Hello Hussein, can you share with us your professional journey and how you became involved in the field of AI and technology, leading to your role as co-founder of Momentable?

I’ve always been fascinated with technology and sci-fi. AI is one of those things that sticks in your mind, and you can’t help but think about it. 

I studied engineering and worked in tech, and even with all the advancements in technology we have been witnessing in the past two decades, AI was one of those things that we always thought would remain a sci-fi pipe dream for a long time. 

This is not because there was nothing happening. But those who work in tech know it takes a while for the evolution of these technologies, and advancements are usually several degrees of separation from the regular user. 

I’m always learning, reading, and building tech products, so AI was a field of study; however, implementing it was never accessible for early-stage products. 

The status of AI has forever changed. OpenAI’s ChatGPT launch has had a remarkable impact on the field of AI and tech in general. AI is now available for regular users. People like me working in tech can now use AI in everything they are doing, which will accelerate product development and will impact the kind of products we can build and deliver to customers.

In addressing concerns surrounding AI ethics, you mentioned the importance of regulatory measures, technological transparency, and societal readiness. Could you elaborate on how Momentable approaches these areas to mitigate potential ethical dilemmas?

With great power comes great responsibility. AI is a powerful technology, and it’s very easy for those wielding it to amplify the impact of the good and the bad in the work they do. 

While we, in the tech space, are doing our very best to build great products that deliver great value, we are not social scientists, psychologists, or public servants. So, we can’t be expected to regulate and supervise ourselves, nor can we evaluate the impact of these technologies and the products using them on the individual and on society. 

It’s great when companies have values, codes of ethics, missions, and visions; however, those are not enough. Just like we do not rely on drivers to drive safely, we have traffic laws, signs, lights, and we make sure people driving a car are licensed and trained. We need to do the same with technologies, which, I would argue, have a massive impact on shaping our future as a species more than anything we’ve ever had in our history. 

At Momentable, we are acutely aware of the impact of generative AI on our stakeholders, artists, cultural organizations, and art lovers. We engaged our stakeholders, ran several experiments where generative AI created artworks with input from artists, with their permission and consent. 

In addition to using AI to enhance customer experience on our platform, we are using the learnings to evolve our product and introduce Generative AI in a thoughtful way that adds value and advances the art and culture space.

How do you personally strategize and prioritize addressing the ethical implications of AI within Momentable’s projects and initiatives?

We start by listening to our stakeholders; artists, art lovers, clients, and team. From simple Slack messages, to meetings with artists who are friends of Momentable, to talking to the experts, and sharing YouTube videos of leading content creators in the art space. 

By taking in the input, feedback, concerns, and advice, we make sure we are thoughtful about the next steps we plan to take. In addition to the data and numbers we get from market reports, we use the qualitative input we gather to help us focus on where we can add significance. 

We understand the AI conversation is ongoing, and as the industry keeps moving at rapid speed, we must stay engaged, always learning, and maintain an open attitude.

As someone deeply involved in the AI industry, what advice would you offer to our readers who are concerned about the ethical use and bias in AI technology?

 Ethical use and bias are not new to tech; it’s further amplified in AI, particularly generative AI. Three core reasons lead to challenges in ethical use and bias in generative AI: 

1. Products are developed by the tech sector, which deals with many ethical challenges and major bias due to the lack of diversity. These challenges are amplified by keeping the technology and products developed closed, which. 
2. The data used to train and develop the AI models also has many issues with how it was sourced, used, and also carries within it implicit bias. These issues are amplified even further since many AI models keep their. 
3. The nature of generative AI severely exacerbates these issues and challenges. By producing content mimicking the training data using code developed by a sector dealing with ethical challenges and bias, generative AI is adding to the problem with every answer it provides. 

Your ability to influence or mitigate the ethical use and bias in AI depends on where you are in the systemic hierarchy of the tech ecosystem. As a product builder and customer, there is very little you can do to change things.

The sector requires regulatory and systemic intervention. But it can’t be done without engaging with the stakeholders and having them at the table.

This is not to say that as a consumer you do not have any power; you do. You can make your voice heard through social media, customer feedback, calling your representatives, and voting.

I encourage you to learn and gain some hands-on experience to develop your understanding and appreciation for the technology and how powerful it is.

In your view, what role do education and skill development play in preparing society for the impact of AI, particularly in addressing job displacement and socio-economic challenges?

As technology continues to evolve and take over more of our roles at work as we know it today, the transformation will have massive implications on our lifestyles, how we do things, and even how we define ourselves and the value we assign to our roles.

We need to stop thinking about education as a precursor to job placement. This limited view meant that education is always lagging behind the needs of the economy and helplessly lacking in addressing any of the needs of our society.

Education must focus on the future beyond the jobs of today or tomorrow. It must graduate innovators and value creators. Education must focus on graduating creatives skilled at solving the problems we face 50-100 years from now.

To create a better world, schools and universities must become open spaces for research and discovery, where art, technology, and culture collide and fuse to inspire new thought forms.

Could you share some examples of how Momentable ensures transparency in its AI technologies, particularly regarding decision-making processes and algorithms?

We do everything in collaboration and coordination with our key stakeholders. This gives us a baseline to measure against.

It’s easy to be influenced by what we read and watch and think it’s an accurate representation of the world. To avoid the pitfalls of building on the learnings and understanding within our own bubble, we always start by expanding our perspective. Put simply, we talk to people.

It’s slow, inefficient, and important. If we are going to use technology to impact people’s lives, we better speak to those people, learn from them, understand their perspective, and take into consideration what matters to them.

This approach led us to experimenting with AI without limitations at the very beginning. We shared our results with our community: our users, partners, artists, and our advisors.

We wrote about our process and shared it through workshops and webinars, and we took on all the feedback we could gather.

While the inclination at the beginning was to keep things close to the chest, this open and transparent approach helped us focus on the areas where AI can add the most value in our work.

In the case of Momentable, we use AI to help us deliver the best user experience and make it easier, faster, and better for our users to use Momentable and capitalize on the democratized access to the largest collection of great art in the world.

