Ali Ghaemi

Embrace, Master, Direct AI

Aug 312025

Both here and elsewhere, there has been an ongoing discussion about what AI (Artificial Intelligence) means for human productivity. A more ominous, and related, discussion concerns the implications of the technology for employment, or, more accurately, the risk of unemployment. A comprehensive Microsoft research paper dated July 22nd, 2025 identifies the occupations most at risk from AI. This paper is a good read, but scroll down to page 12 immediately if you’d like the list.

Grouped by occupation these include:

Sales,
Computer And Mathematical roles and
Office And Administrative Support

By contrast, manual labourers and skilled tradespeople, as well as doctors and nurses, are considered among the safest occupations.

Obviously, considering the latest scientific thinking, being analytical about the information and taking in the research in a clear-eyed manner is important. We all have our inherent personal and occupational biases – including those who work for companies like Microsoft or Nvidia (see below) – which taint our judgments. Yet, it is necessary for us to think less about esteem and societal expectations and more about reality when choosing educational paths and career directions going forward. Advertising Creative Director may sound more prestigious than Dredge Operator, but is it truly more secure in the age of AI? And does the imperative to work make us less choosy about which jobs we occupy? Because perhaps the job didn’t go away, but it surely changed. You are not working alone. You, and your team, are working with AI now.

While we reflect on this, two additional points should be made. First, no matter whether one believes AI’s impact on society will be large, moderate, or small, those estimates are likely exaggerated. The reason is simple: resources are limited. Consider $1,000 set aside for investment: if allocated to Nvidia, that amount isn’t additional growth, but diverted from another potential investment. If that full amount (or even part of it) goes into, say, Nvidia, that doesn’t mean society has gained an extra $1,000 of investment overall. Instead, it means that $1,000 was directed toward Nvidia rather than something else. In other words, the benefit to one area comes at the expense of another, so the net effect on society’s total pool of investment is smaller than the raw figure suggests.

Secondly, to whatever extent AI will impact our lives and jobs, it makes the most sense to scale yourself and become the AI expert for that role. So, Customer Support managers could become the AI Support Specialists, Automation Programmers, Customer Support Analysts, call it what you will, for Customer Support AI. Salespeople or Sales Managers need to embrace AI and fight any redundancy by becoming adept Sales AI workflow managers, AI Sales Architect, Sales AI programmers or Sales AI Operation Managers. Get into your core business processes and become the go-to resource for such knowledge. Here is Nvidia CEO Jensen Huang: “Every job will be affected, and immediately. It is unquestionable. You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.” Again, Huang’s perspective comes from a vested interest, but directionally, the logic makes sense. In other words, if you can’t beat them, join them.

Embrace AI, become part of the change and help rethink the way your company is organized and thinks about the technology. Functions like chatbots, virtual support agents and content creation are productivity gainers, but surface-level. The real prize is in rethinking your business process for qualitative and quantitative reasoning that is transformational and who understands those weaknesses and inefficiencies better than the user community, that is, you.

Things That Need To Go Away: Not Thinking About Outcomes And Being Reactive And Not Proactive About AI Solutions.

Body Language In Sales

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Jul 222025

The right solution is key to winning over customers. Words matter. Tonality is important. The right body language is also imperative. Most people agree and understand that the right body language and posture is important when on-site with a customer. The same concept applies to remote selling. Non-verbal cues also apply to video. Body language, consciously and subconsciously, conveys credibility, confidence and intent.

Here are the body language principles to practice:

Do As They Do: Mimicking or mirroring their posture and body language establishes likeness and similarity. This is a form of rapport and trust building. Isopraxism, as the concept of syncing body language is called, helps establish trust.

Stay Within Eyesight: Remain within your conversation partner’s field of vision. You are more influential this way and, moreover, you have a better grasp of whether your points are landing or not. It helps bring them back into the conversation if you nonchalantly wave your hand and assists with maintaining eye contact. Speaking of which, make sure you look into their right eye, which is more sympathetic than the aggression indicating stare into their left eye.

Do Not Sit Head-on: While you want to be in the field of vision, you do not want to be antagonistic. You are not two bulls butting heads. Angle your body slightly instead of facing them directly.

