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

Data Governance In AI

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Nov 132024
 

I speak with buyers on a mission to procure the right product and services for their project. They include decision-makers figuring out the best applications for their companies.

 

They are all interested in AI and range from experimenting with LLMs (Large Language Models) and ML (Machine Learning) in silos to those who are eager to unleash AI for all their employees to take advantage of a range of possibilities. More and more, every one of the above conversations conveys a simple concept: AI is becoming pivotal to everything we will do.

Did you know that 91% of info/tech companies have mentioned ‘AI’ in their earnings calls at least once so far this year (2024)? Yet, a common concern arises consistently in conversations with enterprise management:

  • They are concerned about AI governance.
  • The compliance team is worried about data integrity and inputs.
  • The security team is daunted by the task of implementing AI that impacts their products and
  • Senior executives are uneasy regarding the potential implications if anything goes wrong during customer and end-users interaction with the technology.

These are legitimate concerns.

We will address and define what governance in AI is in a subsequent post. For now, it is important to remember that there are no absolutes and, the answer is, that we all must have the same expectations of AI as we have with any other piece of technology. Put another way, it makes sense to think of AI in the same way we expect the Internet or SAAS applications to function. Having said that, there are actions that technology custodians can and must take.

For AI governance to be done right we need to meticulously follow responsible data practices. These include:

  • Documenting the origin of the data and educating the user base is a start.
  • Similar transparency in where the data will be deployed and which use cases it covers is required.
    Abiding by applicable laws is a must and non-negotiable. These include the EU Artificial Intelligence Act, which went into force on the 1st of August 2024. Additional regulations are en route, from federal and state jurisdictions, such as Canada’s AI And Data Act. The legal framework includes the above bullet points, yet it is important to remember that the framework and implementation will always evolve, so keep an eye on its implementation and evolution.
  • Keep a Human In The Loop. In other words, designating a person to interact with the LLM ensures human oversight; allowing for timely intervention when needed. The underlying models are getting better and algorithms learn and improve, but HTL allows for human intervention in any case.

In tandem, we know that the LLMs by IBM, Meta and others allow for scrutiny and the peace of mind of the user community to some extent because they are open source in code and licensing. Other models strive to accomplish the same credibility by offering access to the foundation models. This does not infer perfection. It does imply scrutiny and a level of credibility, however.

Being concerned and diligent is warranted. Not moving forward due to fear of the technology, however, is a recipe for falling behind. To stay competitive, remain informed about AI developments, begin experimenting and consider a low-risk use case for an initial quick win.

 

Things That Need To Go Away: AI At Any Cost And No AI At Any Cost

Artificial Intelligence (AI) And Sales & Related Jobs

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Jun 092024
 

AI, in general, is a hot topic everywhere. This site has discussed the nature of AI before and posted about the hype versus reality of it. Every passing day has made the promise and reality of AI more real, more rewarding and more ominous. The aforementioned AI article listed several vendors who had begun working on inserting artificial intelligence and its brain, machine learning, into sales and its various departments.

A recent quotation by Eric Yuan, chief executive of Zoom, the video conferencing company, was worth attention. He is suggesting that an AI version of us can attend meetings for us in five or six years. The implication is that a digital version of us could act for us and, moreover, would be as diligent, acceptable and effective as the person it represents. Yuan was careful to couch this as an augmentation technology as opposed to a replacement one. The scenario again was the utopian dream of having much more leisure and downtime. Yet, his thoughts, if taken to their logical conclusion, could make a reader imagine being replaced by the said system gradually. Moreover, what is stopping AI from creating multiple copies of us? AI could create dozens of me if it can duplicate me once.

 

Artisan, a company which bills itself as “Creating Highly Advanced Digital Workers … Using Cutting-Edge AI Technology,” is marketing itself as offering functional AI avatars replacement of salespeople, customer success, BDRs, recruiters, financiers, etc. with names like Ava, Liam, Noah, Olivia and others. Thought prospects are being bombarded by business development and sales now? Wait until a million Avas start getting in touch. In fact, among the features Artisan advertises per its artificial human replacements is “Sends 1000s of emails per month.” Buyers, decision-makers, V and C-levels take cover. Incoming! Pricing? From less than $1,000 (US) down to almost $100 per month. Do I get to talk to AI if I contact their sales department though?