Considering the rapid advancements in AI, how do you navigate the balance between innovation and the need for regulatory control within Momentable’s operations?

Until a clear regulatory framework is developed and introduced, like most companies, we continue to operate within the regulatory frameworks for the tech sector and business in North America and Europe.

At Momentable, we are governed by our internal ethical code and guided by our strong sense of mission to bring the best visual experience to customers through innovative software, personalization, and immersive storytelling.

With our stakeholders being engaged and involved throughout the process, we make sure we create a space for creativity and innovation with boundaries that keep our work focused on adding value with minimal negative impact on our stakeholders.

What steps do you believe are necessary for governments and regulatory bodies to effectively oversee AI development and ensure alignment with ethical and safety standards?

Bring all the stakeholders, industry players, academia, builders, users, communities, regulators, and the public to the table to collaborate and constructively build for the benefit of all.

Form a steering board and create a framework for engagement so that adding value to all stakeholders is a main condition.

Be clear and transparent about the objectives and outcomes you are after.

Develop a roadmap with realistic short-term goals and objectives, in addition to highlighting the mid-term and long-term areas of focus.

Maintain connection with stakeholders through regular roundtable meetings. Share regularly, and invite input, feedback, and criticism.

Keep moving forward and getting things done.

From your perspective, what are the most pressing ethical dilemmas or challenges currently facing the AI industry, and how can businesses and individuals contribute to addressing them?

The most pressing ethical dilemmas or challenges currently facing the AI industry can be viewed from three perspectives: long-term, mid-term, and short-term.

Long-term: AI is going to play a significant role in shaping who we are as a species and how we live our lives. Just like there are generations today that do not know a world without smartphones and the internet, we will have generations who do not know a world before AI, and we will have a generational gap and challenges that arise from this gap. Older generations will feel left behind, while new generations will be heavily dependent on AI and AI-enabled devices. The energy consumption will be extreme, and errors caused by AI will have massive ramifications, especially since AI will be embedded in essential services, infrastructure, and defense. In many ways, some might say we will be at the mercy of AI, and even if AI doesn’t become aware or evil, mistakes AI makes are possibly disastrous.

Mid-term: AI will cause massive socio-economic shifts that require offering support and help to those individuals and businesses impacted until the transformation is complete. Changes to the education sector are inevitable, and the evolution of our economy will have positive and negative implications that must be observed and prepared for. Focusing on the energy sector, making sure equitable, democratized, and open access to AI tools and training is crucial. New incubators, accelerators, resources, and support services must be made available to help manage the shift and protect society and the economy from the negative implications. As more people become proficient in using AI tools, they will be able to build massive businesses that compete with existing businesses, and just like smaller teams were able to disrupt businesses with software, now individuals can disrupt businesses with a few tools. Not to mention the malicious use of these tools can lead to even more challenges and threats.

Short-term: The immediate priority lies in creating spaces for engagement, learning, and hands-on experience with AI. It’s crucial to create an environment where individuals and businesses can understand, interact with, and ethically utilize AI technologies. This involves opening dialogues, providing educational resources, and encouraging ethical AI use through policy advocacy and community involvement. Businesses can lead by example, ensuring their AI applications adhere to ethical standards and are transparent in their operations, and share their learnings and discoveries. By actively participating in these efforts, we can navigate the complex and ever-changing terrain brought forth with the advancements in AI.

Finally, what are your thoughts on the future of AI and its potential to positively impact society, and do you have any closing remarks or key insights you’d like to share with our audience?

The future of AI holds remarkable potential for bettering every part of our lives. This technological evolution will accelerate advancements and enable breakthroughs in healthcare, climate science, education, and the sustainability of our species.

This optimistic vision is dependent on democratizing access, sharing openly, and ensuring there is transparency in how AI models work.

In addition, we must have an unwavering commitment to ethical principles, inclusivity, and equitable access to AI technology, prioritizing creating and delivering value to ensure all technological advancement, including AI, is a catalyst for positive change.

I invite you, the reader, to think of yourself as an active participant in this future being shaped today. Do not be a spectator; instead, take part, engage with AI, learn, build, and innovate. 

Now more than ever, the barriers to entry are minimal, and you can make an impact with less time, money, and resources. Embrace your roles as a shaper of the future, and engage with the world being created in front of our eyes with your thoughts, words, and actions for the greater good.

Hussein Hallak

Co-founder of Momentable

Hussein Hallak is the Founder and CEO of Next Decentrum, the launchpad for the world’s most iconic NFT products.  Heavily experienced in the art and technology fields, his recent roles include General Manager of Launch, one of North America’s top tech hubs and startup incubators, where he helped over 6500+ founders and 500+ startups raise over $1 billion. In 2019, Hussein joined 3 tier logic as VP of Products & Strategy and worked with some of the world’s most valuable brands including Universal Studios, P&G, and Kimberly Clark.

Hussein writes and speaks about startups, blockchain, and NFTs, and advises several blockchain and tech startups including Ami Pro, Gigr, Mobile Art School, Fintrux, Majik Bus, Traction Health, Cloud Nine, and Peace Geeks.  He was recognized in 2019 as one of 30 Vancouver tech thought-leaders and influencers to follow and has been featured in Forbes, BBC, BetaKit, Entrepreneur, DailyHive, Notable, and CBC.  When not building products, he enjoys writing, reading, and engaging in meaningful conversations over good coffee, and his favorite pastimes include playing chess with his kids, binging on good drama and science fiction, drawing, and learning new guitar licks, sometimes all at the same time.

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Vsu, as the SVP of Content Engineering for Avalara, can you provide insights into how your role involves managing Avalara’s tax and compliance content database and driving the company’s use of AI and machine learning?

I lead the research and engineering teams that maintain and grow Avalara’s compliance content database, which powers our compliance products that make tax compliance easy and fast for our customers. I also drive the vision and use of artificial intelligence (AI) and machine learning (ML) across Avalara both for internal use and use in our products.

