Businesses today are competing to make the most out of the $3 trillion opportunity that Cloud-based platforms present in EBITDA.
A crucial factor to consider when moving to the cloud is the resilience of applications, as the maximum of the cloud value lies in running business-critical operations.
Cloud provides a stable on-premises environment with faster recovery time, more compliance and upgraded tools that provide optimal resiliency capabilities. Companies must design, architect, and adopt appropriate resiliency patterns to meet their specific requirements and to leverage these cloud capabilities.
Cloud resiliency patterns are targeted towards reducing technical debt, automating operations, and equipping applications with cloud capabilities.
Understanding Cloud Resiliency
Cloud resiliency is the capability of cloud infrastructure to maintain high availability, reliability, and fault tolerance even during unexpected disruptions such as hardware failures, network issues, or natural disasters. It is the backbone of an organization’s digital operations, ensuring that critical services and data remain accessible at all times.
Amid rapidly evolving customer expectations and regulatory demands, leaders and industry professionals have expressed genuine concerns over ensuring that their critical applications are always functioning and available.
The reason that few companies have moved their critical applications to the cloud can revolve around these concerns. A survey of leaders and technology experts revealed that only 10% successfully moved their critical and mission-based processes to the cloud.
Want to move to the cloud? Download our exclusive Cloud Migration White paper for detailed insights.
Cloud-related issues can be tackled by conducting two crucial analyses:
The first analysis can help determine the financial and reputational impact on the business. Organizations often tend to underestimate or overestimate these costs, which leads to inconsistencies in the decision-making process.
The second analysis helps businesses determine the on-premises operations-related costs compared to the cloud-related expenses. Cloud-based solutions are often more cost-effective and flexible, which allows them to accommodate usage surges and their pay-as-you-go model.
To derive cost benefits, leaders must ensure that their applications are designed well, their infrastructure is well developed and configured, and the lifecycle of the existing hardware is also considered.
Organizations can avoid expensive capital investments and hardware-related costs by migrating critical operations into the cloud, especially before any major changes in their hardware structures. Strong Fin Ops capabilities are required to acquire and build on these insights.
Why Is Cloud Resiliency Important?
- Business Continuity:
Cloud resiliency is fundamental for business continuity. Downtime can lead to significant financial losses, damage to reputation, and customer dissatisfaction. Cloud services minimize downtime, helping businesses stay operational in all circumstances.
According to a survey conducted by Gartner, organizations that rely on cloud technology for over 75% of their backup and recovery requirements can expect to reduce their overall cost of business continuity and disaster recovery by 60%. ( 3)
The cloud allows businesses to scale their resources up or down as needed. A resilient cloud infrastructure ensures that this scaling happens seamlessly, enabling organizations to meet sudden increases in demand without service interruptions. According to a Forbes survey, since 2020, 83% of companies have been managing their workloads in the cloud. (2)
- Disaster Recovery:
In the event of a disaster or a hardware failure, cloud resiliency plays a crucial role in disaster recovery. Data replication, backup, and redundancy strategies implemented by cloud providers help safeguard data and applications, allowing for quick recovery. A survey by the Disaster Recovery Journal found that 46% of organizations using the cloud for disaster recovery reported faster recovery times. (4 )
Security is a significant aspect of resiliency. Cloud providers invest heavily in cybersecurity measures, protecting against cyber threats and data breaches. It enhances the overall resiliency of cloud-based systems.
Building a resilient on-premises infrastructure can be expensive and complicated. In contrast, cloud resiliency is usually more cost-effective. Cloud providers manage the complexities of redundancy, failover, and disaster recovery, enabling businesses to concentrate on their primary operations without the stress of extensive infrastructure management.
- Competitive Advantage:
Companies that can provide uninterrupted services have a competitive advantage. Clients value reliability and are more likely to choose firms that can guarantee their services will be available when needed.
Forbes reported that 74% of Tech Chief Financial Officers (CFOs) believe cloud computing has the most measurable impact on their business. (5)
Resilient Cloud infrastructures can adapt to changing business needs. They enable organizations to expand their digital footprint and implement new technologies without disruptions.
How Can Cloud Providers Help With Resiliency?
Cloud providers deploy redundant hardware and network frameworks across multiple data centres. When one data centre experiences an issue, traffic can be accordingly rerouted to another, minimizing disruptions.
- Data Replication
Cloud providers often replicate data across multiple geographic regions. It ensures data availability even if a specific area faces a disaster or outage.
- Load Balancing
Load balancing distributes incoming traffic across multiple servers, preventing overloads on any single server and ensuring high availability and performance.
- Automatic Failover
Cloud services are architected with automatic failover mechanisms. If a server or data centre experiences a failure, services are automatically shifted to a healthy one, reducing downtime.
