How AI Tools Transform Resume Writing for Success?

On an average, HR managers and recruiters go through a resume in almost six to seven seconds. It’s a really short time and shows that your resume must be outstanding and unique to catch their eye. Using difficult fonts, flashy designs, and a bad layout can become a reason for you to miss out an opportunity, even if you are well-qualified for that role.

Your resume tells about your past work history, skills, hobbies, competencies, etc. Just like many other industries, Artificial Intelligence (AI) can help you with writing your resume. Most people make silly mistakes or are unable to include all necessary information about themselves in their resume. An AI job search tool can help craft a flawless resume for you apart from just searching jobs.

How AI Tools Transform Resume Writing?

Instead of doing it by yourself, when you take the help of AI, it will ensure that your resume has the right format and headings. 

Also, AI goes through the job posting and optimizes your resume based on it so that you have an edge over other candidates. This is how AI is transforming the art of writing resumes.

Suggest Ideal Templates

Most people choose a template for their resume and keep using it for all future applications. This is not the correct way because recruitment trends keep changing and not all organizations are looking for a similar thing.

A template may be good for a particular job opportunity but it doesn’t mean that it will work everywhere. AI tools suggest templates depending on the company you’re applying to. The right template will ensure clarity and visual appeal, highlighting relevant skills to impress HRs.

Analyzes Job Descriptions & Optimizes Your Resume Accordingly

You should never use the same resume for different job opportunities as every role demands different skills. AI tools carefully go through job descriptions and understand the requirements. They optimize your resume with several keywords and skills that recruiters are looking for. 

Also, these tools will place relevant terms in such a way that recruiters surely see them while going through your resume. Using a single resume does not work anymore and you should use AI tools if you want a perfect resume based on the role you’re applying for.

Focuses on Your Top Skills & Achievements

Many people don’t put emphasis on their top skills and previous achievements when creating their resume. Recruiters won’t put in the effort to read every single word of your resume and it’s your duty to showcase your skills and experience in a way that they have high visibility. 

When you use an AI job search tool, it will help you in highlighting the in-demand skills you have and your past work history relevant to the role. Even if you are well-qualified for a job, if your resume does not showcase your skills properly, you’ll miss out.

Helps With Proper Design & Formatting

No one likes to go through outdated or poorly-designed resumes. Your resume should have a visually-appealing design and must be easy-to-read by recruiters. 

AI will recommend design ideas that are trending so that your resume becomes attractive. Also, it helps with proper formatting where every section is highlighted and all the important skills and information is clearly visible.

Detects Error & Silly Mistakes

Many times people are applying to multiple opportunities at the same time which increases the chances of making errors or mistakes. You have to make slight changes to your resume depending on each individual job opening and it is common to make mistakes doing this.

AI goes through your entire resume and alerts you regarding any spelling, grammar, or other mistakes. Attention-to-detail is an important skill that recruiters look for and if your resume is filled with mistakes, recruiters will reject it right away.

Provides Feedback to Improve Your Resume

AI tools will give you personalized suggestions and constructive feedback after going through your resume so you can improvise it. 

Based on your existing resume, skills, work experience, and the industry you work in, you’ll get the best suggestions to create a better resume. You’ll get to know several keywords and skill sets that are in high demand so that you can optimize your resume accordingly.

Conclusion

Millions of people are out there searching for jobs and the competition is intense. As part of the recruitment process, hiring managers first look at the resume of candidates and it is important to have the perfect one if you want to gain an edge over others.

You’re bound to fail if your resume is outdated and you don’t talk about your skills and experience properly. Leveraging technology is a great option as an AI job search tool will help you improve your resume other than just finding potential job opportunities for you. With an attractive, informative, and error-free resume, your chances of getting a job go up significantly.

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Joel, how have the unique challenges faced by small and medium-sized enterprises influenced their adoption of AI in identity management and security practices?

So we commission a biannual small to medium-sized enterprise (SME) IT Trends Report that looks specifically at the state of SME IT. This most recent version shows how quickly AI has impacted identity management and highlights that SMEs are kind of ambivalent as they look at AI. IT admins are excited and aggressively preparing for it—but they also have significant concerns about AI’s impact. For example, nearly 80% say that AI will be a net positive for their organization, 20% believe their organizations are moving too slowly concerning AI initiatives, and 62% already have AI policies in place, which is pretty remarkable considering all that IT teams at SMEs have to manage. But SMEs are also pretty wary about AI in other areas. Nearly six in ten (62%) agree that AI is outpacing their organization’s ability to protect against threats and nearly half (45%) agree they’re worried about AI’s impact on their job. I think this ambivalence reflects the challenges of SMEs evaluating and adopting AI initiatives – with smaller teams and smaller budgets, SMEs don’t have the budget, training, and staff their enterprise counterparts have. But I think it’s not unique to SMEs. Until AI matures a little bit, I think that AI can feel more like a distraction.