Given your experience in overseeing large-scale engineering programs at Optum and Thomson Reuters, how has that background influenced your practical approach to implementing AI, and what perspectives do you find refreshing for the audience?

Experimenting with AI versus implementing AI at-scale are quite different. Implementing AI requires the same operational sophistication needed to run any large software engineering solutions — they need to be resilient and be able to deal with unexpected situations. Implementing AI at-scale also requires high levels of monitorability and traceability. In scale implementations, the AI models must be supported by a solid engineering infrastructure, architecture, and overall operations.

Can you elaborate on Avalara’s sales tax plugin for ChatGPT? What specific use case can ChatGPT users solve with the plugin? 

Avalara launched the first tax compliance plugin for ChatGPT that allows users to ask the platform to calculate and research general sales tax rates based on their location. We were also the first tax compliance software provider to work with OpenAI to use its protocol to build an integrated plugin for ChatGPT. 

Businesses and consumers who have ChatGPT Plus accounts can install the Avalara plugin from ChatGPT’s plugin store. Once the plugin is installed, users can ask ChatGPT for general sales tax rates on the sale of tangible personal property by location or to calculate general sales tax on a specific sale by location. ChatGPT chooses when to use the plugin based on the question and conversation. 

How does Avalara differentiate itself by applying AI and machine learning in a practical way that makes a significant business impact, as opposed to focusing on creating flashy new AI experiences?

At Avalara, we’re on a mission to make tax “less taxing, more relaxing” by automating the steps of the tax compliance process. We think about AI as a strategic tool to allow us to work better, smarter, and faster in pursuit of this mission. 

We’re looking for places across our business and throughout the customer journey where AI can provide value around efficiency, simplicity, and more. For example, we use AI-powered tax classification tools to quickly and efficiently classify products or services to aid in sales tax taxability determinations and cross-border tariff determinations.

How has the integration of AI technologies allowed Avalara to streamline its processes and deliver better results? Are there specific examples or use cases that highlight the tangible benefits?

One step of the sales tax compliance journey for many businesses it to manage exempt sales, also known as exemption certificate management. Exemption certificates contain a lot of different information depending on the supplier, buyer, transaction details, and more. At Avalara, we process forms and notices and leverage AI to automatically extract information, which helps our customers manage and maintain exemption certificates more easily and effectively.

How do you balance the need for innovation with the practical application of AI to ensure it aligns with Avalara’s business goals and objectives?

We conduct regular hackathons to encourage innovation and experimentation within our engineering teams — many of which lead to new innovations for Avalara. When we find promising ideas, we do an ROI analysis and assess alignment with our strategic priorities before we invest in any of these ideas.

Can you share any challenges or lessons learned in applying AI in a tax automation context, and how has Avalara addressed these challenges to achieve successful outcomes?

At Avalara, we’re making sure that being quality-focused is a priority across the business. We know that AI is a capability that we can leverage, but it’s not a cure-all. That’s why we’ve been intentional about developing and releasing policies around AI to ensure that we are all following responsible AI guidelines and using it safely and efficiently across the business.

What strategies have you employed to foster a culture of innovation and collaboration within the research and engineering teams at Avalara?

We employ numerous strategies to ensure that we are fostering a culture of innovation and collaboration within Avalara Engineering. One key example is our use of hackathons, which allows members from across engineering and the company at-large to participate in the hackathon, We also set aside two weeks each quarter designated as an “innovation sprint,” which is time that our teams can spend focused on new innovation, learning and forward thinking planning.

We also make a range of trainings available to allow our teams to continue learning new skills and best practices as they emerge within the industry at-large. 

In what ways do you see the landscape of tax automation evolving with the continued integration of AI and machine learning technologies? Are there emerging trends or developments that you find particularly noteworthy?

What we’re seeing right now is that more customers are starting to ask how AI will be used in compliance. I believe there is an expectation that gradually AI will help in assisting with the compliance journey. At a time where many people are leaving some compliance-related professions, we’re seeing a growing dearth in skill sets that create an opportunity for AI to help with many mundane manual tasks.

What areas do you see further opportunities for leveraging AI to enhance the company’s offerings and maintain a competitive edge in the tax automation software market?

Avalara was there at the beginning of the automation journey for tax. We have always been a pioneer in the tax space by being among the first to leverage the power of the best in technology, so AI will be no different. We are harnessing the power of AI to help us do what we do best already, in an effort to bring more scale and convenience to customers. We will continue to identify other areas within our products, customer experience, and internally where AI can help us deliver tax compliance automation to businesses with greater efficiency and speed at-scale.

Vsu Subramanian

SVP of Engineering & ML Services at Avalara

Vsu leads the research and engineering teams responsible for managing Avalara’s proprietary tax and compliance content database. He also drives the company’s use of AI and machine learning. He previously held senior leadership positions at Optum and Thomson Reuters overseeing large-scale engineering programs.

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Generative AI technology like ChatGPT has brought the world one step closer to the futuristic society envisioned by forward-thinking science fiction writers. But will this future be a utopian or dystopian one? Time will tell. In the meantime, businesses must understand and leverage AI’s burgeoning influence over the zeitgeist to build favorable public sentiment about their brand’s reputation. It has become a make-it-or-break-it moment for corporations in the battle against the spread of AI-led misinformation.  

AI Angst 

Opinions run hot, cold, and everywhere in between when it comes to AI’s possibilities and ramifications. A recent survey by the Pew Research Center found that 52% of Americans are expressing greater concern rather than excitement regarding the increasing dependence on AI. This sentiment has risen by 14% since 2022. The current era represents a Cognitive Industrial Revolution teeming with potential, including AI’s provocative ability to sway public opinion.

Businesses and their communication teams must fortify their message with an if-you-can’t-beat-them, join-them approach, leveraging AI’s influence over public opinion and using that to their advantage. By being open and transparent, companies can direct the narrative and strengthen their brand’s image by becoming thought leaders in their industry—with more communication, not less. 