- Monitoring and Alerts
Cloud providers regularly monitor their infrastructure and services. They use advanced monitoring tools and artificial intelligence to detect issues and send alerts to administrators.
Critical Steps To Take Towards Cloud Resiliency
To launch cloud resiliency, companies must be aware of potential risks and equip themselves with the required resources to mitigate them.
Here are five critical steps to adopt cloud resiliency:
- The first step is prioritizing urgent business operations, identifying all related applications, and aligning the organization’s strategic goals with the impact analysis results.
- The second step is to identify the pain points and technical hindrances organizations encounter during cloud migration. In addition to this, companies must also define resiliency patterns to meet specific business needs.
- Companies must design a roadmap to implement a trial lighthouse to accelerate the learning process and add value.
- Organizations must also identify gaps in processes (such as incident, problem, and change management) and talent (including unfilled engineering roles) in conjunction with the architecture.
- The last step should be setting achievable goals for making the cloud resilient and ensuring that it aligns with business goals, duration, and costs.
Cloud resiliency is vital for ensuring the availability, reliability, and security of digital services in an increasingly interconnected world. It not only safeguards businesses against disruptions but also enables them to thrive, innovate, and remain competitive in a rapidly evolving digital landscape.
Consequently, it is a key consideration for organizations when selecting cloud providers and designing their cloud-based solutions. Businesses that harness the power of the cloud’s resiliency are better prepared to deal with disruptions and continue delivering their services without hindrances.
For more insights on Cloud Migration and Top Trends in the Cloud, please refer to our informational video on Top Cloud Native Trends to Watch in 2023.
- 83% Of Enterprise Workloads Will Be In The Cloud By 2020. (Forbes)
- Source: Gartner
- Source: Disaster Recovery Journal
- Roundup Of Cloud Computing Forecasts, 2017. (Forbes)
Digital transformation is not just a technological upgrade but a holistic rethinking of how a business operates, interacts with customers, and remains competitive in the digital age. It requires strategic planning, investment, and a commitment to ongoing adaptation and improvement.
A study by Gartner reveals that 91% of businesses are involved in digital initiatives, with 87% of senior business leaders saying digitalization must be a priority for all organizations. (1)
People can interpret the term digital transformation in many ways. It basically means bringing technological capabilities to a process or an entire organization for competitive advantage, improved customer experience and lower operational costs. Artificial intelligence, cloud computing, the Internet of Things (IoT), and data analytics are some technologies used when adopting digital transformation initiatives.
Digital transformation needs consistent long-term efforts to completely transform organizational operations, address improvement areas and upgrade tech capabilities.
It correlates largely with the terms digitization and digitalization:
The Gartner IT Glossary describes digitization as changing from analog to digital form, also known as digital enablement. In other words, digitization takes an analog operation and changes it to a digital format without any different-in-kind changes to the process itself.
Transforming all manual data into digital data to eliminate paperwork, conveniently store data, and reduce human errors is called digitization. Tasks such as scanning financial records and storing them as PDFs on a computer or cloud server are digitization.
The Gartner IT Glossary explains digitalization as using digital technologies to change a business model and provide new revenue and value-producing opportunities; it can be defined as moving to a digital business.
Digitalization means automating business operations to enhance efficiency and profitability. For instance, incorporating intelligent process automation to improve tracking, communication, and version control of creative projects is an example of digitalization. It involves a fundamental re-evaluation and restructuring of how you conduct your business, which includes digitization and digitalization.
Expanding on the role of automation in digitalization, Georg Tacke, CEO and Annette Ehrhardt, Global Head of Communications and Marketing Senior Director at Simon-Kutcher & Partners, believe “Digitalization increases process efficiency and enhances data transparency along with giving your top line a boost. If a company operates an online platform, then the company may already be 80 percent digitalized. It gives way for increased efficiency and customer value by remaining 20 per cent of the way.”
HOW IS DIGITAL TRANSFORMATION DIFFERENT FROM DIGITALIZATION?
Digitalization and digital transformation are two different concepts. An organization can carry out digitalization projects that involve automating processes and using digital tools.
However, digital transformation is not a project to be undertaken in isolation. It is a customer-focused strategic plan that requires an organization-wide change and the implementation of digital technologies.
Digital transformation comprises several digitalization initiatives, but leaders and company executives are under the false impression that digital transformation only involves digitalization. It is a grave strategic mistake on their part.
Digital transformation is a complex process that changes how an organization operates. It involves developing a change mindset and making transformation a core competency as the enterprise becomes customer-driven end-to-end. This agility is essential for ongoing digitalization initiatives but should not be confused with them.
Each stage of the process, from digitization to digitalization and digital transformation, is necessary but insufficient for the next level of the rewiring process. Understanding the distinction between these terms is crucial for organizations that want to embrace digital transformation and thrive in the digital age.