Considering your background in identity, what critical considerations should SMEs prioritize to protect identity in an era dominated by AI advancements?

I think caution is probably the key consideration. A couple of suggestions for getting started:

Data security and privacy should be the foundation of any initiative. Put in place robust data protection measures to safeguard against breaches like encryption, secure access controls, and regular security audits. Also, make sure you’re adhering to existing data protection regulations like GDPR and keep abreast of impending regulations in case new controls need to be implemented to avoid penalties and legal issues.

When integrating AI solutions, make sure they’re from reputable sources and are secure by design. Conduct thorough risk assessments and evaluate their data handling practices and security measures. And for firms working more actively with AI, research and use legal and technical measures to protect your innovations, like patents or trademarks.

With AI, it’s even more important to use advanced identity and authentication management (IAM) solutions so that only authorized individuals have access to sensitive data. Multi-factor authentication (MFA), biometric verification, and role-based access controls can significantly reduce that risk. Continuous monitoring systems can help identify and thwart AI-related risks in real time, and having an incident response plan in place can help mitigate any security breaches. 

Lastly, but perhaps most importantly, make sure that the AI technologies are used ethically, respecting privacy rights and avoiding bias. Developing an ethical AI framework can guide your decision-making process. Train employees on the importance of data privacy, recognizing phishing attacks, and secure handling of information. And be prepared to regularly update (and communicate!) security practices given the evolving nature of AI threats.

AI introduces both promises and risks for identity management and overall security. How do you see organizations effectively navigating this balance in the age of AI, particularly in the context of small to medium-sized enterprises?

First off, integrating AI has to involve more than just buzzwords – and I’d say that we still need to wait until AI accuracy is better before SMEs undertake too many AI initiatives. But at the core, teams should take a step back and ask, “Where can AI make a difference in our operations?” Maybe it’s enhancing customer service, automating compliance processes, or beefing up security. Before going all in, it’s wise to test the waters with pilot projects to get a real feel of any potential downstream impacts without overcommitting resources.

Building a security-first culture—this is huge. It’s not just the IT team’s job to keep things secure; it’s everybody’s business. From the C-suite to the newest hire, SMEs should seek to create an environment where everyone is aware of the importance of security, understands the potential threats, and knows how to handle them. And yes, this includes understanding the role of AI in security, because AI can be both a shield and a sword.

AI for security is promising as it’s on another level when it comes to spotting threats, analyzing behavior, and monitoring systems in real time. It can catch things humans might miss, but again, it’s VITAL to ensure the AI tools themselves are built and used ethically. AI for compliance also shows a lot of promise. It can help SMEs stay on top of regulations like GDPR or CCPA to avoid fines but also to build trust and reputation. 

Because there are a lot of known unknowns around AI, industry groups can be a good source for information sharing and collaboration. There’s wisdom and a strength in numbers and a real benefit in shared knowledge. It’s about being strategic, inclusive, ethical, and always on your toes. It’s a journey, but with the right approach, the rewards can far outweigh the risks.

Given the challenges in identity management across devices, networks, and applications, what practical advice can you offer for organizations looking to leverage AI’s strengths while addressing its limitations, especially in the context of password systems and biometric technologies?

It’s a surprise to exactly no one that passwords are often the weakest security link. We’ve talked about ridding ourselves of passwords for decades, yet they live on. In fact, our recent report just found that 83% of organizations use passwords for at least some of their IT resources. So I think admins in SMEs know well that despite industry hype around full passwordless authentication, the best we can do for now is to have a system to manage them as securely as possible. In this area, AI offers a lot. Adaptive authentication—powered by AI—can significantly improve an org’s security posture. AI can analyze things like login behavior patterns, geo-location data, and even the type of device being used. So, if there’s a login attempt that deviates from the norm, AI can flag it and trigger additional verification steps or step-up authentication. Adding dynamic layers of security that adapt based on context is far more robust than static passwords.

Biometric technologies offer a unique, nearly unforgeable means of identification, whether through fingerprints, facial recognition, or even voice patterns. Integrating AI with biometrics makes them much more precise because AI algorithms can process complex biometric data quickly, improve the accuracy of identity verification processes, and reduce the chances of both false rejections and false acceptances. Behavioral biometrics can analyze typing patterns, mouse or keypad movements, and navigation patterns within an app for better security. AI systems can be trained to detect pattern deviations and flag potential security threats in real time. The technical challenge here is to balance sensitivity and specificity—minimizing false alarms while ensuring genuine threats are promptly identified.

