Generative AI marks a massive leap forward in human innovation. In a matter of decades, we’ve come from a point of imagining this technology to a reality in which it can be used in everyday life.
ChatGPT is the first thing that comes to mind for many in this respect; released in November 2022, the conversational chatbot quickly went viral for its ability to churn out college-level essays, cooking recipes, and original poems with nothing more than a short text prompt. It’s just one of many generative AI tools that pioneer tech firm Open AI has developed over the past few years, starting with the initial release of Generative Pretrained Transformer version 1, or GPT-1, in 2018 to its most recent launch of GPT-4 this past March. Today’s newest model boasts a number of added features, such as the ability to accept image and text inputs, emit larger outputs, and maintain longer conversations with users.
The past few years in particular have been the most formative for generative AI as investors and industry giants recognize the potential gains ahead. They’ve begun competing against each other in a race to see who can capitalize upon opportunities first and dominate an emerging, but extremely promising and lucrative market. Big names Google, Microsoft, IBM, and Amazon all have their hats – or dollars – in the ring and major projects on the horizon.
Google, for instance, just released the beta version of its proprietary conversational bot Bard, which it hopes to one day integrate into users’ search experience. While Microsoft has its own initiatives in-house, it recently invested in industry leader Open AI to the tune of $10 Billion USD. Both IBM and Amazon have respective projects in the works as well; the former is working on a supercomputer, named Vela, designed to help its scientists create and optimize new AI models. Amazon expanded its partnership with startup Hugging Face in February to bolster AWS and potentially provide customers with exclusive AI-powered tools.
All of this action has experts predicting a major revolution to come in the tech industry – and the world at large. According to data from PwC, AI contributed a whopping $2 trillion to global GDP in 2019. Just a few years later and IBM now forecasts it to add more than $16 trillion by 2030.
And unexpectedly rapid adoption is only adding fuel to this growth. IBM’s 2022 Global AI Adoption Index reports that over 35% of companies are now using AI to help run their business. Some countries, such as Brazil, exceed the average and have adoption rates upwards of 41%.
Looking forward, an additional 42% of organizations around the world say they plan on exploring Artificial Intelligence in the coming years. We could very much be headed for a future reality in which computers that think for themselves are commonplace, forcing anyone who wants to stay competitive to adopt AI or risk obsolescence.
Tapping Into Tech’s AI Revolution in Three Steps
Akin to other technological marvels of the past, business leaders must choose how they want to respond to revolutionary developments in AI technology: with reluctance or with enthusiasm. The latter has historically proven to be the better option, as those who embrace new ideas and technologies first are typically the ones that reap the biggest benefits. Just ask Google, Microsoft, IBM, and Amazon – they all got their start by capitalizing upon emerging trends, and by the looks of it, are poised to do so again.
But just how can business leaders leverage the transformative power of AI? Here are three steps to get you started:
1. Identifying Potential
Part of the reason for AI’s significance to date is its accessibility. Unlike many other cutting-edge technologies, generative tools have been developed in front of the public eye and with an interest in open use. This has enabled them to become mainstream much earlier, providing opportunities for businesses of all sizes to assess their own potential applications.
In this sense, the first step every organization needs to take in entering AI is to consider how they can best capitalize upon its capabilities. The answer will look different from business to business depending on industry and size but could range from customer service improvements to increased organizational efficiency or enhanced product designs.
The key to unlocking full value lies in finding ‘golden’ use cases – those different from what competitors are exploring and that could yield the biggest operational advantage. This is critical because virtually everyone is jumping on the AI bandwagon. Seeing real returns on AI investments means not only implementing the technology but doing so in a way that no other company has thought of.
Once use cases have been established, it’s a matter of narrowing down options to decide which course of adoption will be the most effective. For organizations without a particularly unique use case, buying or using an existing open-source model is likely enough. There are plenty of options available on the market that can be implemented at a relatively low cost and in a short matter of time. The only tradeoff for this is reduced flexibility and control; those that don’t need either have nothing to lose.
Companies with advanced functionalities in mind, on the other hand, are best suited to training their proprietary model, either in-house or through a partnership with other organizations. This requires a lot more investment upfront but may be worth it for organizations that want to use generative AI in very specific ways.
2. Preparing Staff and Resources
The unbelievable efficiencies of AI are a two-sided coin. Implementing them will redefine standards in more ways than one, potentially changing the face of workplaces forever. As adoption ramps up, it will only be inevitable that some jobs are replaced by AI-powered tools. This is probably the biggest public fear surrounding the technology to date; a narrative that technology will simply eradicate the need for human labor.