Nodding: A head nod tells the prospect that you are listening and understanding. This gives them a feeling of safety and validation.

Don’t Forget To Ask: Involve them in the conversation and keep them engaged. Not only do you want to match your content to the person’s interests, but you want to make sure you know if your audience’s interest is waning or if they are not engaged. Of course, customers want you to know their challenges, pains and goals so this goes without saying.

Two exceptions come to mind, however. One study has found that snobbish salespeople increase luxury goods sales. A study from The University Of British Columbia’s Sauder School Of Business indicates snobby salespeople increase the desire to purchase in their buyers. The other is the famous HBR study that suggests companies should remove obstacles and make life easy for their customers during interactions as opposed to trying to delight them.

Those aside, the right body language and posture are conducive to customer trust and prospect satisfaction, which, in turn, can only mean good things for your sales outcomes.

Things That Need To Go Away: Making A Customer Feel Ignored

AI Sales Agents in 2025: Solutions For Enterprise Revenue Leadership

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May 012025

Artificial Intelligence has evolved from a buzzword to a revenue-critical lever for enterprises.. Sales and salespersons are no exception and so a number of sales specific AI solutions have appeared on the market.

A 2024 McKinsey & Company report states that companies leveraging AI for sales can experience a “nearly 50%” increase in leads and appointments.

Another statistic claims that inside sales professionals spend 35% of their time selling. The rest? Research and administrative work.

Most importantly, would it not be phenomenal if guesswork, assumptions and shooting from the hip was replaced by salespersons and sales manager with data-driven actions and next steps? Instincts are often correct because they are based on experience, but the power of AI to provide analytics and insights will serve sales, forecasts and replace reactive with proactive.

Here is a non-exhaustive list for anyone seeking a solution or looking to research the right tool. AI now handles lead qualification, conversation analytics, note taking and pipeline automation freeing teams to focus on higher-value negotiations and relationship building. This is a selection of specialized AI sales tools (excluding embedded solutions like Salesforce Einstein, Zendesk’s AI or Microsoft Copilot) that you should evaluate for competitive advantage.

This is not an exhaustive list and, moreover, given the nature of the market will assuredly be dated as soon as the article is published. The author has not personally used or trialled every solution listed. Having said that, given my personal experience, one piece of advice would be to double check every feature/functionality claim and specifically ask if the desired functionality is included in the price or is considered an add-on. In my experience, solutions are new enough for sales and marketing teams to not always be well-versed with their offering and pricing models are still fluid. The same advice applies to any critical integrations – for example to a CRM or messaging software that you use – that may be important to you.

Sales AI solutions:

Botpress
Chorus (ZoomInfo)
Clay
Copy.ai
Fathom
Fireflies.ai
Nooks
Otter.ai
Pipedrive AI
Regie.ai
Zipteams

Things That Need To Go Away: Assuming Human Control And Personalization Can Be Replaced By AI

What’s Next For Artificial Intelligence?

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May 012025

Something former New Zealand Prime Minister Jacinda Ardern told an audience at the recent Zendesk Relate 2025 conference caught my attention. “My experience with people who ever doubt themselves is that they will prepare, they will read everything, they will reach out to the experts, they’ll do their job with humility because they won’t believe they have all the answers,” she said. Ardern’s approach is commendable, particularly when paired with decisive action. She emphasized that governments need to be transparent with the risks, regarding Artificial Intelligence, and take care of citizens proactively. Numerous societal problems have already been associated with AI. In any case, AI still requires human input and guidance.

This made me think. There already are a myriad of use cases for AI that most readers are aware of (see examples on this website), but what is the future of AI? We often say that we are just scratching the surface of AI’s potential – a thought I agree with – but what about the future? What roles will AI play in the coming years? What are some possibilities in the near future? Here is my list that I hope acts as a catalyst for some thinking and to act as a time capsule.

Productivity enhancements
Prevalence (similar to how the Internet is available everywhere) in many different things and to assist humans enabled by 5G
Scientific modelling and breakthroughs
AI expert and custodian roles
Companionship – for romance, therapy or friendship
Powering robots and making them more capable
Agentic AI for healthcare, financial services, law offices, government services and translation services
Analytics that use NLP to understand and be understood. Analysis and Business Intelligence tools accessible to the masses
Contractual agreements aside, AI-generated actors replacing human performers
More lies, fabrication and fake news and imagery – virtually indistinguishable from the real things
Finally, personally, I hope to see robust AI regulations and oversight enacted.