Now that we have clearly defined each of these definitions. Let us look at some of the capabilities we must tap into for a successful digital transformation:
- Strategy Development – A well-defined strategy is the first step to enhance overall business value. Specific areas for transformation must be looked into by companies, namely customer experience, processes, or functions that generate significant value for the business. Organizations must adopt a roadmap-driven strategy outlining the solutions and resources needed to deliver change to specific domains.
- Expertise – A knowledgeable and expert team makes the digital transformation process a worthwhile investment for your organization. Hiring and retaining digital talent is crucial. Employees within the organization must possess the skills and knowledge to leverage digital technologies. Training and upskilling programs can help these bridge skill gaps. The collaborative approach from digital talent and business professionals gives way to a successful digital transformation journey.
To learn more about Digital Transformation best practices and strategies, download our exclusive Case Study resource for detailed insights.
- IT Infrastructure and Integration -A robust IT infrastructure that supports the integration of new digital tools and technologies is essential. Legacy systems may need to be updated or replaced to ensure compatibility with modern digital solutions.
- Data Analytics -Data is of primary importance in the digital transformation journey. The ability to collect, analyze, and derive insights from data allows organizations to make informed decisions, personalize customer experiences, and identify growth opportunities.
- Change Management– Successful Digital transformation often requires a significant cultural shift within an organization. Change management strategies, templates and methodologies help employees adapt to new working conditions and embrace necessary technologies.
For insights on Change Management Approaches, please download our Change Management Strategy resource for improved implementation.
- Scalability and Flexibility – Digital transformation should be scalable to accommodate growth and adaptable to newer technologies. Scalable solutions ensure that the organization can grow without significant operational disruptions.
ROLE OF AI IN DIGITAL TRANSFORMATION
The role of Artificial Intelligence, especially Generative AI (Artificial Intelligence) is significant in the digital transformation process. A clear understanding of specific business requirements and goals is a prerequisite to derive maximum benefits from these technologies.
With the influx of new technology trends, it is easy to get sidetracked. We must focus on the potential value derived from these technologies to meet specific organizational requirements.
To leverage Generative AI capabilities, organizations require essential components for digital transformation. They need a well-defined strategy, expertise, and a responsive and scalable operating model. Generative AI also requires constant re-evaluation of roadmaps and prioritized solutions for updated models to support business objectives.
HOW TO START YOUR DIGITAL TRANSFORMATION JOURNEY
To embark on a digital transformation journey, here are a few factors to keep in mind-
- Clearly Defined Goals – It is necessary to clearly define your digital transformation goals and objectives and establish what you have set out to achieve in your organization. This vision will guide your efforts and provide a sense of direction.
- Assess Your Current Tech -A thorough evaluation of your current technology stack, processes, and culture are crucial steps in the digital transformation journey. Identify areas of improvement and opportunities for digital integration within your organization.
- Build A Collaborative Team- Digital transformation requires collaboration across all departments within the organization. A dedicated team with representatives from IT, marketing, operations, and other specific domains are essential for the overall digital success of the organization.
- Invest in Technology -Choosing the right digital tools and technologies that align with your goals is crucial. Organizations must ensure they are scalable and adaptable to their future needs.
- Measure and Update -Organizations must establish key performance indicators (KPIs) to track progress. Constantly assess your digital transformation efforts and adjust your strategy as needed.
DETERMINING THE EFFECTIVENESS OF DIGITAL TRANSFORMATION
After undergoing the process of Digital Transformation, it is necessary to assess if the transformation is adding value to your organization.
Here are three key performance indicators to determine its effectiveness:
- Financial Benefits -Digital Transformation services address one or more operational domains that can help generate financial value for the organization.
- Team Effectiveness -Digital transformations often take longer than expected due to a shortage of staff, outdated work methodologies, or a lack of critical capabilities such as efficient product management and user-experience design.
- Change Initiatives – It is crucial to have performance metrics in place for measuring the progress of building new capabilities and ensuring the health of the transformation is consistent. Specific evaluation metrics can be whether the team members are collaborating as planned, whether people are dedicatedly following the plan, whether they are meeting goals, and whether team members are adjusting to new tools, technologies and services built within the organization. An effective change management initiative involves focusing on achieving an optimal outcome rather than striving to achieve a perfect result.
Digital transformation is no longer a choice but a prerequisite for businesses that want to thrive in the digital age. By embracing this transformation, you can better meet customer expectations, gain a competitive advantage, and achieve operational excellence.
Start by defining your vision, assessing your current state, and building a dedicated team. By incorporating the right strategy and commitment, your organization can reinvent itself for the digital future.
Are you ready to embark on your digital transformation journey?
At InovarTech, we believe in the 3i principle: Inspire – Ideate – Innovate. Listen to our story on how we can handhold you in your Digital Transformation journey till benefit realization.
Book a meeting with us!
