implementation.

Understanding the need for an AI policy

As AI technologies become more sophisticated, concerns around privacy, bias, transparency and accountability have intensified. Companies must address these issues proactively through well-defined policies that guide AI development, deployment and usage. An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives.

For instance, companies in sectors like manufacturing or consumer goods often leverage AI to optimize their supply chain. While this leads to efficiency, it also raises questions about transparency and data usage. A clear policy helps ensure that AI not only improves operations but also aligns with legal and ethical standards.

Key components of an effective AI policy

Ethical principles and values

It’s important to define the ethical principles that guide AI development and deployment within your company. These principles should reflect your organization’s values and commitment to responsible AI use, such as fairness, transparency, accountability, safety and inclusivity. If your company uses AI for targeted marketing, for example, ensure that its use respects customer privacy and prevents discriminatory targeting practices.
Data governance

Strong data governance is the foundation of any successful AI strategy. Companies need to establish clear guidelines for how its data is collected, stored and used, and ensure compliance with data protection regulations like GDPR in the EU, CCPA in California, LGPD in Brazil, PIPL in China and AI regulations such as EU AI Act.[…]

Read more: www.cio.com

Der Beitrag Beyond the Hype: Key Components of an Effective AI Policy erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.