Things That Need To Go Away: era of reactive and ad-hoc approaches to AI

Why Responsible People Can’t Ignore AI’s Potential And Pitfalls

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Apr 112025

Quite a few smart people have warned us about the dangers of AI. They demand guardrails and, I believe, most responsible people agree, yet, AI is also powering advancements in climate science, medicine creation, cancer diagnosis and treatment, reducing child mortality and more.

The same technology can bring untold benefits to both persons and companies and organisations of all sizes.

Alphabet and Google’s Chief Executive Officer Sundar Pichai remarks, “I think we are at 1% of what humanity’s information needs are today. It’ll be obvious a decade or 20 years from now.”

Artificial Intelligence (AI) is no longer a distant concept. It’s a transformative enabler that can reshape your industry and redefine how your businesses operate. In my conversations with customers, corporate decision-makers and technology leaders, one thing is clear: everyone recognizes the potential of AI. However, the journey to adoption is far from uniform and, in fact, quite uneven. While some organizations are forging ahead with ambitious AI initiatives (often in stealth), others find themselves paralyzed, unsure where to begin or struggling to secure the necessary buy-in to move forward.

This paralysis often stems from two key challenges: 1. Uncertainty about where AI responsibility should reside within the organization and 2. A lack of clarity on how to take the first step. These roadblocks create friction and hesitation, leading many organizations to delay action. Yet, in today’s fast-paced business environment, standing still is not an option. Inaction is irresponsible and can have consequences ranging from inefficiencies and missed opportunities to falling behind competitors, which is one step closer to irrelevance.

One practical suggestion: Start small. If launching a full-scale AI initiative feels overwhelming or scary, consider beginning with a focused experiment. Identify a specific business problem or process that could benefit from AI-driven insights or automation. Build a small pilot project around it, test its effectiveness and measure its impact. A well-executed pilot can deliver a small but meaningful win and one that not only demonstrates the value of AI, but also provides tangible results, such as improved efficiency, increased employee satisfaction or cost savings. Use these findings to build momentum within your organization. Share the success story broadly, but appropriately after calculating the return on investment (ROI). You have a foundation to scale your efforts now and have the learnings to boot.

By taking this iterative approach, one can overcome organizational inertia while fostering confidence in AI’s potential. Small wins pave the way for larger transformations, empowering your organization to embrace AI as a strategic enabler rather than viewing it as an abstract challenge. The time to act is now! It is a race for innovation. Hesitation is certainly more costly than experimentation. And we also admit that one really doesn’t know what the end-product will be until after the investment!

Consider the below as food for thought and factors that need attention.

What AI Does

Automated Deployment	Automated Testing	Computer Vision	Customer Insights And Analytics
Cybersecurity	Development	Documentation And Document Generation	Modelling And Optimization
Natural Language Processing (NLP)	Predictive Analytics	Recommendations	Testing
Training	Troubleshooting	Virtual Assistants And Support	Workflow Creation And Automation

AI Benefits

24/7 Availability	Automation And Efficiency	Better And Faster Responses	Better Customer Responses
Continuous Learning And Adaptation	Cost Reduction	Data Driven Analytics And Interactions	Enhanced Decision-Making
Frictionless Customer And Professional Interactions	Personalization	Proactive Actions	Query Responses
Risk Mitigation	Scalability

AI Concerns

Accuracy	Bias Based On Ingested Data	Compliance	Disruptive To Organizational Charts
Environmental Impact	Loss Of Human Connection	Misinformation And Manipulation	Privacy & Legal
Security Risks	Transparency And Institutional Knowledge On Process Followed	Trust And Possibility Of Hallucinations

Risks Of Not Adopting AI

Data Overload	Difficulty Scaling	Increased Costs And Reduced Efficiency	Lower Customer Service Standards
Market Share Loss And Reputational Damage	Missed Innovation Opportunities	Missing Modern Revenue Streams	Undetected Security Flaws
Undetected Security Vulnerabilities	Weaker Customer And Professional Relationships

Things That Need To Go Away: Being Blinded Whether By Shiny New Objects Or The Absence Of Complete Lucidity

DeepSeek AI

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Jan 292025

AI/Artificial intelligence has been a hot topic ever since OpenAI released its ChatGPT over two years ago. A Chinese competitor, called DeepSeek, has made waves with its own offering this week. The app went viral becoming the #1 downloaded app on Google Play Store and Apple’s App Store.