Similarly, there is GoCust, which advertises itself as “Sales Teams Now Have Online Assistants.” It touts itself as a SFA, assistant, mapping and route optimizier, that gains cost and time advantages in order “to minimize the need for sales teams to constantly struggle with messaging, phone and email traffic…  develop solutions to increase the rate at which customer conversations are recorded as data… to offer sales managers the opportunity to proactively manage their teams.” And these companies are not alone.

All of this has become possible, in the meantime, because storage and computation costs have fallen drastically and are basically cheap.

In the meantime, according to a study by Zendesk, which itself has jumped wholeheartedly into the AI space ($50/user/month), management believes AI is beneficial and helpful. “Four in five (81%) employee experience leaders now see AI as essential in boosting workers’ ability to tackle complex tasks,” reports Zendesk.

At this point, a forecast of five or six years for these technologies to be operationalized and become part of the mainstream seems frankly… conservative.

 

Things That Need To Go Away: Efficiency, Effectiveness And Productivity Ai Technology That Never Mentions The Potential For Creating Layoffs And Attrition. The Discussion Around These Technologies Needs To Be Brave And Candid.

Take The ‘l’ Out Of Ali

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Sep 142023
 

And you have AI. My friend Bhuvan and I were chatting last night and the conversation drifted to the topic of AI (Artificial Intelligence). Owing to several movies in the last few years, OpenAI and its already mainstream ChatGPT, and the general proliferation of the concept and technology the acronym has become a buzzword. We were remarking on the tech and its usefulness versus hype when Bhuvan joked that taking the ‘l’ out of my name may be a neat idea.

In all seriousness, we have all begun working with, or at least seen mentions of and read about, Artificial Intelligence recently. To be exact and lucid, AI is serious business with a serious utility (in more ways than one). Persons who do not learn to use it are falling behind. Companies that do not adopt it will be at a disadvantage.

With that said, at this moment in time, hype is overtaking both the utility and reality of the state of AI. Like any other concept, humanity tends to overdo everything. Everyone hires at the same time, fires at the same time, buys stock in tandem and sells at the same time. AI is not a FAD, but its idea is being abused today.

Artificial Intelligence came up because while travelling last week my home airport was displaying advertisements regarding AI. Then upon landing (and later returning) digital billboards advertising companies with ‘AI’ products were ubiquitous. Walking around San Francisco my eyes caught glimpses of an ‘AI’ dry cleaner and an ‘AI’ cafe. Being short on time, and probably cynical, I kept going, but should have otherwise taken a moment to step in and find out more. Perhaps folks are confusing AI with mere automation the same way the new name for the software industry seems to have become ‘SAAS.’ On the other hand, perhaps there was true AI at work at these businesses, although my doubt lingers.

What do you folks think?

 

Things That Need To Go Away: Confused taxonomy around hip technologies

Let’s Talk Artificial Intelligence

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Apr 302022
 

 

 

I posted an article on Sales Enablement recently. Much of the modern software used in that niche utilizes Artificial Intelligence (AI). So let us focus on AI now.

 

What Is AI?

Firstly, let us understand what AI is. Most of us will think back (forward?) to Arnold, Terminator and Skynet and why not? Machine Learning is a subset of AI, but more precisely Artificial Intelligence is programming that teaches a system to mimic human behaviour and actions, but obviously at a faster and more effective manner that brings with it the consistency of a machine. More completely, AI is a series of networks that leverages statistics and instructions over and over to emulate humans. It is designed to improve overtime as well because the more ‘experiences’ (a.k.a. statistics) it has the more complete it becomes.

One more thing, AI may be all the rage now, but it is hardly new. This notion goes back to Alan Turing and the 1950s. For an early application look up ELIZA from the 1960s.

So What (For Sales)?

The end goal, however, remains somewhat elusive. Systems are not perfect. It is thought that perfection has been attained when humans cannot fathom whether they are dealing with a machine or a human being and results are impeccable. If AI, therefore, includes Machine Learning, analytics, natural language, simulation, learning and interaction then how can it help the profession of sales? Here the idea is to take all the information and transaction in sales – conversations, e-mails, responses or lack thereof, every CRM entry, every sale, every lost deal, et cetra – and put them into one place in order to help the seller. The goal is to identify the correct course of action, the next step, the way to help customers and sellers and to win business. Is it possible? To some extent the answer is yes. The hesitation, however, stems from the unpredictability of human psychology and of course different cultures and needs or wants. Yet, AI is supposed to learn those too because after all, it is all data translated to action.