For detailed insights on Inovar Tech’s Automation initiatives, best practices, and strategies, please refer to this informational YouTube Video on Revolutionizing Business Efficiency: Process Automation Case Study.
- Where and How to Target Your Digital Business Transformation.
- Gartner Information Technology Glossary.
Automation and AI have changed how organizations conduct operations these days. As businesses continue to adopt automation within their operations, they are also using AI to enhance their strategies.
A study by Gartner Inc. reveals that 80% of business leaders believe AI-powered automation can be integrated into any business strategy.(1)
This power duo promises to boost productivity, minimize errors, automate manual tasks, accelerate decision-making via predictive analysis, enhance customer experiences and detect outdated patterns. These technologies collaboratively promote innovation and efficiency and help achieve unprecedented productivity within existing operations.
AI-powered automation is making a significant impact across various sectors, offering a wide range of benefits. Some key areas where AI is driving positive change:
Personalized Product Recommendations
AI algorithms analyze user behavior and preferences to offer tailored product recommendations. This not only enhances the customer experience but also boosts sales and engagement.
Anomaly Detection in Operations
AI can quickly identify unusual patterns or anomalies in large datasets. This is crucial for maintaining operational efficiency and addressing issues before they escalate.
AI’s ability to process vast amounts of data in real-time makes it a powerful tool for detecting fraudulent transactions. This not only saves companies money but also protects customers from financial harm.
Advances in AI
The latest generation of AI technologies is improving classification (sorting data into categories), estimation (predicting values), and clustering (grouping similar data points). This leads to more accurate insights and decision-making.
As AI continues to evolve, it promises to deliver even more value to companies. Improved accuracy, efficiency, and scalability mean that businesses can leverage AI for a broader range of applications and achieve better results.
How Has AI Evolved Over The Years?
In recent years, AI has made significant leaps in natural language processing, machine learning, computer vision and robotics. Translating and giving out customized responses to human speech, image analysis, forecasting patterns based on operational data, and independently navigating environments are some capabilities of newer AI models. This evolution is due to AI’s advancements in data collection, processing, algorithms, and access to vast training data sets.
Hindrances AI and Automation Present Us With
Data and Algorithm bias, privacy constraints, lack of comprehensive data and cyber security risks related to automated decision-making are some hindrances AI and Automation present us with. Despite automating manual tasks, they raise numerous concerns around privacy, security, and the digital divide, especially during crucial processes like the development and deployment of credit processing systems or creating healthcare treatment plans.
The Indispensable Role of AI and Automation
Despite the hindrances, companies can leverage the powerful combination of AI and Automation in numerous ways-
One of the primary advantages of Automation and AI is their ability to ensure accuracy across all operations. These technologies can perform repetitive tasks at an accelerated speed. From data entry to quality control, they can help significantly in reducing errors and increasing overall output quality.
Enhanced Decision Making
AI goes beyond automation and provides valuable insights that can assist in making smarter decisions. By analyzing massive volumes of data in real time, AI can detect patterns, trends, and correlations that may not be noticeable to humans. It enables businesses to make informed decisions promptly, optimizing processes and allocating resources efficiently.
Scalability And Flexibility
As your organization expands and grows, your workload will also increase. Scaling up operations can involve extensive logistical tasks; this is where AI and Automation step in to make your job easy. These technologies offer unparalleled scalability, allowing you to handle increased workloads without proportionally adding to your workforce. Additionally, automation can be customized to fit specific business needs, ensuring adaptability in an ever-evolving market.
Reduced Human Efforts
AI and automation minimize human efforts by taking care of routine, manual tasks, enabling employees to focus on activities that require critical thinking, problem-solving, and innovation. This shift aids in the development of a more skilled and competitive workforce.
Where human efforts end, AI and Automation take over. Businesses can maintain continuous operations around the clock, even across different time zones. These technologies benefit industries that require constant surveillance, like cybersecurity.
Customer Centric Approach
AI-powered chatbots and virtual assistants are changing the way businesses interact with customers. All these tools offer immediate responses to customer queries, enhancing the overall customer experience. AI analyzes customer data, helping organizations tailor their offerings to individual preferences, resulting in greater customer satisfaction and loyalty.
Incorporating AI and automation technologies is not just about keeping up with the latest trends, it is also about gaining a competitive edge. By embracing these technologies, businesses can become leaders in their domain, offering faster, more efficient, and higher quality products or services.
To effectively implement AI and automation throughout an organization, it is essential to establish a well-defined strategy, allocate dedicated resources, and promote innovation and adaptability. Additionally, safeguarding data privacy and security should be of utmost priority, and employees may require training to acclimate to new technologies and processes.
The advantages AI and Automation bring to productivity and efficiency are undeniable. These technologies are no longer a luxury; they have become a prerequisite for businesses aiming to thrive in a rapidly evolving digital world. By harnessing the power of AI and Automation, you are not just streamlining operations – you are paving the way for a future where unprecedented productivity and efficiency reign.