Observers marveled at a product that reportedly matched or surpassed its Western competitors in several benchmarks in a short time – despite using less advanced Nvidia chips. The Biden administration banned the export of Nvidia’s top-of-the-line chips to China to impede that country’s AI ambitions.

While some publications cautioned users against using DeepSeek’s app due to data privacy concerns, it’s important to note that privacy issues are not unique to Chinese companies. The U.S. government has access to data from American tech companies through various laws and programs.

Let’s address several misconceptions:

While DeepSeek did admit to using secondary and less powerful chips, it did not deny having previously acquired and deployed Nvidia’s top chip, the H100. In fact, many reports pointed to DeepSeek having amassed an inventory of the same prior to the ban going into effect.
The experts advocating for non-usage were also seemingly off the mark. We already know through the NSA revelations and public knowledge laws, like the Patriot Act, that privately generated data entrusted to US tech companies is shared with the US Government and, from there, other allied intelligence agencies. Many point out that those are not equivalencies. Fair enough. Nonetheless, it does bear pointing out as all the stories I read omitted the fact.
Moreover, does anyone remember Cuil? The search engine by two former Google search employees launched sometime in 2008 and immediately received wide press coverage including for its main thrust that it had the most indexed pages of any search engine, Google included. Cuil was everywhere seemingly for a few weeks, yet shut down and went out of business in 2010. That is not to say that DeepSeek will meet the same fate as Cuil. It just serves as a reminder that the race is a marathon and not a sprint and proof should be in a pudding older than a week.

In the world of AI data is everything. Consistently acquiring and synthesizing it is the determinant of an AI’s success – as far as technology goes, that is – as the myriad of models out there all seemingly work fine. Yet, given the ongoing data ingestion requirements a winner is never assured. In fact, as of today another Chinese company Alibaba’s Qwen-2.5, beats Anthropic’s Claude 3.5 Sonnet, Google Gemini-2 Flash and OpenAI’s GPT-4o in most benchmarks. It also surpassed DeepSeek’s results. It is important to note that DeepSeek R1 used other available datasets to achieve its operation rather than going out there and ingesting the universe of all data. This also implies that DeepSeek is subject to the same hallucinations as its progenitors.

Finally, and most importantly, for the viability of AI and the universe of GenAI users, it is imperative to point out the bottom-line: DeepSeek is good news for all AI including the more established competitors. How? Firstly, it brings AI further into the public consciousness. The articles and publicity bring GenAI to a wider audience and one that is global. Secondly, let’s remember that DeepSeek was put to work serving its founder’s (Liang Wenfeng) main occupation, namely the High-Flyer hedge fund. By all accounts, the man and his hedge fund have been wildly successful thanks to the power of artificial intelligence, which is now in your, and my, hands. Can anyone think of a better validation and advocacy for AI technology and its myriad of possibilities and use cases?

Things That Need To Go Away: Judging AI By News Cycles, Rather Than Focusing On Its Engagement And Market Share Capture Across Various Use Cases And Verticals

AI Transformation: Implementation Across Industries

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Jan 192025

Generative Artificial Intelligence is no longer a futuristic concept. It is a business imperative. As technologies evolve at (even more) unprecedented speeds, organizations across industries need practical strategies to harness AI’s potential. The below provides business leaders and technology managers with a blueprint for understanding, evaluating and implementing AI technologies that drive value.

A post by Sam Altman, CEO of OpenAI, reported last week that the company is losing money on its top tier ChatGPT Pro, which was launched at $200 per month in December 2024. The tier offers users unlimited access to OpenAI’s latest model, OpenAI o1, as well as access to its Sora AI video generator and Advanced Voice Mode. In other words, in case it was not obvious, the more subscribers, the bigger the loss. There is also the possibility that Altman managed to slide in an ad for OpenAI’s top tier of course.

