AI Integration

AI integration involves embedding AI models and algorithms into existing systems, processes, and workflows. This can range from automating simple tasks to complex data analysis and predictive modeling.

Proper AI Integration requires the assessment of existing infrastructure with a carefully planned approach. The objective is to ensure data quality and security throughout this entire process. Today, organizations have a wide-rang of appropriate AI solutions to select from. Equally vital is the necessary training and collaborating with the designated employees whom will be assigned to directly manage the organizations AI systems and technology stack. Ideally, organizations should consider implementing a phased approach, starting with pilot programs and gradually scaling up, while mitigating risks and ensuring a smoother transition.

Additionally, change management is crucial for fostering a positive attitude towards AI and ensuring that employees are comfortable with the new ways of working. Challenges to AI adoption include lack of AI proficiency, resistance to change, and integration with legacy systems.

AI adoption is the process of embracing AI solutions and incorporating them into daily operations. It requires a shift in mindset and a willingness to collaborate with AI technologies. Change management is crucial for fostering a positive attitude towards AI and ensuring that employees are comfortable with the new ways of working. Challenges to AI adoption include lack of AI proficiency, resistance to change, and integration with legacy systems.

Change Management: Organizations need to address the human side of AI adoption, focusing on communication, training, and support to help employees adapt to the changes.

Data Quality & Security: AI systems rely on data, so it’s crucial to ensure data quality and security throughout the integration and adoption process.

Ethical Considerations: As AI becomes more integrated into organizations, it’s important to consider the ethical implications and ensure that AI is used responsibly. The reason why we must learn how to market not just to people, but how to market to AI is to enable us to better navigate our way around the complex world of AI from within our organizations: We must closely consider the fundamental aspects of Safety/Security: Ethics; and Governance when adopting the AI platforms we select.

The Problem: There’s quite often a HUGE amount of mis-conception or mis-understanding - as a result of tons of mis-information that’s floating around the Internet - and its being improperly mentioned with regard to these 3 concerns.

When deploying AI in production, there are 3 questions that we need to ask ourselves: The first question is: “1. Is the intent appropriate?“ It’s an ethical question. And of course, there’s a number of people today whom have Rebranded themselves as “AI Ethicists“ but as far as we are concerned, there’s no such field as “AI Ethics“: Ethics is the study of “right and wrong“ and the difference between AI and human beings is that human being have intent. AI’s don’t have intent. And there are already processes, structures, and methodologies, to scrutinize intent.

Then, the next question that should be asked is: “2. Are my algorithms explainable?“ The difference between software and AI - really - is that AI tends to be opaque in terms of “how“ they make their decisions. If you make them explainable then you make them transparent, auditable / governable. It really solves a lot of these words.

The final question we need to ask ourselves is not what happens “if“ our integrated AI platform goes wrong. (As engineers, when we build systems, we identify failure points and we try to mitigate them.) But we have to ask ourselves - for the first time ever - “What happens if my AI goes right?“ You can ’overachieve your goal’ and by overachieving your goal, you can actually cause harm elsewhere within the supply-chain.

An Example of Improper or Unacceptable Overachievemnent: If one were to build an AI system tasked to erradicate HIV, what’s the easiest way to erradicate HIV effectively? Answer: By way of erradicating humans altogether. (Thus, without question, we have to be careful about mitigating the risks of AI’s going very right. There have been numerous projects developed more recently wherein AI has overachieved its particular goal(s) and subsequently caused serious damage elsewhere.)

• Micro Risks: Micro risks involve the deployment of AI systems into production in a safe and responsible way.
• Malicious Risks: Malicious risks must be mitigated to prevent bad actors from creating pathogems, cyber attacks and other forms of economic or environmental harm upon humanity. Proper threat mitigation methodologies aimed at these types of risks are increasingly critical to humanity as AI moves forward. Certainly, this is really the role of government and it’s without question, a primary concern, as governments should be capable of fully understanding precisely how they must go about navigating this space.
• Macro Risks: Macro risks are those which pertain to the direct “impact“ that particular forms of AI technologies are going to have upon our society. Let’s examine this a bit deeper. Perhaps you’ve heard of the word “singularity“ which comes from the field of physics, and it’s a point in time in which one cannot see beyond. This word has been adopted by the AI community to refer to the “technological singularity“ - which is a point in time wherein we build a computerized brain of sorts which is at least 1 million times smarter than our biological version.

Now, let’s digress. There’s actually perhaps 6 different kinds of possible singularities which have been proposed more recently. The PESTLE framework is an inferrance to this ideological suggestion, comprising: Political, Economical, Social, Technical, Legal, and Environmental singularities. PESTLE is a macro framework. So, let’s just go through them now and we can argue that there are a PESTLE of singularities.