So, why wait? Download our exclusive case study for best practices and implementation strategies. Let us help you embrace these technologies for accelerated business success.
- Gartner Survey Reveals 80% of Executives Think Automation Can Be Applied to Any Business Decision.
Today, how you and I use technology for work and life has dramatically changed. Each year brings with it innovations and trends that have the potential to revolutionize industries and reshape our daily lives. As Leaders, IT professionals and companies, we are focused on technologies that will help us reduce further investments and acquire more returns.
In 2023, several technology trends have emerged that hold significant promise for transforming various sectors. This year began with renewed vigour and enthusiasm for adopting technologies that will be worthwhile for accelerating business performance.
In this blog, we will delve into eight technology trends you should consider this year.
Recently, one new trend that has been making waves in the tech world is Generative AI. It promises to drive sustainable growth, solve global challenges, and transform business operations significantly. Generative AI is the upgraded version of AI, which adds to the innovative capabilities of existing technologies like applied AI and industrial machine learning.
A June 2023 report by McKinsey titled The Economic Potential of Generative AI: The Next Productivity Frontier states generative AI and related models have transformed how businesses will use AI in the future.
For a better perspective, you can read our blog on privacy and security concerns on Generative AI.
Generative AI upgrades components within assistive technology by accelerating application development and making powerful capabilities available to the non-technical user base.
By looking at the latest predictions, we can deduce Generative AI is likely to add $4.4 trillion in economic value by harnessing specific use cases and other diverse uses like drafting email templates to increase overall productivity.
Although we have not seen significant tech investments over the past year, we can remain optimistic about future growth investment prospects. The year 2022 witnessed almost $1 trillion in tech investments, cementing our faith in the value potential of trending technologies.
Despite witnessing a decline in the adoption and uptake of applied AI, advanced connectivity, cloud, and edge computing, we can conclude that it is partially due to their maturity levels. Mature technologies are often exposed to short-term budget dynamics more than recent ones, such as climate and mobility technologies.
One example would be Open AI, which is predicted to go bankrupt by the end of 2024. The operational expenses for Open AI amount to $700,000 per day. Despite experiencing robust success initially, these financial struggles are hindering the company from achieving a revenue level that covers crucial expenditures.
Despite much noise about trending technologies, we must primarily focus on the value and potential of relevant technologies for our business progress. With a careful assessment of emerging landscapes and a balanced approach towards old and innovative technologies, we can harness innovation for sustainable growth.
Let us look at the current and upcoming trends around relevant technologies that tech companies should watch out for.
1. Applied AI
- AI models trained in machine learning can help streamline numerous mundane tasks. They can help segregate data, forecast predictions, and address automation issues for prompt and effective decision-making.
- From natural language processing (NLP) powering advanced chatbots and virtual assistants to AI-driven drug discovery and predictive analytics, AI is reshaping industries and enhancing decision-making processes.
- In 2022, we witnessed significant equity investments towards Applied AI worth $104 billion.
2. Automating Machine Learning
- The constantly emerging world of software and hardware technologies is expediting and eliminating risks involved in automated machine learning.
- In 2022, Machine Learning Automated solutions secured equity investments worth $3 Billion.
3. Generative AI
- Generative AI possesses capabilities that accelerate business operations significantly. The technology helps automate and expedite tasks by harnessing unstructured mixed modality data sets to help generate the latest content. Generative AI can use data sets to produce brand-new text, video, and code, among other things.
- In 2022, Generative AI secured equity investments of $5 Billion.
4. Next-Gen Software Development
- The latest software development tools possess capabilities to enhance application quality and accelerate the development process. These new tools can generate modern code deployment pipelines, automate code generation, and undertake testing, refactoring, and translation.
- One example is the new AI coding partner, the Git Hub Co-pilot tool that generates and refines the code you are working on. It helps solve problems, write tests, and explore new APIs without extensive search. It adapts to your coding method and accelerates the entire process.
- It uses OpenAI Codex, to gain context from written code, and then suggests new lines of code or overall functions.
- Low Code-No Code democratizes software development by accelerating the creation of prototypes, minimizing the development cycle, and fostering collaboration between technical and non-technical teams.
- In 2022, Next Gen Software Development secured equity investments of $2 Billion.
For a detailed insight into Inovar’s Low Code/No Code practices and approaches, download our exclusive whitepaper resource by clicking here.
5. Digital Trust Architecture and Identity
- Digital Trust solutions help companies establish and uphold customer trust; while using their data, digital products, and services.
- Digital trust architecture and identity management play pivotal roles in ensuring secure transactions, safeguarding sensitive data, and providing users with a seamless experience. As technology continues to evolve, so will the strategies and tools employed to build and maintain digital trust.