A best practice with biometrics is to employ end-to-end encryption for biometric data, both at rest and in transit. Implement privacy-preserving techniques like template protection methods, which convert biometric data into a secure format that protects against data breaches and ensures that the original biometric data cannot be reconstructed.

AI and biometric technologies are constantly evolving, so it’s necessary to keep your systems updated with the latest patches and software updates. 

How has the concept of “identity” evolved in today’s IT environment with the influence of AI, and what aspects of identity management have remained unchanged?

Traditionally, identity in the workplace was very much tied to physical locations and specific devices. You had workstations, and identity was about logging into a central network from these fixed points. It was a simpler time when the perimeter of security was the office itself. You knew exactly where data lived, who had access, and how that access was granted and monitored.

Now it’s a whole different ballgame. This is actually at the core of what JumpCloud does. Our open directory platform was created to securely connect users to whatever resources they need, no matter where they are. In 2024, identity is significantly more fluid and device-centered. Post-pandemic, and with the rise of mobile technology, cloud computing, and now the integration of AI, identities are no longer tethered to a single location or device. SMEs need for employees to be able to access corporate resources from anywhere, at any time, using a combination of different devices and operating systems—Windows, macOS, Linux, iOS, Android. This shift necessitates a move from a traditional, perimeter-based security model to what’s often referred to as a zero-trust model, where every access transaction needs to have its own perimeter drawn around it. 

In this new landscape, AI can vastly improve identity management in terms of data capture and analysis for contextual approaches to identity verification. As I mentioned, AI can consider the time of access, the location, the device, and even the behavior of the user to make real-time decisions about the legitimacy of an access request. This level of granularity and adaptiveness in managing access wasn’t possible in the past.

However, some parts of identity management have stayed the same. The core principles of authentication, authorization, and accountability still apply. We’re still asking the fundamental questions: “Are you who you say you are?” (authentication), “What are you allowed to do?” (authorization), and “Can we account for your actions?” (accountability). What has changed is how we answer these questions. We’re in the process of moving from static passwords and fixed access controls to more dynamic, context-aware systems enabled by AI.

In terms of identity processes and applications, what is the current role of AI for organizations, and how do you anticipate this evolving over the next 12 months?

We’re still a long away from the Skynet-type AI future that we’ve all associated with AI since the Terminator. For SMEs, AI accelerates a shift away from traditional IT management to an approach that’s more predictive and data-centric. At the core of this shift is AI’s ability to sift through vast, disparate data sets, identifying patterns, predicting trends, and, from an identity management standpoint, its power is in preempting security breaches and fraudulent activities. It’s tricky though, because you have to balance promise and risk, like legitimate concerns about data governance and the protection of personally identifiable information (PII). Tapping AI’s capabilities needs to ensure that we’re not overstepping ethical boundaries or compromising on data privacy. Go slow, and be intentional.

Robust data management frameworks that comply with evolving regulatory standards can protect the integrity and privacy of sensitive information. But keep in mind that no matter the benefit of AI automating processes, there’s a critical need for human oversight. The reality is that AI, at least in its current form, is best utilized to augment human decision-making, not replace it. As AI systems grow more sophisticated, organizations will require workers with  specialized skills and competencies in areas like machine learning, data science, and AI ethics.

Over the next 12 months, I anticipate we’ll see organizations doubling down on these efforts to balance automation with ethical consideration and human judgment. SMEs will likely focus on designing and implementing workflows that blend AI-driven efficiencies with human insight but they’ll have to be realistic based on available budget, hours, and talent. And I think we’ll see an increase in the push towards upskilling existing personnel and recruiting specialized talent. 

For IT teams, I think AI will get them closer to eliminating tool sprawl and help centralize identity management, which is something we consistently hear that they want. 

When developing AI initiatives, what critical ethical considerations should organizations be aware of, and how do you envision governing these considerations in the near future?

As AI systems process vast amounts of data, organizations must ensure these operations align with stringent privacy standards and don’t compromise data integrity. Organizations should foster a culture of AI literacy to help teams set realistic and measurable goals, and ensure everyone in the organization understands both the potential and the limitations of AI technologies.

Organizations will need to develop more integrated and comprehensive governance policies around AI ethics that address:

How will AI impact our data governance and privacy policies? 

