Autore: martechcubejohn

Chris, could you start by introducing yourself and your role at Zennify and sharing a little about your background in the finance and technology sectors?

I joined Zennify in April 2023 as Chief Executive Officer. I’m a customer success and IT services veteran with over 15 years of experience in the Salesforce ecosystem and 30 years in technology.

Most recently, I was the Senior Vice President of Customer Success at Salesforce. I led the North American Success team responsible for ensuring the retention and growth of the $15B customer base. Before that, I was the COO of Model Metrics (acquired by Salesforce in 2011) and was a board advisor to Silverline and 7Summits, services firms within the Salesforce ecosystem. I was privileged to advise them on scaling and company growth.

We have a fantastic opportunity at Zennify to push boundaries and change the way consulting is done, using AI and tools to accelerate implementations and customer time to value. We strive to be the top boutique Salesforce and nCino consultancy for financial services firms. I’m proud to be here at Zennify and to continue upholding our reputation as one of the go-to partners for financial institutions that want to see accelerated outcomes.

Why financial institutions should ban AI at their own risk:

Chris, you’ve raised the idea that financial institutions should not ban AI at their own risk. Could you elaborate on why you believe AI is crucial for the financial sector’s future and what potential risks they face by not embracing it?

AI has and will continue to impact the breadth, depth, and quality of products and services offered by financial institutions. There are multiple use cases for AI – and a lot of them focus on increased efficiencies. For example, teams can use AI to better predict and assess loan risks, improve fraud detection, provide better and faster customer support through smarter personalization, and analyze data in unstructured ways – all while reducing costs. These are use cases that would have typically taken more time and have more room for errors. Understanding and implementing AI thoughtfully leads to sustainable business growth and staying ahead of your competitors.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-chris-conant-ceo-at-zennify/

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Hadoop for Beginners

Information Security and the C-suite

The ever-evolving digital landscape presents a constant challenge in the face of cyber threats. While traditional security methods offer a foundation, their limitations often become apparent. AI & Cybersecurity emerges as a powerful new tool, promising to enhance existing defenses and even predict future attacks. However, embracing AI necessitates careful consideration of ethical implications and fostering harmonious collaboration between humans and algorithms. Only through such mindful implementation can we build a truly resilient and secure digital future.

The digital frontier has become a battleground teeming with unseen adversaries. Cybercriminals, wielding an arsenal of ever-evolving malware and exploits, pose a constant threat to critical infrastructure and sensitive data. Traditional security methodologies, built upon rigid rule sets and static configurations, struggle to keep pace with the agility and cunning of these digital attackers. But on the horizon, a new solution emerges: Artificial intelligence (AI).

The Evolution of AI in Cybersecurity

AI-powered solutions are rapidly transforming the cybersecurity landscape, not merely enhancing existing defenses, but fundamentally reshaping the way we understand and combat cyber threats. At the forefront of this revolution lie cognitive fraud detection systems, leveraging machine learning algorithms to scrutinize vast datasets of financial transactions, network activity, and user behavior. These systems, adept at identifying irregular patterns and subtle anomalies, operate at speeds that surpass human analysis, uncovering fraudulent activity in real-time before it can inflict damage.

Gone are the days of rule-based systems, easily circumvented by attackers. AI-powered algorithms, in perpetual self-improvement, evolve alongside the threats. They learn from prior attacks, adapting their detection models to encompass novel fraud tactics and emerging trends. This approach significantly surpasses the static limitations of conventional methods, reducing false positives and ensuring a more resilient, adaptive defense.

The future of cybersecurity is intricately intertwined with the evolution of AI. By embracing the transformative potential of these algorithms, while remaining mindful of their limitations and fostering a human-centric approach, we can forge a future where the digital frontier is not a battleground, but a safe and secure terrain for innovation and progress. The algorithmic sentinel stands watch, a powerful ally in the ongoing quest for a more secure digital world.

To Know More, Read Full Article @ https://ai-techpark.com/evolution-of-ai-in-cybersecurity/

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AI in Medical Imaging: Transforming Healthcare

Guide to the Digital Twin Technology

Access to real-time data and insights has become critical to decision-making processes and for delivering customised user experiences. Industry newcomers typically go to market as ‘real-time’ natives, while more established organisations are mostly at some point on the journey toward full and immediate data capability. Adding extra horsepower to this evolution is the growth of ‘mobile-first’ implementations, whose influence over consumer expectations remains formidable.

Nonetheless, sole reliance on real-time data presents challenges, challenges that predominantly circle matters of interpretation and accuracy.

In this article, we explore why inaccurate real-time data and analytics transpire, explain the commonplace misinterpretation of both, and look at some of the tools that help businesses progress toward true real-time data competency.