- Gmail has been attempting to eliminate phishing attacks by incorporating machine learning. If an email appears to be suspicious or a phishing attempt, Gmail will analyze and delay it as a part of its early phishing detection mechanism. Google has been able to intelligently incorporate AI that promptly detects malware and ransomware messages and blocks them immediately.
- The AI-powered Microsoft 365 Defender creates a defensive layer of protection to prevent active threats. It forms a parameter of inspection across numerous security data points like user accounts and end points and isolates the infected entity to prevent widespread impact. It mitigates both horizontal and vertical side-channel attacks.
- In 2022, Trust Architectures secured equity investments of $47 billion.
6. Cloud Computing Services
- Cloud and edge computing distributes work across multiple locations like hyper-scale remote data centres, regional centres, and local nodes to enhance latency, improve data transfer expenses, ensure data sovereignty regulations are complied with, and autonomy over data and security are adhered to.
- This trend is essential for applications like the Internet of Things (IoT), where real-time analysis is crucial. Edge computing also reduces the strain on centralized cloud systems.
- In 2022, Cloud Computing witnessed $84 Billion worth of equity investments and secured an adoption score of 4.
If you are considering migrating to the cloud, download our Cloud Migration Guide.
- Cybersecurity will witness exponential growth of approximately $198 billion in 2025. In the latter half of 2023, cybersecurity professionals will adopt a zero-trust approach towards external data as cybercrimes grow. AI will be a primary component to defend against cyberattacks.
- Cybersecurity is evolving with more advanced encryption methods, biometric authentication, and AI-powered threat detection. Protecting digital assets and sensitive information has become more critical in current times.
- According to the EU Agency for Cybersecurity, the ransomware business model has seen tremendous growth in the last ten years, and its projected expenditures will exceed $10 trillion by 2025.
Download our Security Case Study that we implemented for a large non-profit education organization.
8. Internet Of Things
- The year 2023 has witnessed more than $650 billion in investments in the Internet of Things, with $315 billion from Smart cities and $335 billion from industrial Internet.
- The technology sector will continue to grow despite economic constraints, compelling businesses to streamline their IoT (Internet of Things) expenditure to devices that offer significant ROI.
Download our IoT whitepaper to check on scenarios you can implement for your business.
Trending technologies of 2023 are the result of the rapid pace at which technology and innovation are evolving. From AI-driven upgrades to finding suitable solutions and upcoming technology patterns, these trends will change how we work, interact, and succeed in this digital landscape.
Technology experts believe 2023 will probably be an exhilarating year for witnessing rapid technological advancements in terms of new tools, their diverse capabilities and how businesses will leverage them to tackle bottlenecks and changes.
The selection and adoption of relevant technologies will pave the direction for the success of several industries in the future.
For more understanding of 2023 Cloud Native Trends, please refer to our informational video on Top Cloud Native Trends to Watch in 2023.
- McKinsey Technology Trends Outlook 2023. (McKinsey Report)
- Introducing GitHub Copilot: your AI pair programmer. (GitHub)
- OpenAI might go bankrupt by end of 2024, ChatGPT costing over Rs 5.80 crore per day. (Business India Report)
- Gmail is bringing in AI security for where humans fail. (CNET)
Generative AI is the new kid on the AI block that is scoring high marks in reducing the time taken in application development with improved productivity.
Generative AI is estimated to boost economic growth and value by $4.4 trillion, delivering powerful capabilities to non-technical users.
Despite the robustness of Generative AI models, they still present ethical challenges towards cybersecurity, balancing precariously between privacy and production.
The technology has both fascinated and alarmed security experts due to its potential to create realistic and sophisticated content.
This blog dives deep into the cybersecurity risks of Generative AI, what industry experts think of this emerging technology and some plausible solutions to prevent AI-driven cyber threats.
Cybersecurity Curve Balls
Generative AI has shown impressive capabilities in generating realistic results, synthesizing creative artworks, and even generating conversational responses indistinguishable from human speech.
However, these same abilities can be exploited for malicious purposes, such as creating sophisticated AI generated phishing attacks, spreading disinformation, or even fabricating information for altering public opinion.
Let us take a look at some curve balls Generative AI presents us with-
1. Data Privacy and Misuse
Generative AI systems use extensive data to determine and deliver accurate outputs. This substantial data collection and storage raises concerns about user privacy and potential misuse of confidential information.
2. Malicious Use Cases
Generative AI is vulnerable to AI-generated phishing attacks, providing scope for impersonation, or AI generated deepfakes, making it challenging for users to distinguish between real and fake information.
3. Bias And Discrimination
If the training data used for Generative AI models are biased, the generated content may reflect and amplify those biases, leading to discrimination or doctored outcomes.
4. Intellectual Property Concerns
With Generative AI capable of creating original content, there are concerns about intellectual property rights and copyright infringement.