What are the societal impacts of our AI deployments? 

What components should an effective AI policy include, and who should be responsible for managing oversight to ensure ethical and secure AI practices?

Though AI is evolving rapidly, there are solid efforts from regulatory bodies to establish frameworks, working toward regulations for the entire industry. The White House’s National AI Research and Development Strategic Plan is one such example, and businesses can glean quite a bit from that. Internally, I’d say it’s a shared responsibility. CIOs and CTOs can manage the organization’s policy and ethical standards, Data Protection Officers (DPOs) can oversee compliance with privacy laws, and ethics committees or councils can offer multidisciplinary oversight. I think we’ll also see a move toward involving more external auditors who bring transparency and objectivity.

In the scenario of data collection and processing, how should companies approach these aspects in the context of AI, and what safeguards do you recommend to ensure privacy and security?

The Open Worldwide Application Security Project (OWASP) has a pretty exhaustive list and guidelines. For a guiding principle, I’d say be smart and be cautious. Only gather data you really need, tell people what you’re collecting, why you’re collecting it, and make sure they’re okay with it. 

Keeping data safe is non-negotiable. Security audits are important to catch any issues early. If something does go wrong, have a plan ready to fix things fast. It’s about being prepared, transparent, and responsible. By sticking to these principles, companies can navigate the complex world of AI with confidence.

Joel Rennich

VP of Product Strategy at JumpCloud 

Joel Rennich is the VP of Product Strategy at JumpCloud residing in the greater Minneapolis, MN area. He focuses primarily on the intersection of identity, users and the devices that they use. While Joel has spent most of his professional career focused on Apple products, at JumpCloud he leads a team focused on device identity across all vendors. Prior to JumpCloud Joel was a director at Jamf helping to make Jamf Connect and other authentication products. In 2018 Jamf acquired Joel’s startup, Orchard & Grove, which is where Joel developed the widely-used open source software NoMAD. Installed on over one million Macs across the globe, NoMAD allows macOS users to get all the benefits of Active Directory without having to be bound to them. Joel also developed other open source software at Orchard & Grove such as DEPNotify and NoMAD Login. Over the years Joel has been a frequent speaker at a number of conferences including WWDC, MacSysAdmin, MacADUK, Penn State MacAdmins Conference, Objective by the Sea, FIDO Authenticate and others in addition to user groups everywhere. Joel spent over a decade working at Apple in Enterprise Sales and started the website afp548.com which was the mainstay of Apple system administrator education during the early years of macOS X.

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Another day, another AI headline. Meta has introduced new AI chatbots, embodied by celebrities, in a bid to mix information with entertainment. Amazon has invested up to $4B in its rival, Anthropic; and Google has launched Gemini, to compete with GPT-4. That’s just some of the AI stories within the last quarter involving three of the most influential companies in the technology sector.

Artificial Intelligence is booming. Its rapid development in 2023 has unlocked a wave of new possibilities and opportunities for the AI and machine learning ecosystem. But one of its beneficiaries isn’t. While AI stock has never been higher, we’ve not seen this optimism translate into the autonomous vehicle (AV) sector. This makes little sense. The development of AI and the future of autonomous vehicles is inextricably linked – the former quite literally powers the latter. So why is there this disparity in market confidence between the two sectors? And what does the surge in artificial intelligence mean for the AV sector as a whole?

The AV crystal ball

The challenges of AV at present are those of AI’s future. One of these big challenges revolves around data. An advanced driver assistance system (ADAS) or autonomous driving (AD) system relies on sensors (such as cameras and radar) to ‘see’ the world around them. The data these sensors collect is processed by machine learning to train an AI algorithm, which then makes decisions to control the car. However, handling, curating, annotating and refining the vast amounts of data needed to train and apply these algorithms is immensely difficult. As such, autonomous vehicles are currently pretty limited in their use cases.

AI developers outside the AV world are similarly drowning in data and how they collate and curate data sets for training is equally crucial. The issue of encoded bias resulting from skewed, low quality data is a big problem across sectors: bias against minorities has been found in hiring and loans, where in 2019 Apple’s credit card was investigated over claims its algorithm offered different credit limits for men and women. As applications of AI only continue to increase and reshape the world around us, it’s critical that the data feeding algorithms are correctly tagged and managed.

In other sectors, errors are more readily tolerated, even while bias harms. Consumers may not mind the odd mistake here and there when they enlist the help of ChatGPT, and even find these lapses amusing, but this leniency won’t last long. As reliance on new AI tools increases, and concern over its power grows, ensuring applications meet consumer expectations will be increasingly important. The pressure to close the gap between promise and performance is getting bigger as AI moves from science fiction to reality.

