Category: AI Labs

TLDR: Over the last few weeks, sentiment towards AI has shifted from optimism to fatigue. On the Gartner Hype Cycle, AI is now entering the “Trough of Disillusionment,” a phase where hype-driven expectations have been left unmet. But while it’s easy to dismiss AI in the short term, history has shown that those who continue to experiment with new technology as it approaches the “Slope of Enlightenment” and eventual “Plateau of Productivity” will greatly benefit in the long term.

Welcome to the Trough of Disillusionment.

Wow, that was quick!

In the course of a week, I’ve started to see the bright lights shift from optimism to fatigue. LinkedIn, Twitter, National News Media, colleagues, friends, and family are all starting to roll their eyes at any discussion of AI. This is predictable, natural, and ok.

 

“It’s perfectly normal
to be skeptical.”

 

The AI hype has been a bit omnipresent. Hyperbole or not, the idea that AI is the next big step for humanity is being tossed around. It’s perfectly normal to be skeptical. It’s also predictable that the hype can not deliver the promise in the short term.

 

AI has achieved a lot in the last 6 months.

GPT-4, Bard, Midjourney, and Adobe Firefly have taken exponential leaps forward – with outputs almost indistinguishable from magic. People are concerned about the route this “choose your own adventure” AI will take from the incredibly positive (think cancer cures) to the extremely negative (think Terminator AI soldiers). It’s easy to dismiss this in the short term because the crystal ball is cloudy today.

We’ve been pretty bad at predicting the future when it comes to AI. We predicted we’d see factory AI robots first and AI creative last. It’s actually been inverted.

We’ve entered a new phase of the technology Hype Cycle called the Trough of Disillusionment.

 

 

This was developed by Gartner in 1995 and has been consistently used to monitor the phases of technological introduction to adoption. It’s pretty bang on when we look at the current phase of AI.

 

Peak of Inflated Expectations

We’ve had our Trigger event; In late November last year, ChatGPT was released to the world and it was the fastest technology to reach a million users in history. From December to June, we’ve gone up the curve toward the “Peak of Inflated Expectations.” What have we been told? The world is going to be changed forever. White-collar jobs are going to be replaced. A million new AI software tools are being launched weekly.

 

Trough of Disillusionment

Now we’ve reached or have passed the “Peak of Inflated Expectations.” Interest is starting to wane because the expectations of the hype aren’t being met. I think we’re now just entering the downward slope to the “Trough of Disillusionment.”

For example, I saw a post by MOPs meme master Jason Raisleger and the gist was, “OK, OK, I know I’m using ChatGPT wrong.” And today I woke up and read a newspaper opinion piece titled, “Will AI really change everything? Not likely.” It concludes with, “So the next time you hear a platitude spoken in the worship of AI, feel free to roll your eyes.” Even when technology moves fast, and AI definitely has, we humans can be predictably impatient.

 

“Those who continue to experiment
will benefit in the long term.”

 

Some people are getting to the trough quicker than others. But history has shown that those who stick around and continue to experiment and iterate with the technology will benefit in the mid and/or long term.

 

Slope of Enlightenment

The “Slope of Enlightenment” happens when the ways the technology can benefit the enterprise start to crystallize. Think internet and e-commerce in the late 90s and social media and targeted social ads in the late 00s. It takes a while for new technology to demonstrate its commercial value. Social ads were pretty effective at targeting up until we asked apps to stop tracking us on our phones.

 

Plateau of Productivity

The final stage in the Gartner Hype Cycle is the “Plateau of Productivity.” This is when the benefits, applicability, and relevance of the technology are very clear and investments are paying off. You can argue about if and when this is going to take place, but it is ultimately a predicted path for the future of AI.

You could even say that Adobe’s Firefly AI product, released in beta in Photoshop, is already approaching the plateau. There is no doubt that for creatives, the Slope of Enlightenment has been embarked upon. And while not everyone is a creative, I encourage you to ask an art director about AI – ask them if they think this is a fad.

 

The Route We’re Taking at RP

Our crystal ball, like at most times, is cloudy and unclear. What is predictable, though, is our behavior and impatience. The Hype Cycle helps us understand that this is what we do.