Better Communication Through Thought Leadership 

Businesses are urged to become thought leaders, effectively communicating their brand message through credible third-party channels such as the media and influencers. The influencer marketing sector is booming and is expected to reach a value of $24 billion by year-end. AI is being embraced by 63% of companies for campaign executions, with 55% utilizing it to pinpoint influencers. Moreover, 33% of the total market capitalization of the S&P 500, attributed to goodwill, is impacted by public relations (PR) strategies and tactics.  Brands must proactively shape and manage their narratives to influence their target audiences. Failure to do so relinquishes control of these narratives to others—rendering marketing, and sales, less effective.

However, implementing this shift necessitates moving away from stale approaches in public relations and public perception. Traditional methods in these areas have been neglected, with only a minority understanding the strategic guiding of public opinion. As AI-generated content becomes more prevalent, the importance of compelling storytelling at the beginning of the customer journey or at the start or top of the PR>Marketing>Sales funnel cannot be overstated. 

Succeeding in this evolving era of cognitive engagement requires focused attention on three crucial areas, each demanding commitment and education:

1. Mastering the Codified Body of Knowledge of Public Opinion 

Companies benefit from becoming better acquainted with the established principles and empirical data that mold public perception. Delving into case studies, contemporary theories, rules of engagement, and the evolution of public relations offers valuable insights into the intricate dynamics that shape public opinion. Armed with this knowledge, organizations can develop strategies, and tailor messages that strike a chord with audiences, enhancing the prospects of broader acceptance and dissemination.

2. Understanding Algorithms

By comprehending the algorithms governing social media platforms, search engines, and the media, organizations can enhance their ability to forecast and manage the distribution and reception of their content among their desired audience. This guarantees that their brand message gains momentum and effectively connects with their customers. 

3. Developing Strong Strategic Communications Plans 

As mentioned above, businesses must cultivate persuasive communication strategies capable of quickly adjusting to the unpredictable nature of technology and public opinion. This involves promoting clarity in their brand messaging, maintaining consistency across different platforms through all channels of the PR>Marketing>Sales funnel, and promptly responding to audience feedback. A thoughtful communication strategy integrates crisis management protocols to address any threat to brand messaging proactively.

It’s a Cognitive Revolution

By putting these measures into action, businesses can use AI to their advantage and cultivate a positive public perception, guaranteeing their continued brand relevance that rises above all the digital noise.

Understanding the dynamics of shaping public opinion enables businesses to craft their brand their way. AI is leading a Cognitive Revolution, democratizing technology, and empowering individuals to expand their creative potential. It can be leveraged to be a tool, not a replacement for brand communication. 

Many businesses fail to appreciate their capacity to influence public opinion. By mastering the principles of public perception management, they can elevate their brand awareness, be in more control their brand’s trajectory, and create an atmosphere of public trust in their brand’s message. The more people interested and comfortable in doing business with brands will show in better marketing ROI and sales velocity.

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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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In the rapidly evolving landscape of artificial intelligence (AI), media companies and other businesses alike continue to find themselves entangled in a web of lawsuits and public criticism, shining a spotlight on the issue of ethical transparency. Journalism has long been plagued by issues around deception — consumers often wonder what’s sensationalism and what’s not. However, with the latest casualty in the ongoing Sports Illustrated debacle, whose reputation greatly suffered after being accused of employing non-existent authors for AI-generated articles, a new fear among consumers was unlocked. Can consumers trust even the most renowned organizations to leverage AI effectively? 

To further illustrate AI’s negative implications, early last year Gannett faced similar scrutiny when its AI experiment took an unexpected turn. Previously, the newspaper chain used AI  to write high school sports dispatches, however, the technology proved to be more harmful than helpful after it made several major mistakes in articles. The newspaper laid off part of its workforce, which was likely in hopes AI could replace human workers. 

Meaningful Change Starts at The Top 

It’s clear the future of AI will face a negative outlook without meaningful change. This change begins at the corporate level where organizations play a key role in shaping ethical practices around AI usage and trickles down to the employees who leverage it. As with most facets of business, change begins at the top of the organization.

In the case of AI, companies must not only prioritize the responsible integration of AI but also foster a culture that values ethical considerations (AI and any other endeavor), accountability, and transparency. By committing to these principles, leadership, and C-level executives set the tone for a transformative shift that acknowledges both the positive and negative impact of AI technologies.

To avoid any potential mishaps, workforce training should be set in place and revisited at a regular cadence to empower employees with the knowledge and skills necessary to combat the ethical complexities of AI.

However, change doesn’t stop at leadership; it also relates to the employees who use AI tools. Employees should be equipped with the knowledge and skills necessary to navigate ethical considerations. This includes understanding the limitations and biases as well as learning from the mistakes of others who’ve experienced negative implications using AI technologies, such as the organizations previously aforementioned. 

By cultivating a well-informed and ethically conscious workforce, organizations can remain compliant while also bettering the workplace environment, all while mitigating detrimental risks. The collaborative effort of corporations and their employees is an essential stepping stone to building a more positive outlook for the use of AI and other technological advancements to come.

How to Improve Transparency Around AI Usage

Tom Rosenstiel, a professor of journalism ethics at the University of Maryland, emphasized the importance of truth and transparency in media specifically. He argues that experimentation with AI is acceptable, but attempting to conceal it will inevitably raise ethical red flags for consumers. “If you want to be in the truth-telling business, which journalists claim they do, you shouldn’t tell lies,” Rosenstiel asserted. Lies, consumers have asserted, include failing to share how articles are being written, such as with the use of AI. 

The media landscape’s ongoing transparency struggle with AI is further highlighted by a lawsuit filed by The New York Times against Microsoft and OpenAI in December. The Times alleges intellectual property violations related to its journalistic content appearing in ChatGPT training data. This ongoing legal battle illuminates a slew of other AI-related copyright suits, with experts noting a more focused approach to the causes of action.

With the rise of AI-related lawsuits and public scrutiny over AI usage growing, this begs the question, how do businesses bridge the gap between consumer distrust and using AI in an ethical way that streamlines workflows?