The Risks of Using Imperfect, Legacy, and Unauthorised Real-Time Data and Analytics

Businesses risk misdirecting or misleading their customers when they inadvertently utilise imperfect or legacy data to create content. Despite real-time capability typically boosting the speed and accessibility of enterprise data, mistakes that deliver inappropriate services can undermine customer relationships.

Elsewhere, organisations invite substantial risk by using data without proper authorisation. Customers will often question how a company knows so much about them when they are presented with content that’s obviously been put together using personal details they didn’t knowingly share. When such questions turn to suspicion, the likelihood of nurturing positive customer relationships shrinks.

Misinterpreting Data and the AI ‘Hallucination’ Effect

Real-time data’s speed and accessibility are also impeded when full contexts are absent and can lead to organisations making hasty and incongruent decisions. Moreover, if the data is deficient from the start, misinterpretation of it becomes rife.

Today, the risks of flawed data and human oversight are exacerbated by a novel problem. Generative AI technology is known to ‘hallucinate’ when fed with incomplete datasets. At significant risk to the organisation, these large language models fill any gaps by inventing information.

To Know More, Read Full Article @ https://ai-techpark.com/real-time-data-and-analytics/

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Automated Driving Technologies Work

Ethics in the Era of Generative AI

In this technologically advanced world, companies adopting cloud computing quickly bring new opportunities to the market. IT professionals have the bandwidth to innovate new models and software, which eventually scale up better business efficiency and reduce the risk of technology hazards.

However, it is quite unfortunate that most CIOs still implement the traditional models that may have been successful in the past. Still, in this digitalized world, it is almost impossible to work without cloud computing.

Embracing cloud computing in enterprises ignites innovation, agility, and enhanced customer satisfaction. Therefore, cloud migration leads to a transformative journey by implementing business innovations such as next-generation hosting applications or data platforms to stay ahead of business competition.

This article is about how CIOs can implement the cloud migration process and how it is a transformative journey.

Cloud Computing’s Holistic Approach

Cloud computing technologies have been playing a leading role in transforming businesses across all industries for over a decade now. This technology has become the key that enables innovations and technological breakthroughs, as businesses are realizing the advantages of cloud computing over traditional methods of computing and storing data.

Consider an example: Migrating to cloud computing can help your business reduce the unnecessary cost of purchasing on-premises software and hardware. This approach eventually helps you understand if you want to scale up or down the utilization of cloud services, benefiting your business during seasonal fluctuations.

In the current business world, cloud migrations and cloud-native have become top priorities for CIOs; however, this technology comes with its complexities, such as how to optimize cloud costing or what the best practices are for hybrid cloud adoption.

Therefore, CIOs need to make the most of cloud technologies by ensuring a successful cloud migration, which is only possible if the correct steps are taken at the right time.

To Know More, Read Full Article @ https://ai-techpark.com/connecting-dots-with-cios-cloud-computing-chronicles/

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Digital Patient Engagement Platforms

World of Computer Vision

In today's digital landscape, where businesses are constantly vying for attention and striving to expand their customer base, lead generation stands as a crucial component of success. Within the realm of marketing, performance marketing has emerged as a powerful tool for driving tangible results and maximizing return on investment (ROI). By leveraging data-driven strategies and targeted approaches, businesses can effectively generate leads and nurture them into loyal customers. In this article, we delve into comprehensive insights into performance marketing strategies that are proven to enhance lead generation efforts.

Understanding Performance Marketing:

Performance marketing is a results-oriented approach that focuses on measurable outcomes, such as clicks, conversions, or sales. Unlike traditional marketing methods where success is often gauged by brand exposure or reach, performance marketing thrives on accountability and transparency. It allows businesses to allocate resources efficiently by investing in channels and tactics that deliver tangible results.

Key Components of Effective Lead Generation:

Data-Driven Targeting:

Central to performance marketing is the utilization of data analytics intelligence to identify and target the most relevant audience segments. By harnessing demographic, behavioral, and contextual data, marketers can create highly personalized campaigns tailored to the specific needs and preferences of their target audience.

Strategic Content Marketing:

Compelling content lies at the heart of successful lead generation efforts. Whether through blog posts, videos, infographics, or social media posts, valuable content not only attracts prospects but also positions the brand as a trusted authority in its industry. Content marketing fosters engagement and nurtures leads through the sales funnel, ultimately driving conversions.

Optimized Landing Pages:

Landing pages play a pivotal role in converting visitors into leads. Optimized for user experience and equipped with clear calls-to-action (CTAs), landing pages should align seamlessly with the corresponding ad or content that brought the visitor there. A/B testing and continuous optimization are essential to maximize conversion rates.