 

So, Is AI Going To Take Over The World And Rid Us Of Our Jobs (And Sustenance)?

Maybe. Still, as of today the reality on the ground is that AI is here to assist, help, improve and enhance the seller’s efforts not replace it. Put that way, would anyone argue against help? Which salesperson would claim he or she does not need help? One issue, that one can foresee easily, is that AI may be trained to be biased to think like a seller or a vendor. To be successful, this writer supposes, AI needs to think like a customer or prospect. That is the way to successfully sell after all.

 

So Which Are The Tools?

Like any other category, AI solutions are bound to be comprised of the good, the bad and the so-so and trials, proofs of concept and honest assessments are a must. It is smart to gauge results, ask the user community (the sales team) honestly and measure revenue enhancement before committing. Randomly picked, because TNG and SugarCRM are as good or bad as any other to keep an eye on, I have bookmarked this in order to track the revenue for my ‘proof is in the pudding’ hobby tracking, but truthfully the market will speak sooner or later.

 

One last thing. Candidly put whether effective or not, the reality is that the market for AI-driven solutions in sales is going to expand. Just keep in mind how much salespeople have traditionally disliked using CRM and yet the parallel expansion and growth of the sector! One factor that speaks to my hypothesis is the growth of AI in other niches. With increased adoption of AI in healthcare, customer service, arts and more the concept is becoming mainstream, which means more revenue for the sector to enable improvement and also for more people to become more comfortable with the notion.

 

Here goes a list of vendors and providers in the Sales AI space:

 

  • Affinity (including Nudge.ai) – A tracking CRM for industries where relationships are important.
  • Conversica – Provider of a conversational AI. Claims that all its AI Assistants are more accurate than a human. Suited for business development and marketing.
  • Clari – An opportunity management and forecasting tool to offer better visibility to sales teams.
  • Drift – Sales and Marketing conversation at the right time with the appropriate content plus insights especially for inbounds.
  • Exceed.AI (Part of Genesys) – Similar to drift geared towards inbound prospects and leads for sales and marketing, it automatically picks up the conversation, sets appointments and updates Calendars.
  • Gong.AI – Captures and analyzes customer interactions for insights and next steps.
  • Heyday – Tuned for retail, Heyday’s AI connects inventory and catalogue to customer search results and nudges sales to connect with customers when most appropriate.
  • Introhive – Relationship intelligence that leverages CRM to reveal ones network and relationships with customers.
  • Kixie – Automates calling and texting of the names in CRM and records and tracks the events.
  • People.ai – Provides persona-specific productivity tools and provides insights.
  • SalesDirector.ai – Offers predictive insights into sales team’s pipeline and customer interactions.
  • Salesforce – Salesforce, the leader in CRM, has embedded AI in much of its solutions for insights and automation.
  • Saleswhale (Part Of 6Sense) – An AI assistant to engage with and follow-up with leads.
  • VeloxyIO – A platform that integrates e-mail, CRM and calling into one solution and view.
  • Zendesk – Engage with and support customers across a myriad of channels and keep all interactions in one place.

 

*Things That Need To Go Away: AI technology companies that are made to be acquired as opposed to being there long-term to help customers.

Watson Versus Cancer

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Dec 182012
 

Do you remember IBM Computer Watson? It competed against two Jeopardy champions in 2011 and won.

http://www.bbc.co.uk/news/technology-12491688

The computer is back with a more practical and serious purpose. New York’s Memorial Sloan-Kettering Cancer Center and IBM are using Watson’s computing capability and massive database of 1.2 million current and former patients spanning 20 years to help the specialists diagnose cancer and recommend treatment options. Watson will tap into both the patient records and outside data, something it successfully demonstrated ability for on Jeopardy.

The question is whether Watson is as good in diagnosing alternatives as it is in reaching out into a datamart of facts, figures and general information.

Watson has been tasked with working on lung cancer to start.

http://www-03.ibm.com/innovation/us/watson/index.html