Boosting the Understanding of the Collaborative Effort Between AI and Humans

Enhancing transparency around AI usage in media involves a comprehensive multifaceted approach. The first, arguably most important step to take is for media companies (and any other business) to not only acknowledge the integration of artificial intelligence but actively share the role it plays in content creation. This includes highlighting whether AI was used for researching, editing, writing, or a combination of the three. In turn, media organizations must implement clear disclosure in any easy-to-locate place on their web pages, openly informing the audience when and where AI tools were used for the production of articles. 

Educating the general masses about AI and its role in content creation is equally important. Businesses can take a more proactive approach to help consumers understand how AI technologies work by offering insight into the inner workings of AI (such as its algorithms), the ethical guidelines that govern their use, and how much human oversight is involved. For example, sharing if the work was edited by an actual person or if AI was used for research but written by a human. 

Public awareness campaigns, informative articles, and interactive platforms can all come into play to help bridge the knowledge gap, empowering consumers to make informed decisions about the content they choose to consume. By improving transparency and calling attention to exactly how AI will be used, businesses only stand to build greater trust with their intended audience and mitigate concerns. Consumers are proving authenticity aligns with their core values, and businesses must comply with consumer expectations to stay ahead.

Lastly, establishing industry-wide standards for AI usage in journalism and every other industry can contribute to driving transparency forward. This begins with collaboration among media organizations, tech developers, and ethical experts to generate clear guidelines that outline best practices for AI usage. By developing these standards, businesses are looped into how and where to showcase disclosure protocols and how to address potential biases in AI algorithms. Clear standards also ensure every player upholds its commitment to transparency, leading to improved trust for both creators and consumers as AI continues to play a larger role in journalism.

Establishing A New Era of AI Trust

In the face of escalating AI-related lawsuits and growing public concern, the only clear route for businesses to take is to work diligently to bridge the gap between consumer distrust and ethical AI usage. The evolving landscape of AI demands a closer examination of how others have failed and what businesses can learn from these setbacks for a brighter road ahead. The Sports Illustrated, Microsoft, and Gannett examples highlight the need for prominent companies to set a more positive example, striking a balance between innovation and maintaining public trust.

To navigate these challenges successfully, organizations will need to become transparent about how they’re using AI. This starts with acknowledging how exactly they’re leveraging AI, and sharing if it’s a collaborative effort between AI and humans in content creation. Implementing clear disclosures, whether in the form of an individual AI usage landing page or standardized labels for AI-contributed content, helps ensure consumers stay in the know, building more trust through openness. The ongoing legal battles also bring attention to the need for industry-wide standards that outline best practices in AI integration, ensuring greater uniformity and understanding.

In an era where consumer trust has the power to make or break a business, all publicity is not necessarily good publicity. This is evident by the continuous negative attention large corporations continue to receive, months after these incidents take place. But it’s not all doom and gloom for AI. A recent study found that 31.8% of respondents think generative AI and/or machine learning will help their business a lot this year. The ethical use of AI remains a challenge to accomplish across the board, however, lawsuits and public backlash, as detrimental as they may be, are undoubtedly paving the way for a more harmonious future.

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Artificial Intelligence (AI) has been developing at a rapid pace and has been integrated into a growing number of applications across every industry. AI continues to widen its capabilities to assist in a variety of daily tasks but, as can be expected with any Internet-based technology, AI also has a dark side. As cyberattacks have grown in volume and complexity over the last few years due to Covid-19, what could cybersecurity and AI look like going forward? If you want to know more about how Covid-19 affected cybersecurity, check out our blog “Cybersecurity in the post Covid-19 world.”

What Is AI and Why Is It Getting So Much Attention Lately?

Artificial Intelligence is, in a nutshell, a technology that is able to start and augment tasks that are usually done entirely by humans. Like humans, AI learns from those tasks and applies those lessons to future actions, which in turn allows it to learn and improve the way it completes a task. Rinse and repeat. One of the well-known AI applications you may have heard of is ChatGPT, the powerful, fast-growing consumer app, capable of writing about endless topics with speed and sophisticated language skills based on a seemingly infinite depth of knowledge.

But did you know that AI has been around for decades? Take Grammarly for instance; this tool has been in circulation for 14 years and uses a powerful method of Generative AI to assist in writing proper sentences. Why has there been a massive upscale in AI visibility recently? After ChatGPT, a conversational AI model developed by OpenAI, burst onto the scene, it seems like there has been an acceleration of Artificial Intelligence implementations around the world. From image recognition, generation and optimization to website development, moderation and authorship, new uses for AI are continually being unearthed. Now that we see the potential of AI and how quickly it’s moving, that brings up the next question: will the power of AI be helpful or hurtful to cybersecurity as we know it, or both?

AI Entering the World of Cybersecurity

AI is already surprising the world of art by producing pieces in any style, on demand. It’s capable of digging up facts and writing on highly varied topics. If AI can act like a jack-of-all-trades while delivering the comprehensive power of high end search engines, why can’t they shatter security protocols too? Or perhaps enhance them?

It’s no secret that AI has been shown to be useful in automation and the same goes for AI in the cybersecurity field. Some AI security solutions are able to detect patterns to discover malicious code and make cyber predictions based on historical data. Some companies have also used AI by integrating it into their vulnerability and risk management programs to analyze their threat exposure and understand their security posture. It’s known that many security breaches involve human error, therefore the implementation of AI systems to monitor networks for all internal and external threats can assist in decreasing, if not entirely eliminating, these numbers. However, AI does not come without its own set of challenges and risks.

Preserving Privacy Around Artificial Intelligence

The cost of implementation for these types of integrated AI systems can be very high, making it an unattainable option for smaller businesses. Unfortunately, on the threat front, cybercriminals can use AI to devise and launch increasingly more complex cyber attacks. A study from 2023 by Blackberry stated that 51% of IT decision makers believe there will be a successful cyberattack credited to ChatGPT within the year.