While some may pack up their AI enthusiasm for now, that’s not the route that we’re choosing to take at RP. We’re going to continue to learn, experiment, and iterate with AI. It’s probable that AI will impact our work and our client’s work for the foreseeable future. We’re going to push through the Trough of Disillusionment for the promise of the Slope of Enlightenment.

We hope to see you along the way, but we can always catch up at the Plateau of Productivity.

TLDR: AI tools allow us to work faster than ever before. But with this speed comes several organizational challenges, including quality control concerns, integration issues, and increased pressure on decision-makers. Companies must identify these problems and prepare for them to fully benefit from the productivity and efficiency increases that AI can provide.

When I think about the impact of AI on businesses, the most significant factor is speed; The countless AI tools at our disposal allow us to work faster and more efficiently than ever before.

But in the wake of such speed, it’s crucial to acknowledge the organizational challenges that may emerge – and the need to identify and prepare for them.

Let’s take a closer look at specific problems companies will face as AI accelerates operations.

 

“AI tools allow us to work faster than ever before.”

 

Approval Lags & Quality Control Challenges

Once teams streamline and optimize their processes through the use of AI systems, projects might move faster than management can review and approve them. If managers don’t have the capacity to audit and control these fast-moving projects, the result will be either:

(1) significant delays as managers catch up or
(2) decreased quality as unchecked work slips through.

It will be crucial for managers to remain highly detail-oriented throughout this operational transformation; overlooking finer points or skipping essential steps in a process could lead to costly problems down the line.

 

Integration Issues

When it comes to implementing AI systems to speed up tasks, many teams may face early integration issues with existing tools and workflows. Organizations who fail to configure their processes properly and troubleshoot technical setbacks effectively will face significant disruptions and risk falling behind.

 

Quality vs. Speed

This also complicates the delicate balance of quality vs. speed. While AI systems certainly have the ability to speed up our work, there are many situations where rushing tasks could lead to compromised quality. It’s essential to carefully design processes in a way that maximizes AI assistance while maintaining the standards you’ve set for your business.

 

Increased Pressure on Decision-Makers

This quality vs. speed problem not only applies to day-to-day work but higher-level decision-making as well. As projects move more quickly, leadership teams and C-Suite executives will be pressured to make high-impact, informed decisions on accelerated timelines. To effectively adapt and thrive in this fast-paced environment, companies may have to restructure traditional decision-making hierarchies in favor of new strategies and agile methodologies.

And pressure on decision-makers will also come in the form of heightened expectations from company stakeholders. Consistently maintaining high-quality output at increasing speeds will be a real challenge that can lead to disappointment and friction between leadership and ownership groups.

 

Managing Rapid Change

It’s clear that the implementation of AI has the potential to rapidly change the way we work and make decisions — and this will likely cause disruption throughout many levels of your organization. If this rapid change is managed poorly, leaders will be met with resistance as employees become overwhelmed, confused, and even less productive than before.

 

“We must pay attention
to the fast-moving
developments of AI.”

 
 

There are many potential challenges ahead when it comes to utilizing AI systems to speed up our work.

But if we prepare ourselves and manage the integration of these tools skillfully, the resulting increase in productivity and efficiency will be game-changing.

Now more than ever, we must pay attention to the fast-moving developments of AI.

That’s all for this week.

TL;DR In our first AI experiment, our team used GPT analysis to determine our Ideal Customer Profile (ICP). Our process involved data preparation, cluster analysis and refining the dataset using a genetic algorithm. This experiment was a significant step forward, enabling us to determine an organization’s true ICP within just a few hours of work. Exciting possibilities lie ahead!

A couple of weeks ago, one of my colleagues said to me, “I really believe that within 3-5 years, we’ll be able to analyze Salesforce data and determine the true ICP of an organization”.

My immediate thought to this was: why wait?

With the AI tools available at our disposal, we can do this now.

 

“Determine the true ICP of
an organization within a few hours.”

 

So I put a small team together, and we started working on a way to achieve this – and frankly, the results were pretty incredible. After 3 days of work, we came up with a process centered around GPT analysis that can determine the true ICP of an organization within a few hours.

Here’s how we did it.

Step 1: Data Preparation

The first thing we did was ask ChatGPT directly how to approach this project. Essentially, it told us we needed to run a cluster analysis on a CRM data export using a Python script (this is the simplified version of the response).