There is also a free tier and the limited access Plus model for $20 per month.

It was refreshing that the CEO accepted responsibility for the pricing and the loss, which he did. OpenAI had projected losses of about $5 billion (US) and revenue of $3.7 billion for 2024 as of last summer. Amazingly, in one recent report from Jfrog ML 87% of AI users said they were leveraging OpenAI’s model. It highlights how market share could come with financial challenges. Some may recall that Amazon lost money for years before becoming what it is today.

Assuming that $200 per month is a ceiling for what most people will pay for access, and knowing that the company is still losing money, it is reasonable to deduce that the company needs to make up for the shortfall and encourage uptake and usage elsewhere.

With that in mind, here is some help for Altman and co. to present the need for their solution and gain more customers and revenue. The use cases could apply to other providers like Cohere, DeepSeek-R1, Anthropic, AWS Bedrock or Google Vertex as well. Below is a summary of the utility and use cases for AI both in general and by vertical.

Recommendations for AI implementation

Start Small, Scale Smartly: Begin with a pilot project to demonstrate value before full-scale deployment.
Data Strategy: Emphasize the importance of a robust data management strategy, including data quality and governance.
Continuous Learning: Implement a feedback loop for model improvement and adaptation to changing needs.
Ethical Considerations: Incorporate ethical AI principles into the deployment process.

Steps and prerequisites to deploy AI:

Assess needs and corresponding applications.
Budget for and procure the technologies and resources needed for data ingestion, integration, testing and on-going optimization.
Ingest in-house, public or both types of data including structured, unstructured or both types of data.
Designate team members responsible for the model, policies and operation including the HITL (Human-In-The-Loop) model requirement.
Map and connect data with designated business outputs and desired outcomes.
Train employees and stakeholders.
Continuously monitor and review risks, security and evolving regulatory compliance (see above designated team requirement).

Mainstream and commercially available AI that could be deployed today could take different forms. The actual ones utilized are dependent on the use case. These include:

Analysis and didactic AI
Conversational and Personal AI
Developer AI
Image/Visual AI (or multi-modal AI potentially)
Predictive AI
Process AI
Search AI and
Task AI

Zendesk reports that “in a few years,” 80% of customer interactions will be powered by AI – a significant leap from the 20% we see today. There really is no time to waste then. In the same context, the fact that the tech has gone mainstream so recently means new use cases will emerge all the time.

Why AI?

Increased efficiency and productivity
Innovate more and faster
Improve customer and employee experience
Impact revenue and margins positively
Reduce costs
Maintain and improve competitive position
Manage risk, governance and compliance

General use cases:

The standard use cases for Generative AI are horizontal ones. These include:

Automating things,
Speeding up tasks like knowledge augmentation and management,
Software development,
Customer service and
Recommendations and personalization

More specifically, and beyond the above bullet points below is a summary of likely use cases by industry.

Industry	Broad Generative AI Utilities
Healthcare	– Medical image analysis and enhancement – Clinical data synthesis and analysis – Personalized treatment planning – Drug discovery acceleration – AI-assisted diagnostics and treatment planning
Financial Services	– Risk assessment and fraud detection – Personalized financial advice generation – Automated report and document creation – Market trend analysis and prediction – AI-driven compliance and regulatory reporting
Manufacturing	– Product design optimization – Predictive maintenance – Supply chain optimization – Quality control enhancement – AI-optimized energy management in production
Retail & Ecommerce	– Personalized product recommendations – Customer behavior prediction – Inventory management optimization – Dynamic pricing strategies – AI-powered visual search and product recognition
Technology & Software	– Code generation and optimization – Automated testing and debugging – User interface design – Natural language processing for user interactions
Media & Entertainment	– Content creation and editing – Personalized content recommendations – Audience engagement prediction – Virtual character and environment generation
Education	– Personalized learning content creation – Automated assessment and grading – Educational content summarization – Adaptive learning path generation
Real Estate	– Property valuation modeling – Virtual property tour generation – Market trend analysis – Client matching and recommendation
Energy & Utilities	– Energy demand forecasting – Grid optimization – Fault prediction and diagnosis – Renewable energy output prediction
Agriculture	– Crop yield prediction – Pest and disease detection – Precision farming optimization – Weather impact analysis

As the AI landscape evolves, companies must remain agile, focusing on value creation while managing costs effectively.