The importance of alignment

These questions of safety carry into AI alignment – the new focus in artificial intelligence. It’s a field of safety research that centres on aligning AI with human and societal values and looks to build a set of rules or principles which AI models can refer to when making decisions, so outcomes are in tune with human goals.

This concept of humans setting standards that AI must meet, rather than being dictated to by code, will be vital in shaping the future of both autonomous vehicles and AI as a whole. One of the reasons true self-driving cars are struggling to materialise is because there is no absolute truth with driving: driving is subjective and everyone will do it differently.

Navigating the complexity and subjectivity of driving means a new methodology is needed. Old tactics of training AI through observing human behaviour won’t work – instead, developers need to employ an outcome-based approach and first decide how they want a product to behave, then, how they will achieve this behaviour.

At the heart of this new way of working is an iterative approach. As an algorithm is developed it should be monitored and the evolving dataset shaped, to ensure it aligns with the predetermined product goals. Incremental progress may not grab as many headlines but it’s crucial in prioritising safety, winning consumer trust and marrying expectation with end results. And there are more immediate economic wins to be gained, too, as iterative processes can help AV manufacturers cut costs.

The future of AI and autonomous vehicles is intertwined, although their current narratives might say otherwise. AI developers across the field should look to the AV industry as a foreshadowing of the challenges looming in their own future, and preemptively correct course. Aligned development and iterative working will be the way autonomous vehicles, and artificial intelligence as a whole, reach their desired destination.

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The rise of artificial intelligence, specifically generative AI, which includes tools like ChatGPT and Google’s Search Generative Experience, is shifting the marketing landscape, demanding that brands evolve or lose narrative control and market share. The evolution sees brands moving away from channel-centric approaches to putting the spotlight on building authentic consumer connections. One that leverages search intent data-derived consumer insights to truly understand the audience, in order to satisfy them and win trust across the entire buyer’s journey. 

Adopting this mindset and implementing an owned asset optimization strategy (OAO) does just that. It tells the compelling story to connect with consumers, but it also generates a richness of content that AI tools, especially the current wave of AI search engine features need. With OAO-driven content brands can influence the output of these tools. More on that later.

First, let’s look at generative AI and its big risk for unprepared brands.

Understanding Generative AI

Generative AI and AI-powered search engines are data-hungry. These tools are trained on massive datasets, an amalgamation of crawled public web, Reddit, Wikipedia, social media, textbooks, and much more. ChatGPT relies on infrequent training and, recently, internet browsing that provides the model more data. It also learns from individual user queries and engagement. Connected tools like Bing Chat and Google Search Generative Experience (SGE) have access to everything on the public web.

Generative AI produces an output based on its training data and the given query posed by the user. ChatGPT has been exposed to billions of pieces of content. From that experience, it builds content or answers questions — generating text it considers to be a reasonable continuation of what came before. It analyzes probabilities of the next word and creates a ranking, picking from the most likely words to build a comprehensible meaning. SGE, from our early experimentation, builds content based on similar modeling, but uses recently cached web pages for sourcing, building an answer and a list of source pages. Traditional SEO page factors likely come into play when signaling what SGE will use.

The Risk To Brand Narrative Control

A major risk facing brands across industries is the loss of control over brand narrative — the vital storytelling process. AI is exacerbating the risk.

Brands are storytellers, communicating their story (brand narrative) to authentically connect with consumers. 

AI represents a rival storyteller. It can tell the wrong story (outdated, erroneous, unflattering, etc.) and result in loss of narrative control. Without action, this loss is inevitable.

Brands with unoptimized content infrastructures, unfavorable stories, and the resulting difficulty building authentic connections, risk narrative control erosion as well as declining market share. The war for narrative control has many fronts (impact of consumer empowerment, reviews, social media, competitors, etc.), and AI opens an additional one.

Dangers of Uninfluenced Generative AI

By default, generative AI tools only use training data, user feedback, and in the case of internet-connected tools, live web content. What AI produces tends to reflect what it already knows, has learned about, or has access to. 

When users ask about a brand or perform a related action in an AI tool, the output will reflect what’s already out there. If the input is wrong, unflattering, or simply uncontrolled by a brand, AI’s output will be more of the same. This AI loop can create snowballing preferred narrative erosion and reputational harm. Also concerning is AI’s propensity towards producing thin or even hallucinated content when it has thin data to work with. 