Email Marketing Automation:

Email remains a potent tool for lead nurturing and conversion. Through automation, marketers can deliver personalized and timely messages to prospects based on their behavior and interactions with the brand. Drip campaigns, triggered emails, and segmented lists enhance engagement and drive conversions over time.

Paid Advertising Campaigns:

Pay-per-click (PPC) advertising on platforms like Google Ads and social media channels enables precise targeting and immediate visibility. Performance metrics such as click-through rates (CTR), cost-per-click (CPC), and conversion rates provide valuable insights for optimizing ad campaigns and maximizing ROI.

To Know More, Read Full Article @ https://salesmarkglobal.com/performance-marketing-lead-generation-insights/ Maximize your growth potential with the seasoned experts at SalesmarkGlobal, shaping demand performance with strategic wisdom.

According to research from VMware Carbon Black, ransomware attacks surged by 148% during the onset of the Covid-19 pandemic, largely due to the rise in remote work. Key trends influencing the continuing upsurge in ransomware attacks include:

Exploitation of IT outsourcing services: Cybercriminals are targeting managed service providers (MSPs), compromising multiple clients through a single breach.

Vulnerable industries under attack: Healthcare, municipalities, and educational facilities are increasingly targeted due to pandemic-related vulnerabilities.

Evolving ransomware strains and defenses: Detection methods are adapting to new ransomware behaviors, employing improved heuristics and canary files, which serve as digital alarms, deliberately placed in a system or to entice hackers or unauthorized users.

Rise of ransomware-as-a-service (RaaS): This model enables widespread attacks, complicating efforts to counteract them. According to an independent survey by Sophos, average ransomware payouts have escalated from $812,380 in 2022 to $1,542,333 in 2023.

Preventing Ransomware Attacks

To effectively tackle the rising threat of ransomware, organizations are increasingly turning to comprehensive strategies that encompass various facets of cybersecurity. One key strategy is employee education, fostering a culture of heightened awareness regarding potential cyber threats. This involves recognizing phishing scams and educating staff to discern and dismiss suspicious links or emails, mitigating the risk of unwittingly providing access to malicious entities.

In tandem with employee education, bolstering the organization’s defenses against ransomware requires the implementation of robust technological measures. Advanced malware detection and filtering systems play a crucial role in fortifying both email and endpoint protection. By deploying these cutting-edge solutions, companies can significantly reduce the chances of malware infiltration. Additionally, the importance of fortified password protocols cannot be overstated in the battle against ransomware. Two-factor authentication and single sign-on systems provide formidable barriers, strengthening password security and rendering unauthorized access substantially more challenging for cybercriminals.

To Know More, Read Full Article @ https://ai-techpark.com/top-trends-in-cybersecurity-ransomware-and-ai-in-2024/

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Automated Driving Technologies Work

Ethics in the Era of Generative AI

Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.

The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.

However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.

Why Is XAI Vital for AI Professionals?

Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.

However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.

Let’s consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.

After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.

With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model’s outcome properly.

To Know More, Read Full Article @ https://ai-techpark.com/why-explainable-ai-is-important-for-it-professionals/

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What is ACI

Democratized Generative AI

Could you please share some insights into your professional journey as APJ Field CISO Director at SentinelOne?

Prateek: The role of Field CISO is very interesting as it focuses on the value proposition of the security initiatives and deployments. This role helps me in cutting the prevailing noise in the industry because of the overwhelming number of jargon, overmarketing, and overpromises of the providers. At the same time, it helps the security leaders climb the maturity curve and define the security charter.

Can you provide an overview of the current cloud security landscape in the Asia Pacific Japan region and explain why it’s becoming an increasingly critical concern?

Prateek: The adoption of cloud technologies and platforms is only accelerating in the APJ region alongside the threat landscape, and the risks are increasing too. With businesses moving their critical business applications, data, and operations to the cloud, they are increasingly being targeted by threat actors as the organizations’ maturity level in cloud security is relatively lower than the traditional architecture. Additionally, the data protection and privacy laws in different countries and regions emphasize the need for cloud security.

According to you, what could be the key strategies and best practices that organizations should prioritize when securing their cloud platforms within the Asia Pacific Japan region?

Prateek: The first step is the realization that the approach to securing the cloud is different from the traditional approaches and understanding the shared responsibility model between the cloud service provider and the client. Cloud is not inherently secured but can be secured with the right policy, configurations, and controls. The journey to securing the cloud should start with Cloud Security Governance.

Can you identify specific challenges that organizations in the Asia Pacific Japan region typically encounter when it comes to maintaining the integrity and security of their cloud-stored data?