Things That Need To Go Away: Worrying About The Dangers Of AI As Reason For Inaction

HOW NOT TO SELL BY MIKE WICKS

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Jan 012025

Don’t Do This!

In my experience, salespeople are often cynical about sales advice, classes, tutorials and books by persons who have either never primarily worked in sales or haven’t been professional salespeople. That appears to be the case with the author of this book, Mike Wicks. His biography and parts of the book’s narration suggest he has some sales experience and has held a variety of roles that are related to the field, but he has never worked full-time in a sales job or held the official title of salesperson. On page 39, There is a story about being in sales and getting kicked out by the owner, resulting in the narrator, and his sales samples, landing in the gutter. Not sure whether the story is true, but if it is, it sounds both nasty and funny at the same time (incidentally, the narrative is about matching the personality of the buyer). Regardless, the aforementioned prejudice may, or may not, be valid. After all, prospects, users and folks who understand human behaviour surely have a lot to teach salespersons.

Nonetheless, the book did attract me from the get-go. It may be the indirect approach the title portends, which is original if nothing else, or perhaps that the book is a quick read. It could also be because my own posts always end with an antithesis. The book’s focus may come across as focusing on what to avoid doing, but this is always contrasted with what the right behaviour looks like. Each (short) chapter contains at least one story, which makes reading the book more enjoyable.

The book is divided into three sections: It’s All About You, It’s All About What You Know and It’s All About The Sale.

One reason the book is an easy read is that the 200 pages are not densely packed and the chapters are short. Another is that each chapter repeats itself, not only with the aforementioned antithesis, but also with an instant summary of what was just expanded on. In general, the advice is elementary and the book is better suited to less experienced sales professionals. Having said that, the chapters do become progressively more intricate and developed. To be clear, the advice is always relevant and accurate, but it would be hard to imagine a seasoned salesperson being unaware of it. Still, it is a good compendium and comprehensive reminder.

How Not To Sell reminds the reader that there are four types of personality. These are the Analytical, Drive, Analytical and the Amiable.

Analyticals make good accountants or scientists. They may be reserved and proceed methodically. Make sure your facts are correct when speaking with them and show you want to support their personal credibility. Sell to them by being thorough and precise. Do not rush them and bring evidence. Analyticals need technical specifications and details. Use pros and cons and reverse engineer the process.

Drivers make good CEOs or surgeons. Their concern is the bottom-line. They do not appreciate people who hesitate. They are all about their output and are competitive. Show them how you can help them achieve their desired results. Sell to them by focusing on the task and emphasizing the results. Be concise and answer “what” questions. Drivers prefer not to be bogged down by excessive details. Drivers need to know your product or service makes them money, saves time and does both quickly. Demonstrate how they can make money or save resources and deploy assumptive closes.

Expressives make good salespeople or facilitators. They are stylish and like to be the centre of attention. They are often popular and chameleon-like. Expressives tend to bore easily. Help build their standings with others and you’ll get on. Sell to them by focusing on the relationship and promote a conversation. They are not detail-oriented either and like approachable people. Expressives need the feel-good factor. Be their friend and assume you have the order.

Amiables make better teachers or nurses. They take things slowly and care for relationships. Sell to them by being relaxed and approachable. Amiables need logical and systematic approaches, but also predictable. Agree often and listen. Amiables need guarantees and knowing that they are protected. Build the relationship and recommendation close with ongoing support and a solid warranty in place.

The book has several good presentation tips that are worth noting. Here’s a hint: Keep it short, be clear how you can help them after demonstrating you understand the challenges they face, revisit the challenges at both the start and end and, for heaven’s sake, do not read your bullet points verbatim.

Lastly, do not forget to sell yourself, sell your company and sell your product!

It’s Coming, It’s Coming, It’s Here (A Tale Of Two AIs)

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Dec 072024

Absolutes are typically mythical, being analogous to sasquatch and E.T. Trends and developments are rarely linear.

There are usually pros and cons in everything. Benefits and hazards are built into most things. Even an accelerating race car hesitates momentarily before it gathers more speed. That is the nature of things.