Owned Asset Optimization Influences Generative AI 

Owned asset optimization is a strategy that uses detailed intent data to create consumer behavior insights and then creates an overarching touchpoint infrastructure to deliver what consumers want. The process of creating this infrastructure — a constellation of owned assets featuring brand-controlled content built with real consumer connection in mind — generates the added surplus of content needed to influence AI tools.

Each owned asset is a fully optimized consumer touchpoint designed to deliver value and build trust, but each also acts as a new data source to feed the AI tools. Because owned assets are brand-owned venues featuring brand-controlled content they’re authoritative and likely to positively influence the story AI tools tell about the brand. Additionally, each touchpoint is optimized for AI visibility using injections of direct, high-volume questions and unique, proprietary data and branding.

These optimized assets take the uninfluenced AI output and inject control where there can be chaos.

ChatGPT and OAO

If you build owned assets now, there’s an increased likelihood that they get pulled into the training dataset, informing how ChatGPT understands your brand and what it produces. OpenAI has not updated ChatGPT’s dataset beyond September 2021, but it is likely to happen in the future. OAO helps brands prepare with authoritative, accurate, and contextualized data.

Google SGE and OAO

SGE is still a Search Labs experiment, but our experience with it confirms that owned assets will be able to directly influence what it creates when users submit a query in Google Search. When you ask SGE, it rapidly generates a response. On the right it lists the sources. In a fully implemented OAO strategy, a brand’s owned assets can get pulled into SGE results. This injects brand control into the generative response, pointing consumers to the asset, whether it’s a blog, landing page, or other owned asset type, and encouraging a more favorable, accurate response.

Role of Consumer Intent Data

Owned assets are created and optimized with consumer engagement and authentic trust-building in mind. But how do you know exactly what your audience wants? Consumer intent data, like search intent data, social engagement, and first-party insights, answers that question, and can be applied to all owned assets. Real world consumer behavior data, correctly analyzed and leveraged, tells brands what people want and how to solve their problems. Each owned asset in the network of assets is built with consumer intent in mind, aimed at people within all journey stages.

This is important for two reasons. 

First, the touchpoints and content built with consumer intent in mind, and designed to truly help users wherever they are, can actually connect with humans. It garners trust, and influences consumers to make a purchase decision later on. Second, the same content that helps real people solve their pain points and develop deep brand trust also helps AI tools understand your brand, your values, and your story. The more you produce, the more the AI has to pull from, and the higher the likelihood of owned assets influencing the generated narrative.

Pursue Consumer Connections, Win With AI

The takeaway here is: produce a higher volume of data-informed owned assets to pursue authentic connections and the rest will follow. Delighting your audience by knowing what they want and satisfying them with an abundance of useful content also satisfies the AI tools that consumers increasingly rely upon. Tightening up your brand’s control with owned assets in the digital space will pay dividends and result in AI strengthening your position instead of undermining it.

The best path is proactively telling your brand story through owned assets. A laser focus on investing in, growing, and optimizing your network of assets offers more control over your story, strengthening its authority, accuracy, and diversity as well as defending it from competitors on one hand, and AI-generated erosion on the other. Each individual owned asset tells a piece of your story to consumers and to the AI tools that they are fast adopting. Today’s brand marketers must be ready to evolve.

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Artificial intelligence (AI) is already transforming many parts of our lives. By using machines to mimic human intelligence, it can be used to do everything from personalised shopping to driving autonomous vehicles and even fraud prevention.

One of AI’s newest and most vital applications, however, is in forecasting the weather. Given the increasing unpredictability, frequency and severity of natural catastrophes and extreme weather events caused by climate change in recent years, it’s an invaluable tool in tackling the problem.

By monitoring historical data of weather patterns and being able to predict what may happen, AI can be used to foresee potential disasters before they happen. If done sufficiently ahead of time, this enables communities and businesses to take the appropriate action, whether that’s building or reinforcing their defences, or moving away from the area.

The traditional method

Previously, meteorologists would have had to wade through vast amounts of data manually to spot these key weather trends. But all that has changed almost overnight thanks to the advent of AI tools. 

By using machine learning algorithms, AI systems can analyse all this information in a fraction of the time, and more accurately and efficiently, without the risk of human error. By looking at historical weather patterns and comparing them to the current conditions, it can then predict potential anomalies that may result in natural catastrophes.

Forecasting earthquakes

Among the biggest risks in recent times are earthquakes. Only in February, a Mw 7.8 earthquake struck southern and central Turkey and northern and western Syria, causing almost 60,000 deaths and destroying thousands of properties and vital infrastructure.