The reality is more nuanced than this, however. Certain jobs will indeed be automated out of existence, but many others are likely to be augmented or transformed by AI. For leaders embracing this technology, the primary concern should be on upskilling current staff and putting systems in place to ensure workers are as prepared as possible for this shift.
Effective next steps include:
The first and most important challenge of ensuring an effective AI adoption is getting employees on board. As already mentioned, many don’t want to do so. They see the technology as a threat to their livelihoods and dismiss the idea that they could ever become “AI experts”.
In order to alter this perception, business leaders need to make it clear that the technology is not replacing them, but rather augmenting their existing roles. This requires effective communication around how exactly AI will be used; what new capabilities it will bring; and how it is expected to improve existing processes.
Regular checkups to track employee sentiment will also be important to ensure they remain comfortable with the changes AI is bringing to the organization. Any noted resistance should be addressed directly and employees should always be given the opportunity to ask questions or express ideas on how AI can be used more effectively.
Adjusting Operating Models
The implementation of AI will also lead to changes in how work is organized, performed, and evaluated. Processes that were once tested for accuracy through manual review are likely now to be supervised through AI algorithms. In the same vein, hiring practices may become more automated and data-driven over time.
Organizations should look to establish cross-functional teams of IT, HR, and line-of-business professionals to address the new requirements associated with AI adoption. An emphasis on building scalability and creating feedback loops should also be a priority goal. This will ensure AI is not deployed in one-off projects, but rather, integrated into the way the organization functions from the ground up.
Developing an effective AI environment requires a new skillset; one not necessarily held by existing staff. Organizations should consider investing in external training programs to provide employees with the technical know-how they need to take full advantage of the technology.
The extent of this training will vary depending on the complexity of the AI solution but could range from developing code to understanding basic machine learning concepts. The key is that employees should be familiar enough with the technology to use it effectively and see the value of its implementation in their day-to-day roles.
3. Implementing Policies
The tricky thing about great power is that it often comes with downsides. In the case of Artificial Intelligence, this manifests in the form of risk.
While highly capable, today’s models are nowhere near the point where they could be trusted to carry out tasks completely unsupervised. They seem smart – some people even argue that AI has already reached a point of sentience – but in reality, are just really good at acting like it.
Generative models are trained on mass amounts of data to learn the context surrounding given inputs and respond with relevant outputs. The information they’re fed essentially defines what they know, while the way they’re trained determines how they interpret that information. Mistakes are very common – as are bias, inconsistency, and inaccuracy.
Not only that, but AI tools aren’t inherently secure. Third-party applications and cloud services often require access to confidential data, which increases the risk of data breaches. The code that these models generate is also vulnerable; one study conducted by New York University’s Tandon School of Engineering found that GitHub Copilot produces insecure outputs about 40% of the time. This opens the door to bugs, design flaws, malicious attacks, and data theft.
As far as the potential for error goes, organizations need to recognize that there’s currently no generative AI model they can simply leave on autopilot. The day may come when one does exist, but we’re not getting there without learning experiences from what’s available as of now. Effectively implementing the technology in 2023 means doing so with human oversight. Policies should be set in place to ensure this is the case, and that any errors or malfunctions are quickly identified and addressed. It’s also important to build a culture of ethical AI in order to ensure human values remain the primary driver of decision-making.
Preventing the privacy risks posed by AI starts with policy. As the rulebook organizations follow, it’s the most direct way of ensuring AI is used responsibly and securely. Policies should cover topics such as data usage, storage, and accessibility; how the technology will be monitored and maintained; and how it will be used to make decisions. Companies should also consider implementing a system of checks and balances in the form of technical reviews, ethical review boards, and an AI audit trail. This will help to identify issues before they become problems – allowing organizations to address them promptly and effectively.
A revolution in tech is coming. And while the possibilities offered by generative AI were once unimaginable, they’re just one iteration of a very long line of discoveries that have reshaped the way our world works. As with previous cycles of evolution, survival in this new era depends on an ability to adapt. Businesses must step up and embrace the potential AI holds for them or risk losing out in an ever-evolving market. TeraDact is an AI/ML Information Security company and our products allow for on-prem and cloud-based proactive protection of your sensitive data. Organizations with a clear, well-defined plan for leveraging AI technology such as ours stand to gain a competitive edge, while those that wait too long could be left behind. Reach out today before it’s too late.