Some malware architects have used AI to recreate malware strains and techniques described only in research publications, introducing an entirely new level of cyberattacks. For example, Chat GPT has successfully written functional malware that is capable of stealing sensitive files, encrypting hard drive content, and more. While this malware is not yet sophisticated, the speed and scale at which it can be produced is alarming. Additionally, other AI models have the capability to make attacks even more sophisticated by impersonating the voices of people and demanding money transfers. We can expect to see more attacks that are highly targeted social engineering attacks. Cybersecurity experts also state that AI-created deep fakes are finding ways to bypass biometric authentication, thus gaining access to protected systems.

We are still in the early stages of AI. These AI integrated systems need to be constantly monitored as they are far from perfect and can be prone to errors and biases. But it is clear AI products will continue to improve with time. When AI is used for corporate purposes, it is important that businesses which incorporate these AI systems ensure the technology is used for ethical purposes. These AI systems must be monitored to prevent them from being engineered to act against the corporate assets, and are not used to invade user privacy or circumvent traditional security measures – the  double-edged sword when it comes to security. While AI can provide benefits in threat detection and response capabilities, it can also pose a significant threat – be sure that your data is protected.

Defense Steps You Can Take Right Now to Protect Your Company

As it seems that every company is working on incorporating AI in one way or another into their businesses or products, make sure to understand how you can take defensive steps to secure yourself from the new forms of attack that AI has created. Below we have outlined what you can do right now: 

• Stay informed – As the world of AI continues to change, it is important to stay up to date on the latest trends and techniques that are related to AI, and more specifically those that are related to AI-based cyberattacks. This will ensure that your eyes are open to the new forms of attack so you can respond quickly and effectively.
• Train employees – While remote work increased the need for training employees, the new AI-based cyber attacks brings this to the forefront yet again. Teaching your employees how to detect and report suspicious activity can help you stay protected and secure. Additionally, as things update – make sure that your training is also up to date.
• Ensure you have robust passwords and access management set up – Utilizing strong passwords helps protect your information from unauthorized users. Strong passwords are not your only defense, but weak passwords that are easy to crack will  leave you vulnerable to attack.
• Encrypt your data – Not only does encrypting your data protect your company’s assets and helps avoid data breaches, but it also allows you to easily comply with regulations and privacy laws. Having an encryption defense strategy ensures that you are proactive rather than reactive to a threat.
• Engage with security professionals – Not sure where to start? That’s where NetLib Security can help. Our Encryptionizer solution secures databases in physical, virtual or cloud environments. Our data encryption solution can be deployed in a few clicks with no additional programming, and virtually no impact on performance. Best of all, you can start with a free trial to see if it’s the right fit for you and your company.

About NetLib Security

NetLib Security has spent the past 20+ years developing a powerful, patented solution that starts by setting up a formidable offense for every environment where your data resides: physical, virtual and cloud.  Our platform simplifies the process while ensuring high levels of security.

Simplify your data security needs. Encryptionizer is easy to deploy. It is a cost-effective way to proactively and transparently protect your sensitive data that allows you to quickly and confidently meet your security requirements. With budget considerations in mind, we have designed an affordable data security platform that protects, manages, and defends your data, while responding to the ever changing compliance requirements.

Data breaches are expensive. Security does not have to be.

NetLib Security works with government agencies, healthcare organizations, small to large enterprises, financial services, credit card processors, distributors, and resellers to provide a flexible data security solution that meets their evolving needs. To learn more or request a free evaluation visit us at www.netlibsecurity.com.

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Chatbots, powered by artificial intelligence (AI), are fundamentally changing how businesses operate and enhancing productivity and efficiency. Chatbots are computer programs designed to simulate conversation with human users via text or voice. From simple FAQ bots to complex virtual agents, chatbots are automating business processes and transforming how companies interact with customers and employees. The industry is still at the nascent stage but holds great promise and potential for the future.

In this post, we’ll explore the key ways chatbots are improving business productivity and efficiency.

1. Streamlining Customer Interactions

One of the biggest impacts of chatbots on business has been streamlining and automating customer service and support. According to Juniper Research, chatbots will deliver over $10 billion in global cost savings by 2024. Chatbots powered by natural language processing (NLP) can understand customer questions and requests, and then provide immediate answers without human involvement. This reduces customer wait times and frees up staff to focus on higher value work.

Chatbots like those from IBM Watson, Bold360, and LivePerson are providing 24/7 automated assistance across industries. Customers today expect quick, convenient service. Chatbots deliver by integrating with websites, apps and messaging platforms to engage customers in real-time, whenever and wherever they need help. ChatGPT has revolutionized the chatbot industry with its innovative approach of handling large language models and coming out with responses even to the complex questions that hitherto had not been seen. Google’s bard too is not far behind. It is going to be the battle of chatGPT vs Google Bard in coming years. Both would want to dominate the industry and that perhaps will lead to develop the solutions for any shortcomings that we see today.

2. Enhancing Employee Productivity 

From HR and IT help desks to project management and internal communications, chatbots are assisting employees and improving productivity. Repetitive administrative tasks and company policy questions are ideal applications for AI chatbots in the workplace. Employees get quick answers to common issues, freeing up HR and IT to tackle more strategic work. Chatbots make it easy for businesses to automate and outsource tech support taking the help of technology to handle queries from the customers and even in-house staff.

Intelligent chatbots act as virtual assistants, helping employees with tasks like booking travel, submitting expense reports, and setting up meetings. Through automation and conversational AI, they provide an intuitive way for staff to get things done quickly. Leading companies like Airbnb, Spotify, and Mastercard are implementing workplace chatbots to streamline communication and workflows.

3. Driving Business Efficiency

As AI systems, chatbots continuously improve through machine learning. They utilize data from past interactions to deliver ever more accurate responses and perform tasks more efficiently over time. Natural language interfaces allow chatbots to understand context and intent, engage in complex dialogue, and complete tasks just as a human assistant would. Companies are leveraging the technology to come up with optimal marketing strategies with AI and chatbots. These strategies help companies reach a wide customer base and scale new heights of business growth.