Predicting earthquakes is notoriously challenging due to the complex nature of seismic activities. But, by using machine learning to examine the data, AI has been proven to be highly effective in forecasting such events.

In Japan, a country which is prone to sudden and devastating earthquakes, AI systems have been implemented to predict seismic activities. By analysing the data, they have been able to provide early warnings, thus helping to minimise damage in the area and to save many people’s lives.

Flood predictions

Another problem that has become increasingly prevalent is flooding.  When they strike, floods can be devastating, causing widespread damage, especially in densely-populated areas or regions. 

But by using AI, scientists can make early and accurate flood forecasts. This can help in organising people to be evacuated and other preventive measures to be put in place, such as erecting flood defences like dams and levees. 

India has been particularly susceptible to flooding in recent years. So, Google and the Central Water Commission partnered to implement an AI-based flood forecasting system for the country, which, since its launch, has significantly improved the accuracy and timing of flood warnings, thus reducing potential damage.

Wildfire detection

Wildfires have also increased in severity and frequency in recent years. Previously, they were largely limited to places such as California in the US, but now they are becoming increasingly common in regions like Europe and Australia too.

Detecting wildfires early is key to significantly reduce their potential destruction. By analysing satellite imagery, AI can spot signs of a wildfire much faster than traditional surveillance methods.

Given it’s hot and dry climate, California is prone to devastating wildfires. But thanks to newly-implemented AI technology, wildfires can now be more quickly and accurately predicted and detected, helping to fight fires more effectively, thus saving both lives and property.

Future of AI in disaster management

Given the promising results so far, AI looks set to play a key role in disaster management moving forward. But its implementation in this area isn’t without its challenges, namely data privacy and security, and the high costs associated with its adoption and development, which can be a significant barrier to entry.

Then there’s the lack of qualified and skilled professionals needed to implement, develop and maintain these systems. Added to that, AI also raises significant ethical challenges, such as biased or discriminatory algorithms and the potential loss of human jobs.

To overcome these challenges, the technology providers need to collaborate with governments to ensure data privacy and security. They should also seek funding from global organisations and private investors to support AI’s implementation and development.

Investment in education and training programs is also key to developing skilled AI and disaster management professionals. Additionally, the ethical concerns can be addressed through the use of transparent development and implementation processes, highlighting the benefits of AI’s application within disaster management.

AI benefits

Despite its challenges, AI’s benefits by far outweigh its downsides. As well as providing faster and more accurate forecasts and early warnings, it also results in better decision-making through the use of data-driven insights and suggestions.

Other key advantages include resource optimisation, with AI being used to more effectively allocate resources, ensuring timely response and recovery efforts. It can also help to minimise property damage and save lives, as well as allowing for a better preparation and response.

As AI systems are continually being improved through machine learning, they are becoming increasingly more accurate in their predictions. Their capability to predict and prevent natural disasters is expected to be only further enhanced by integrating AI with other technologies such as the Internet of Things and Big Data.

AI has huge potential in predicting and preventing natural disasters. As it becomes increasingly more widely adopted across the world, it can play a vital role in minimising the damage caused by natural disasters and, ultimately, saving lives.

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In fact, companies strategically scaling their AI are experiencing nearly two times the success rate and three times the return from AI investments compared to companies pursuing siloed proof of concept solutions. With AI tools becoming more advanced and human-like by the day, it’s clear that the tech is here to make the future of business more effective.

So much technology is evolving and becoming available to streamline information processing and other administrative tasks that more business leaders have felt compelled to adopt AI tools. As a result, AI and machine learning have inevitably harmonized with the corporate world—in ways you probably don’t even realize.

One of the areas that will benefit the most from AI technologies is business communications, taking internal comms to the next level.

The Rise of AI for Business Comms

At a glance, internal comms seem less compatible with AI than other departments, partly because tools helping with content creation are perceived more as a threat than a productivity enabler. It’s easy to fall into the “Robots are coming!” anxiety, but the truth is that while AI is here to assist in repetitive or research-based processes, it can’t replace the human touch that is required to make real connections and recognize communication nuances. 

In fact, in an era of digital transformation and the restructured workplace, AI and machine learning stand to improve some of the biggest internal comms challenges—including alleviating employees’ engagement fatigue—while keeping them updated and connected with relevant materials.

As more businesses invest in digital communications systems, a massive data cache is produced. As a result, the data captured includes workplace discussions, thought processes, employee preferences, and other actions that are ideal for applying AI technologies to better identify and classify the content that organizations communicate to their teams. At Poppulo, for example, we use natural language processing to identify which content topics resonate the most within an organization. Our AI tools help automatically categorize communications by theme, including learning and education, IT or HR-related topics, diversity, and inclusion, or other key themes at scale.