• Political: The Political singularity a post-truth world: a world where we no longer know “what“ is true. So, a world wherein AI mis-information bots; and deep-fakes have challenged our political foundations and they will continue to challenge our political foundations but they’re also now starting to challenge the fabric of our reality. (With 3 seconds of your voice and a few images, one can be cloned.) Presently, there exist people whom have been attacked by clones of there children or their work colleagues. If you Google ’WPP CEO clone’ - he was Mark Read’s clone 3 months ago - and somebody tried to setup a board meeting to commit fraud. These types of low-cost, deep-fake AI algorithms pose increasingly significant global cybersecurity risks that will need to be scrutinized through government regulation and ongoing improved cybersecurity mechanisms and standards. It is, however, likely that we can deploy AI solutions to actually solve this problem. (There are some that believe we can mitigate the risk of a post-truth world.)
• Economical: The Economic singularity is a point in time when we fully automate the majority of human labor. For years, we’ve witnessed AI-structured systems that ultimately free people from mundane repetitive, structured tasks. (Most of those individuals have’nt necessarily lost their jobs either. Those people have gone on to do more important; more impactful; more purposful things.) Over the next 10 years, we’re going to see a Cambrian explosion of the same: New innovations; new opportunities will be created. AI is like an energy source that will allow humanity to “grow“ to the next level. Beyond 10 years, there are two extremes to the argument: On one end of the argument, the belief is that if we can free up all jobs with AI and robotic automation, we probably will considering the pressure to reduce costs; increase profits. And if that transition happens relatively quickly, our economies won’t be able to balance; consumer spending will sharply decline; and this will lead to social unrest. Some suggest that 4-day work weeks, among other ideas could be effective mechanisms to mitigate some of these risks. The other end of the argument, which is completely the opposite and rather quite contraversial, is that we should, in fact, be accelerating toward Economic singularity achievement as quickly as possible. (In other words, if we can remove the friction from the equation - which, by the way, usually means the human labor component - from the production and desimination of goods, like food, healthcare, education, energy, transport, we can bring the cost of those goods down to zero. So, imagine being born into a world where you might not access “paid work“ but everything that you need to survive and thrive is free. Of course, many people would say: “What would I do if I did’nt have a job?“ or insist that “A job defines who I am as a human being?“ Right? But most people who don’t have to work today, are’nt sitting at home all day long, bored and depressed.)
• Social: The Social singularity is the point in time when we cure death. (There are scientists today that believe there are people alive today that don’t have to die.) Think of AI as a medicine that’s able to monitor our bodies and clean us out regularily. Such AI approach (as its intended to be developed) would ideally be applied as a medical treatment in a sophisticated medicine form, such as ’nanobot’ particles that would be designed to continuously move around inside our bodies; detecting; repairing; and removing damaged cellular waste (junk DNA), and other toxic or non-essential substances, including cancerous cells. (Similar to a car, the concept is that if you stay on top of general maintenance, repairs, and damage that occurs through use, that car will never, ever break-down and it will always look (and function) like a brand new automobile.)
• Technical: The Technical singularity is a point in time where we become the second most intelligent species on our planet. Importantly, there are many within the computer science field that had previously believed that this achievement would not be realized for at least another 30 or 40 years - at best. However, today, there are multitudes of individuals within the top computer science community that insist that “we are presently in the era of achievement“ of this form of singularity, suggesting that this will happen rather soon - within the next 10 or 20 years from now.
• Legal: The Legal singularity is the point in which surviellance becomes ubiquious with full AI-driven “perception comprehension“ along with advanced AI-driven “perception influencing“ techniques. And AI can do this incredibly well and that is a very powerful capability to develop and unleash. (Think audio & video data on steroids: Such surveillance state could ideally entail GPS geo-locational positioning with real-time tracking; integrated biological monitoring; bio marker detection and authentication functionality; biometrics capturing with accurate authentication and verification, e.g., facial recognition and quantifyable behaviors, including: pulse/heart rate; electrical signal analysis and confirmation via heuristic-touch screen sensor technology; organic data collection, including speech/voice detection, and perhaps, even adaptive “thought monitoring“ by way of predictive analytics AI-driven tools that operate via 6G bands from everywhere within our society.) As such systems are deployed, we will need to use them safely and responsibly. We will also need to mitigate bad actors from using these technologies in ways to accumulate more wealth and more power. We should be able to solve these problems as well through specific optimization processes and unique security tool implementation practices which will need to be widely adopted on a geo-political scale.
• Environmental: The Environmental singularity is a point in time wherein we lose control of our eco-system. Now, we know that AI is substantially increasing consumption. Of course, AI-generated consumption gives us access to goods and services that typically reached their lives. We know its putting pressure on our planetary bounderies but if we apply AI in the right way - across our supply-chains - we could at least half the energy we need to depend upon it. This is because when we look closely at the ’Last Mile Delivery Problem’ at Tesco or PwC’s ’Workforce Allocation’ problem, the typical reduction in the amount of carbon is at around 25%. (In other words, if companies can optimize across their supply-chains - create digital twins of entire companies - they could reduce the amount of carbon even more. We can reduce the amount of carbon by at least half, and unlock more capacity if we apply AI in the right way.)

Empowers employees: Training helps employees feel comfortable and confident in working alongside AI tools, fostering a positive environment for AI adoption.