5. AI-Augmented Cyberattacks
As AI evolves, AI-driven cyber threats could become more sophisticated, with attackers employing Generative AI to create ever-changing attack patterns that evade traditional security measures.
When asked about privacy concerns with Generative AI, Prashant Choudhary, Ey India’s Cybersecurity Partner, expounded, “Generative AI poses several privacy challenges. While some challenges have been discovered and many more are still coming out as more and more use cases pop up. And it is pervasive across all Generative AIs –Chat GPT, BERT, DALL-E, Midjourney, and so on.
The whole model is that you use training data, and then the AI comes out with whatever output it is supposed to give. It will give (output) based on the data that was used to train the model.
In this business, the data source is the internet and, there is a lot of web scraping involved, which brings the data to train these base models or the Large Language Models (LLMs).”( 1)
Plausible Preventive Measures
Although, we are unsure whether we can find foolproof solutions to mitigate Generative AI threats in cybersecurity, here are some plausible solutions we can adopt to address them.
1. Responsible Data Usage
- One preventive measure that can be taken is responsible collection, usage, and storage of data, while adhering to privacy regulations.
- Limiting data retention to the minimum for model training and actively seeking user consent can also be helpful.
2. Robust AI Verification
Developing AI-powered malware detection and prevention solutions for detecting and verifying the authenticity of the content delivered by Generative AI; can help user identify potential risks effectively.
3. Explainable AI
Be sure to implement techniques that make AI models transparent and open to interpretation. This allows users to understand the decision-making process and identify potential biases.
4. Collaborative Efforts
Encourage collaboration between AI and researchers, cybersecurity experts, regulatory authorities, and ethical governance bodies to determine and address ethical implications of Generative AI in cybersecurity.
5. Adaptive Cybersecurity Measures
Consistently update AI and cybersecurity policy to counter AI-driven cyber threats effectively. Also, use of AI technologies to develop proactive defence mechanisms against evolving threats can be beneficial.
6. Informed Consent
The use of Generative AI in various applications, such as virtual assistants, chatbots, or customer service interactions, raises questions about whether users should be explicitly informed when they are interacting with an AI system instead of a human.
In addition to these preventive measures, EY Cybersecurity partner, Prashant Choudhary believes “Synthetic data is a very interesting conversation to address all the copyright, legal, and other concerns when it comes to training LLMs. There are multiple interpretations of synthetic data, but for this conversation, I am assuming that synthetic data is basically when you generate a data using a computer and then you use that to train the LLM.”
He further explained that using computer-generated or synthetic data may appear to be a reasonable solution due to lower data costs, scalability, and the ability to generate multiple variants. However, this approach presents a challenge as the data will always reflect the algorithm used to generate it.
Industry experts are participating in discussions about using anonymized or tokenized versions of Personally Identifiable Information (PII) and other sensitive data.
With this approach, data is still extracted, but PII and other sensitive information are identified and replaced with anonymous labels to protect individual identities. This method can address privacy and other related issues and can be used to train LLM.
There are several regulatory authorities around the world who are giving their inputs around this issue. The NIST (National Institute of Standards and Technology) has developed the AI Risk Management Framework; the European Parliament is insisting on the EU Artificial Intelligence Act; the European Union Agency for Cybersecurity under discussion about cybersecurity for AI; the US Securities and Exchange Commission (SEC) are having conversations around AI, cybersecurity, and risk management.
Despite these factors, none of these solutions can create a solid defensive layer of protection against AI-driven cyber threats. These are not sure shot solutions but only preventive measures.
Generative AI presents an intriguing frontier in cybersecurity, offering both innovative solutions and ethical implications.
It is imperative to address ethical implications of Generative AI in cybersecurity, as the technology continues to evolve to guarantee a secure digital environment for all.
By encouraging a comprehensive discussion between stakeholders, implementing responsible AI and cybersecurity policies, and deploying innovative verification techniques, we can harness the power of Generative AI while mitigating its potential risks and fostering a safer digital environment for everyone.
For a deep learning of AI powered analytical tool development and implementation, please refer to our exclusive whitepaper resource on AI-based Gross To Net (GTN) Tool.
- Cybersecurity in the age of Generative AI: solving the ethical dilemma. (EY.com)
Artificial Intelligence and Machine Learning have become revolutionizing strategies that businesses are incorporating into their operations. By leveraging data science consulting services, organizations are striving to tread ahead of their competitors.
In this blog, we will discuss how processes like document processing, market predictions, and CRM (Customer Relationship Management) services can be accelerated using AI and ML tools. We will also discuss bottlenecks and hurdles like biased algorithms and the need for extensive data preparation, which organizations must overcome when using AI-powered tools.
Let us look at some statistical information that has been made available in recent years on data science implementation:
- A survey by Gartner Inc.(1) recorded that 37% of companies have implemented AI in their operations. The adoption rate of the technology has tripled in the last year.