From customer service agents to sales reps and administrative staff, chatbots are taking on roles humans performed in the past at lower cost and with higher consistency. They don’t need holidays, sick days, or coffee breaks.  For many routine, repetitive tasks, chatbots simply offer a more efficient alternative. Intelligent chatbots are providing tremendous ROI through increased productivity and cost savings. But there are still domains where chatbots can’t function properly. Human touch and help are required in the form of on-demand tech support for various things like cybersecurity, cloud, office printer setup, computers, and network help. It is better to look for the human mind and hands when requiring help in these core business domains.

4. Transforming Customer Experience

Today’s customers expect ultra fast, personalized, and seamless experiences. Intelligent chatbots provide a superior level of convenience by serving customers anytime, anywhere at the pace they expect. With NLP and machine learning, chatbots analyze customer data and past interactions to make recommendations and tailor experiences to individual needs and preferences.

Chatbots are revolutionizing industries from e-commerce retail to banking and travel. They minimize wait times, reduce human errors, and allow staff to focus on higher value functions like complex problem solving and building customer relationships. By streamlining the customer journey, chatbots drive satisfaction, loyalty, and revenues.

5. Improving Business Agility

Chatbots give businesses more agility to respond to changing customer demands and needs. Chatbots understand the subtleties very well and can help in detecting QR code attacks as well.

Since they are software-based, chatbots can be quickly updated with new capabilities, integrations, and information. Companies can test variations of chatbots to optimize performance. This means they can rapidly deploy new automated solutions to improve the customer experience. With traditional customer service models, it could take weeks or months to train staff on new policies or programs. With chatbots, changes can be made instantly. Their flexibility enables businesses to be far more nimble.

6. Generating Actionable Data Insights

Unlike human agents, chatbots capture every interaction, enabling rich data collection. With customer permission, conversations can be recorded and analyzed. Using AI and analytics, businesses can gain data-driven insights to better understand customers and continually refine chatbot capabilities. Transcripts reveal how customers express needs, surface new topics and trends, identify areas for improvement and more. Chatbots provide a wealth of actionable metrics on customer behavior and intent. Data analytics drive optimization and maximize the business value from chatbot investments.

Conclusion

From large enterprises to smaller businesses, chatbots are fundamentally changing how companies operate; enhancing productivity, efficiency, and the customer experience. By automating repetitive tasks and processes, chatbots enable staff to focus on more meaningful, revenue-driving work. With intelligent self-learning capabilities, chatbots will only expand their capabilities and business value over time. Its clear conversational AI is transforming engagement across industries, delivering tangible returns on investment, and driving competitive advantage.

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Table of Contents

Introduction

1. How Does Generative AI Work?

2. Best Generative AI Applications

2.1. Visual Applications

2.2. Audio

2.3. Text Generation

2.4. Coding

2.5.  Image Generation

Conclusion

Introduction

Artificial intelligence (AI) holds an essential role in reshaping various industries and driving progress, as it can process vast amounts of data and derive valuable insights, enabling IT professionals, researchers, scientists, and more in various industries to make smarter and more data-driven decisions. This reliance on making decisions and executing tedious tasks can be eased with generative artificial intelligence (Generative AI or Gen AI), as it helps generate innovative solutions and strategic foresight by interpreting data on a large scale.

In recent years, with the popularity of generative AI tools like ChatGPT, PyCharm, Midjourney, Speak AI, and many more, businesses have been able to generate new ideas, solutions, and content faster, which helps streamline operations and allows businesses to stay ahead of a competitive and ever-evolving market.

This article delves into how generative AI works, the popular applications, and the use cases across industries.

1. How Does Generative AI Work?

Generative AI models use neural networks to identify patterns and structures with the help of existing data in the form of audio, text, or visuals to generate new and original content for their users. For instance, a popular application like GPT-3 allows users to generate essays based on short text requests.

With this data, generative AI can then step beyond just generating imitative content and also create a realm for multi-tasing and even create foundation models with the help of unsupervised or semi-supervised learning for training. For example, one stable diffusion, which is used as a base for AI systems to perform multiple tasks, allows users to understand the power of language.

2. Best Generative AI Applications 

Generative AI is a powerful tool that helps streamline workflows for users from different industries. With the help of genetic AI models, one can take inputs like text, visual, audio, and code to generate new or modified solutions.

Let’s take a quick look at the best generative AI applications that may help enhance business operations.

2.1. Visual Applications

Generative AI applications simplify video production through highly flexible and efficient features that generate high-quality video content. Using generative AI models, video applications can automate tedious jobs like animations and video compositions, adding special effects, editing video snippets, etc.

Besides video generation, Gen AI also helps in 3D shape generation to build 3D models and shapes with autoregressive models, like GANs (generative adversarial networks) and VAEs (variational autoencoders).

2.2. Audio

Generative AI in audio models uses machine learning (ML), AI, and algorithms to create new sounds from existing data, like background scores, audio recordings, and speech-to-sound effects. After training the model, it can create new audio that is original and unique.

Gen AI audio tools like MusicLM, AudioLM, and OpenAI’s Whisper can develop songs and audio clips with the help of text input, videos with recognizable objects, and the surround sound of different video and audio footage.

2.3. Text Generation

One of the best examples of a text-generative AI tool is ChatGPT, which can create and also summarize textual content with prompts. Such Gen AI models are well-trained on large data sets to generate updated and authentic content. Furthermore, these tools can be fine-tuned to translate any language by analyzing the text.

Generative AI also powers virtual assistants and chatbots, especially during the holiday season, to generate relevant and natural responses in real-time when conversing with the user.

2.4. Coding 

Generative AI applications are also helping in software development and coding through innovative solutions that streamline coding, improve code quality, and enhance productivity. Many IT organizations have started training and implementing generative AI models for coding, as it helps in predicting future codes that developers may need to speed up the coding process and also reduce possible errors.

2.5.  Image Generation

One of the most common applications of generative AI is image generation, which is text-to-image conversion. The user enters a textual prompt to describe the type of image they desire, and the AI tool will process and generate a realistic image. Apart from text-to-image, Gen AI can also be used to modify or alter images in terms of color, texture, lighting, or style while maintaining the originality of the image.