Artificial Intelligence has done a lot of good and will continue to do a lot of good. It is not for nothing, however, that many of the smartest voices of the scientific community warn us about the dangers of AI. Many of these forebodings are unprecedented and cataclysmic. Somewhere in-between, and more imminent, is the tectonic shift to our day-to-day lives that is happening right in front of our eyes. Online education platform Chegg has lost half a million paid subscribers, its market cap has crashed from $15 billion (US) to $300 million and it has laid off 25% of its employees. What will this, for example, do to authors?

True Artificial Intelligence, not the imposter AI that everyone and anybody is touting these days, following the training phase learns independently, benefits from advanced machine learning algorithms and deep neural networks, such as deep learning, and improves without direct human input. Certain AI models, like transformer-based architectures (the ‘T’ in GPT) rely on data to improve their ability to reply and make (better) predictions overtime. This is what makes this technology different from anything that has come before it. It is self-reinforcing and can be unleashed to run independently. Change after change in history, including a variety of technologies, has moved the landscape, made certain skills and jobs redundant while creating others. Think of a bridge replacing a ferry. Computers replacing typewriters. Cloud-based software (SAAS) replacing dedicated workstations, among others. This one is different.

With that said, it would be both societal and personal mistakes to stay away and try to close the stable door after the AI-driven, robotic horse has bolted. Sticking one’s head in the sand is not the solution. More likely, not making an effort and attempting to learn passively is not adequate. It is counterproductive. The fact of the matter is, and not many would admit it, that AI is not well understood even by the scientists and programmers at some of the best-known tech organizations of today. In such an environment, the most sane way to proceed, is to counterintuitively lift the lid, bring AI to the masses, give everyone every opportunity to use the tech, be educated on it and make it publicly sourced. Legislation is desirable, and necessary, because guardrails and moats do not build themselves, but even more powerful is a public taught to understand what AI is, what it is not, what it does, what it does not do and transparently knows where and how this technology is deployed. Shining a light, embracing and understanding is the best antidote to ignorance and the best way to insulate oneself from becoming redundant thanks to AI – or any change for that matter.

Things That Need To Go Away: Being Scared Of AI And Trying To Dock

What Is Governance In AI?

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Nov 282024

In an earlier post, I discussed the importance of governance in Artificial Intelligence (AI) and how, arguably, aside from the initial hurdle of getting started, governance is one of the most significant barriers to adoption, particularly in large enterprises. Concerns such as liability, intellectual property and the risk of introducing incorrect or biased information into AI models are often cited as the biggest impediments to AI integration at scale.

My previous advice encouraged experimentation, emphasizing the importance of gaining momentum, learning from efforts and celebrating small wins. However, as promised, this follow-up aims to define what governance in AI really means. The first paragraph above provides some context, but let’s dive deeper.

AI-governance

Governance in AI refers to the set of practices, principles and processes that an organization establishes to develop, deploy and manage its Artificial Intelligence systems. In practice, this encompasses all systems that provide data to the AI, all outputs and outcomes generated by the AI and all stakeholders – individuals, teams, departments – whose jobs, roles and successes are influenced by AI. Since AI is fundamentally built on data, this broad reach underscores the technology’s far-reaching impact.

AI is still relatively new in the wider society and not fully understood. It is imperative that the governance framework adopted by an organization is designed with a clear end-goal in mind, and is implemented transparently, with widespread knowledge across the organization. This approach helps the AI initiatives align with organizational acceptance.

This does not imply that organizations should become paralyzed by over-analysis, as failing to implement AI would likely mean falling behind in today’s competitive landscape. The key to success lies in balancing careful governance with agile action. Trust is a vital component of AI adoption, and proper governance fosters trust by ensuring transparency and accountability.

Additionally, AI systems must be regularly monitored and evaluated to ensure they continue to function as intended, without introducing unforeseen risks or biases. This ongoing governance is essential for maintaining the public’s trust in AI technologies, as well as ensuring compliance with evolving regulations.

AI governance is multifaceted, but definitely possible and practical. Keeping a human in the loop is a check against an unintended consequence. Diverse stakeholders need to focus on long-term goals and organizations must engage to harness the full potential of AI while minimizing risks and fostering trust.

Things That Need To Go Away: The ‘AI Can Wait’ Attitude

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