Prateek: Security in the cloud is more of an identity and access management issue. When the identities and access to cloud resources such as data storage are configured with secured configuration such as no public access to storage buckets, expiration of API tokens, etc, it will ensure the integrity and security of the data stored in the cloud.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-prateek-bhajanka/

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Trends in Big Data for 2023

Diversity and Inclusivity in AI

Artificial intelligence (AI) has been a game changer in the business landscape, as this technology can analyze massive amounts of data, make accurate predictions, and automate the business process.

However, AI and ethics problems have been in the picture for the past few years and are gradually increasing as AI becomes more pervasive. Therefore, the need of the hour is for chief information officers (CIOs) to be more vigilant and cognizant of ethical issues and find ways to eliminate or reduce bias.

Before proceeding further, let us understand the source challenge of AI. It has been witnessed that the data sets that AI algorithms consume to make informed decisions are considered to be biased around race and gender when applied to the healthcare industry, or the BFSI industry. Therefore, the CIOs and their teams need to focus on the data inputs, ensuring that the data sets are accurate, free from bias, and fair for all.

Thus, to make sure that the data IT professionals use and implement in the software meet all the requirements to build trustworthy systems and adopt a process-driven approach to ensure non-bais AI systems

This article aims to provide an overview of AI ethics, the impact of AI on CIOs, and their role in the business landscape.

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

The foundation of ethical AI responsibility is to develop a robust AI lifecycle. CIOs can establish ethical guidelines that merge with the internal standards applicable to developing AI systems and further ensure legal compliance from the outset. AI professionals and companies misidentify the applicable laws, regulations, and on-duty standards that guide the development process.

Conducting Assessments

Before commencing any AI development, companies should conduct a thorough assessment to identify biases, potential risks, and ethical implications associated with developing AI systems. IT professionals should actively participate in evaluating how AI systems can impact individuals’ autonomy, fairness, privacy, and transparency, while also keeping in mind human rights laws. The assessments result in a combined guide to strategically develop an AI lifecycle and a guide to mitigate AI challenges.

Data Collection and Pre-Processing Practice

To develop responsible and ethical AI, AI developers and CIOs must carefully check the data collection practices and ensure that the data is representative, unbiased, and diverse with minimal risk and no discriminatory outcomes. The preprocessing steps should focus on identifying and eliminating the biases that can be found while feeding the data into the system to ensure fairness when AI is making decisions.

To Know More, Read Full Article @ https://ai-techpark.com/the-impact-of-artificial-intelligence-ethics-on-c-suites/

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Generative AI for SMBs and SMEs

Mental Health Apps for 2023

Aurelien, could you elaborate on how your professional experiences and background have contributed to your current position as Product Manager of Esker?

Prior to my current position as Product Manager of Esker’s Customer Service solution suite, I managed Esker technical support teams both in France and the US. I wanted to use the customer knowledge I gathered while helping Esker customers and bring my contribution to providing better products that fully answer customer needs. That led me to becoming a Product Owner within Esker’s R&D department, following the Agile Scrum methodology. I then became a Product Manager for a predictive lead scoring startup where I developed the necessary skills to position and market a new product, aiming at helping marketing and sales professionals develop their businesses.

I then came back to Esker as a Product Manager where I can combine my technical background with my many years of business and technology experience to deliver solutions that relieve Customer Service professionals from time-consuming tasks and enable them to develop new skills.

What is Esker’s overall vision and mission as a company? How does the organization strive to make an impact in the market or industry it serves?

Esker’s mission is to create a better business experience: businesses face uncertainty and need to build stronger relationships with their employees, as well as their customers and suppliers. We want to enable all stakeholders in the ecosystem to generate value together and never come at another’s expense. This is what we call the Positive-sum growth.

With our AI-powered cloud platform, we want to make an impact by automating finance and customer service processes, ensuring team members are more productive and engaged and eventually strengthening the business ecosystems of our customers.

As a Product Manager in the Order Management domain, what are the key challenges you face in delivering a successful SaaS product? How do you address these challenges?

The first challenge that I face is actually not specific to the Order Management domain but rather generic to all product managers: how do you make sure that you identify the most important problems and pains for your users and how do you make sure that you address them and provide value. In a nutshell, you need to remain close to your users and keep this user-centricity when developing your solutions. But I’ll come back to this topic in the following answers.

Then, as our solution targets B2B companies and each company operates slightly differently, another challenge consists in identifying the common needs that can make our product better globally, and not only for a niche of customers. But at the same time, sometimes, we want to provide features that mostly make sense for a given industry (such as pharma, medical device, or building materials), because there is a pain that is not answered by the market and we cannot only rely on the customization capabilities of our consultants to bridge the functionality gap. So, finding the right balance between adding generic and target industry-specific ones is a challenge.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-aurelien-coq/

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Generative AI for SMBs and SMEs

Mental Health Apps for 2023