Conclusion 

In conclusion, generative AI transcends the realm of mere artistic exploration, presenting itself as a powerful tool across various industries. Generative AI has not only emerged as a tool but as a collaborator for IT professionals, scientists, researchers, engineers, and many more to create ideas, solutions, and content of different forms through audio, visual, text, language, and coding; this will optimize workflow and spark a creative breakthrough. IT visionaries believe that generative AI offers a bridge between technical expertise and limitless possibilities.

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The inherent inertia and inefficiency of regulators in responding to rapidly evolving sectors like AI can be attributed to several factors rooted in their nature, design, and skill sets. First, regulatory bodies are typically structured to be cautious and deliberative, prioritizing stability and risk aversion over rapid adaptation. This approach, while beneficial for maintaining systemic integrity in traditional markets, often results in a lag when faced with fast-paced technological innovations. Additionally, the design of these institutions, often bureaucratic and bound by complex legislative processes, hampers their ability to swiftly enact new policies or adapt existing ones to novel contexts. Lastly, there is often a skill and knowledge gap; regulators may lack the specialized expertise required to understand and effectively govern cutting-edge technologies, leading to a reliance on outdated frameworks or overly cautious approaches that fail to address the unique challenges and opportunities presented by sectors like cryptocurrency.

This pattern of slow and inadequate responses was most recently highlighted by the rise and fall of FTX, a major cryptocurrency exchange. In 2021, FTX quickly grew into one of the world’s largest cryptocurrency exchanges. In 2022 it collapsed in one of the most prolific financial fraud cases in US history. This failure served as a wake-up call. It demonstrated the risks inherent in the crypto market and the consequences of the US government’s slow response in establishing a comprehensive regulatory framework. 

Enter AI

The rapid advancement of AI is already outpacing regulatory efforts, making it particularly challenging and potentially harmful to consumers if not properly regulated. Driven by breakthroughs in machine learning algorithms, vast amounts of data, and increasing computational power, this pace of AI development far exceeds the traditional timelines of regulatory bodies, which often take years to formulate and implement. 

AI is an extremely dynamic and diverse industry, unlike other more traditional industries like healthcare and finance, and it encompasses almost every aspect of all other industries – from healthcare to finance.Therefore, regulating AI requires a nuanced understanding of all of these domains, and the technical nature of AI systems compounds this challenge further. On the other hand, over-regulation can stifle innovation, leading to reduced competition and slower advancement in beneficial AI applications. In the best case, it will result in a lag of AI technology development compared to other countries that may have a better handle on regulation. In the worst case, it will ultimately harem consumers by limiting access to improved services, increasing costs, and slowing the development of AI solutions that address critical societal issues. Thus, the regulation of AI must be a tightrope balance, ensuring consumer protection and ethical use while not impeding the technological progress that leads to significant benefits.

Regulating AI

After chatGPT, AI regulation has been the hot topic of 2023 at congressional hearings. Several major tech companies have advocated for AI regulation often with modifications that align with their business interests (surprise, surprise).

Perhaps the most vocal proponent of AI regulation has been Microsoft. By positioning itself as a responsible leader in AI, Microsoft is hoping to gain trust from both consumers and business clients. Google is the next obvious culprit. With its parent company Alphabet, Google has shown support for regulatory frameworks around AI, particularly in areas like facial recognition and ethical AI. Google benefits from regulation by reducing the risks of uncontrolled AI deployment, which could lead to public backlash or harmful incidents that might tarnish its reputation or lead to costly legal battles. Meta has faced a lot of scrutiny over its use of AI in content moderation and data privacy. Meta is hoping to guide the formation of policies in a way that aligns with its own practices, potentially mitigating some of the public and regulatory pressure it faces. 

Of course, these companies never state the true intentions behind these regulations – which is to close the door behind them after creating one of the most powerful technologies mankind has ever encountered. By advocating for regulation, these companies can not only start pushing to monopolize this technology for themselves, but they can also get the added benefit of brand reputation and consumer trust by advocating for “safe and ethical AI”. We are already seeing the effects of this through President Biden’s executive order which already implies increased compliance costs, barriers to entry and innovation, and market consolidation – all of which will help them dominate the market and kick out the little guys. 

Consumers are getting screwed

All these regulations will create negative consequences for consumers if not carefully crafted or if they “inadvertently” favor large companies at the expense of smaller ones or innovation in general. Primarily, a reduction in innovation and diversity, slower access to advanced technologies, and decreased competition are a few of many concerns. The best example of this happening is in Canada. The telecom industry consists of only three players: Rogers Communications Inc., Telus Corporation, and Bell Canada. This became possible as they lobbied and bullied their way into the top to introduce regulations to stifle any competition in mobile phone and internet services.

As a result, Canadians have significantly worse coverage plans, both locally and globally, than Americans do. Mobile phone bills have skyrocketed to eye watering prices, and Canadians are often the last to get many interesting and innovative services. This extreme competition stifling has even resulted in fatal consequences. On July 8th, 2022, Rogers Communications experienced a service outage that knocked 25% of Canada offline. This resulted in many crucial services being knocked offline, including 911 services.

If Canada is suffering this much due to the monopolization of telecom services through regulation, it pains me to imagine what catastrophic consequences the regulation of AI will have upon the consumers in a country like the United States. 

What do we do then?

AI must be decentralized. Period. Full stop. Allowing something as game changing and powerful to be centralized and to follow the bottom line of business corporations is akin to allowing the internet to be controlled by corporations. If this had happened, this would have resulted in a much less free and open internet than the one we have today. Consumers of the internet today have freedom of choice in where they shop from, how often they do it, and what they wish to pay, due to the many services allowed on the internet. If it had been regulated like Microsoft had attempted to do in 1995, it would be akin to shopping in a random strip mall in midwestern America. 

The same fate awaits AI if we do not push for decentralization. Other countries may embrace decentralization earlier and end up leading the way in innovation and AI power, whereas the US may still be stuck exchanging emails with a product manager that’s promising their feature is “coming in the next quarter”, then proceeding to thank them for “touching base.” 

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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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