- Investment in AI by leading businesses continues to skyrocket by 91.5%.
- Gartner Inc.(2) predicts that by 2027, Chatbots and Virtual Customer Assistants will become the primary customer service channel for businesses.
- Deloitte(3) reports that 82% of companies that have adopted AI and cognitive technologies early on have noticed a substantial increase in ROI from their production-level projects.
- Accenture(4) reports state that AI can increase profitability by an average of 38% across 16 industries by 2035.
- A report by McKinsey and Company(5) a data analytics company, states that there has been an increase in AI adoption rate by 25% in businesses that operate in the healthcare, pharmaceuticals, finance, and retail sectors.
- According to a PWC study(6), 72% of business leaders believe that AI will be advantageous for their enterprises in the future, with 67% believing that AI and data science consulting services will open new doors of opportunities for them.
AI Strategy Components For Businesses:
In addition to tools and expertise, organizations must strategize how to incorporate AI and ML tools to derive maximum benefits. In this blog, we will look at three key strategy components that businesses must consider:
Before investing in AI and ML technology, organisations must establish a case study to convince stakeholders of an AI investment. Businesses must research case studies or hire data analytics companies concerning AI adoption and how their operations can benefit by using these technologies.
For utilizing organizational resources, leadership support is crucial. Leaders must communicate with teams, emphasizing the need for AI in their operations and how an effective strategy can contribute to the overall growth of their organization.
All organisational teams, departments, and leaders must collaborate and be willing to expend time and effort for the success of an AI strategy. Teams and departments must prioritize tasks and brainstorm ideas for an effective implementation process.
The Starbucks company is one such real-world examples of a business that has successfully utilised Machine Learning algorithms in operations such as digital marketing, procurement processes for improving sales and other functions of the business.
Hurdles businesses must overcome while utilizing Artificial Intelligence and Machine Learning
Although AI and ML can make your day-to-day business operations easy to manage and conduct, utilizing these technologies come with their own set of challenges.
- One of the biggest hurdles that businesses must overcome, is the need for extensive data preparation. For these technologies to operate effectively, businesses are required to have clean, relevant, and diverse datasets to train these algorithms to analyze the data. It can be a time-consuming and expensive process for businesses that have substantial and complex datasets.
- These technologies also pose a threat of presenting biased algorithms. For algorithms to deliver unbiased results, businesses must ensure that their data sets accurately represent their target audience and are unbiased. This is particularly crucial for finance and healthcare sectors as mismatched data can result in grave consequences for individuals and society.
Strategies such as investing in data quality and governance, using diverse datasets to train algorithms and training employees for new roles can be beneficial in addressing these challenges. Using low code platforms such as Microsoft Power Platforms that provide SaaS offerings can make the journey of adopting AI technology seamless for businesses.
Tools that businesses can use for implementing Artificial Intelligence and Machine Learning
- Tensor Flow: Tensor Flow is an open-source software library developed by Google for building and training machine learning models. It is extensively used by developers and researchers for several applications from image recognition to natural language processing.
- Amazon Web Services (AWS): AWS provides businesses with various AI and ML tools such as Amazon SageMaker for building ML models, Amazon Recognition for image and video analysis and Amazon Comprehend for natural language processing.
- Microsoft Azure: Microsoft Azures comes with an extensive set of AI and ML tools such as Azure Machine Learning for building ML models, Azure Cognitive Services for natural language processing and Azure Data bricks for data engineering and ML.
- Google Cloud AI Platform: Google Cloud AI Platform comes with a range of tools and services that help in building and deploying ML models that include Tensor Flow, Keras and AutoML.
While undertaking the process of selecting an AI and ML tool or platform, business must consider whether these tools are specific to their needs and requirements and if they have the proper expertise to effectively implement these technologies.
Businesses must also ascertain the level of complication a problem presents. If it is a simple text analysis, a basic data analysis or an image analysis, businesses can opt for low code platforms like Microsoft Power Platform or other intelligent process automation platforms like UIPath or H2O.
What’s next for Artificial Intelligence and Machine Learning in Business .
The evolution of AI and ML technologies provides businesses with new avenues to explore when it comes to their usage in their operations. AI and ML can be used for supply chain optimization and automation, for improving fraud detection and enabling more personalized and targeted marketing campaigns.
Businesses are required to be cautious and mindful of the ethical and social implications that these technologies present. Especially in the areas of privacy, bias, and job displacement. Businesses need to consider investment in transparency, accountability, reskilling and upskilling their work force for newer roles while adopting data science technologies.
The technologies of AI and ML have the potential to transform how businesses will operate and compete in this digital age. By effective utilization of these technologies, businesses can stay ahead of the curve in their respective industries.
For more understanding about AI and ML strategies, check out our informational video on “Top 3 AI ideas you can use for your business.”