The goal should be for businesses to understand how their internal comms content is trending and where they need to adjust. And using an automated platform helps determine which topics spark the most engagement and how to organize categories based on levels of engagement.

For professional communicators, AI is another tool in their arsenal to help improve the flow of information between business leaders and their employees. These interactions can tell us much more about how and why business decisions are being made, enriching the insights provided in employee surveys. 

A good business communication strategy is vital for any organization’s success because understanding what employees expect is essential for thriving in the market. AI can integrate into almost any IC strategy and optimize the plan of action, from marketing to operations to customer service—the applications are nearly endless. According to an Accenture report, three out of four C-suite executives believe that if they don’t scale their AI capabilities in the next five years, they’ll risk going out of business entirely. For many business leaders, it’s time to fully embrace AI and implement it into IC.

What AI and Machine Learning Can Do For Your Business Communications

Although we’re not exactly at the “robots taking over the world” phase in AI and ML, these tools will take over laborious and repetitive tasks and provide insights from the data they capture. These insights will help internal communicators supply employees with information specifically for them, making them more effective in their roles. Some AI benefits include:

• Assisting in relationship building
• Creating a positive work environment
• Boosting efficiency with automation
• Increasing productivity
• Reducing misunderstanding

By finding and delivering relevant information faster than humanly possible, artificial intelligence will help streamline communications and improve the ability of businesspeople to make smarter decisions. Here are a few examples of how.

Boost Engagement and Advocacy

According to Gallup’s State of the Global Workplace 2022 Report, only 21% of the global workforce is engaged in the workplace—with the other 79% withdrawn due to poor communication from IC. Communication is a necessary part of business, with different employees having their own personal preferences and expectations. From a business perspective, it’s imperative to nail down the proper messaging to minimize confusion and improve company culture. Using machine learning patterns makes it much easier for communicators to create accurate user personas for ad targeting and understand when employees will best engage with messages.

Leveraging AI to help with content creation frees up valuable time for internal comms teams to do the more strategic parts of their role. They pay more attention to the things that add the most value to organizations, like alignment with departmental priorities, channel planning, and meeting employees’ growing needs and demands without half of the struggle.

With the incredible amount of data we have at Poppulo, our AI-enabled platform provides business leaders and managers valuable insights into their company, helping them identify and address any issues affecting employee morale or satisfaction. Our clients send over 200 million email communications monthly with higher-than-average open rates. Since we offer AI-generated insights, we’re reducing the time it takes our customers to categorize and analyze those emails.

Improve Personalization

Internal comms utilizing AI for redundant tasks and personalization can ensure employees receive relevant content consistently and on time. AI can be used to segment employees based on factors such as job role, location, and interests, helping internal communications teams to tailor content to specific employee groups. This can also help employees feel seen and more engaged with the organization. 

This approach saves time, and resources; and perfectly symbolizes the future of AI and IC—working hand-in-hand to streamline tasks yet deliver high-quality human communications. When an employee doesn’t need to do extra work just to find resources, information, and subject-matter experts to do their job, they feel happier and more productive, reducing the time spent searching through irrelevant information.

Enhance Customer Experience

AI and ML technologies augment the customer experience. These technologies allow you to deliver personalized and user-centric experiences catered to your target audience at every touchpoint. With all the advantages AI tools can bring to your business, the ultimate beneficiaries are the customers. Not only do they receive the payoff of your newly optimized business processes and productive teams, but they’ll also form a strong, positive relationship with your brand and be more likely to repeat business and bring referrals—a win-win situation for everyone.

Propel Internal Communications

Internal communication is the lifeblood of modern businesses, and automated advancements can bring your organization a competitive edge while augmenting internal operations. Crafting the right message for the right platform to garner maximum impact with AI and machine learning helps you understand what your employees want so that you can deliver on it—and then learn from that interaction to do it better next time. 

In the near future, AI will be able to take extensive content, summarize it for you—even to a few lines for digital signage, extrapolate a mobile post to long-form text, and even make quick adjustments to messaging that will drive and optimize impact and response. Machines can filter through data and determine what information is most crucial to your employees, customers, and bottom line. Ultimately, AI is an incredible tool that will make the jobs of communications teams easier and more effective. But AI depends on the humans at the helm to strategically execute and lead businesses to stronger relationships both internally and with consumers.

Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!

The post Companies That Aren’t Using AI to Elevate Their Comms Strategy Are Missing Out: Here’s Why first appeared on AI-Tech Park.