With that in mind, we went into Salesforce and exported the last 2 years of our company data. For privacy and confidentiality reasons, we anonymized the data by deleting some information like client names and project values, then replaced those fields with unique identifiers and profitability approximations.

 

Step 2: Cluster Analysis

After that, we ran our data through a cluster model using a Python script we created. After some testing and tweaking, we were left with 4 cluster groups based on 3 company variables we chose: profit, frequency of purchases, and number of employees. This cluster analysis in and of itself is far from revolutionary – but the next step is where things started to get very interesting.

 

Step 3: GPT’s Initial ICP

We took our 4 cluster groups and asked GPT to give us a description of each – and it did. It was able to pull from its enormous knowledge base to give relatively accurate, complete descriptions of each group. Then, we fed GPT a short definition of our company profile and asked it to define our ICP based on the cluster group that best fit. The results were interesting, but it wasn’t exactly what we were looking for. How could we refine things further?

 

Step 4: Refining Our Dataset

We decided to use a genetic algorithm whereby ChatGPT would generate a list of companies that fit the chosen cluster, then regenerate the list but keep the top 3 companies and drop the worst 2 companies. After doing this 25 times, we were left with a highly refined list of 27 companies that matched the chosen cluster group.

 

It gave us information about
revenue, growth profiles and marketing priorities.

 

Step 5: GPT’s Improved ICP

Now, using this new list of 27 companies, we asked ChatGPT once again to generate an ICP for us. And this time, the results were amazing. It gave us extensive information about revenue, growth profiles, industry, location, marketing priorities, primary tech, and business models. Astoundingly, it also summarized all of it beautifully for us.

 

Step 6: Going Deeper

In pursuit of the best results possible, we took things a step further by giving GPT more information about who we are as a company and where our strengths lie as an organization – allowing it to refine our ICP even further based on that.

We also asked GPT questions about our ICP, such as: Who are the decision-makers at this company? What are their pain points relative to their positions?

And it gave us great material to work with, highlighting general ICP pain points as driving growth in revenue, optimizing and improving marketing and sales efforts, keeping up with industry trends, and ensuring seamless integration of technology and security. Wrapping it all up by asserting that if we focus on solutions to these pain points, we can demonstrate value to decision-makers.

 

Data Privacy and Safety

As a final note, I think it’s important to emphasize that there is no data privacy or security risk involved with this process either. The core Salesforce dataset we used was not only anonymized, but it was never actually touched by the GPT engine; The data itself was only used in our internal cluster analysis before we fed these clusters – which are essentially just groups of companies based on characteristics – into GPT.

The irony of all this is that when we look at the list of companies GPT defined as our ICP, many of them are already our clients. But it also gave us a list of companies we need to start a conversation with, which is highly valuable for any business.

Overall, this first experiment has been an excellent step forward. Having a process that can utilize GPT analysis to determine the true ICP of an organization within just a few hours of work is incredible output.

Stay tuned for what’s next.

P.S. In case you missed it, you can read our post about the essential skills of the AI economy.

TL;DR: AI’s rapid advancement poses new challenges. Essential skills for the AI economy include AI literacy, ethics, and human-AI collaboration. Organizations and individuals need to proactively develop these skills to align with the demands of the AI economy. Read last week’s post here.

At this point, I wouldn’t be surprised if you’re already using some form of AI technology on a daily basis in your personal or professional life. Nearly every week, a new AI tool is released that can boost our productivity and improve our work.

But with the accelerated advancement of AI comes a slew of new challenges and implications for our future.

Last week, we discussed how the rapid evolution of AI would impact the core skills we’ve come to rely on over the previous three decades in the knowledge and digital economy. And with so many of those core competencies now called into question, what areas should we prioritize as we go forward?

 

“What essential skills will we
need in the AI economy?”

 

In other words: what essential skills will we need to flourish in the new AI economy?

I don’t have a crystal ball, but here is what I think will be the most important.

AI Literacy

Understanding the fundamentals of AI technology will be a critical starting point for both individuals and organizations. This means constantly updating yourself on AI’s evolving capabilities and applications, as well as familiarizing yourself with core concepts that underlie these tools – such as machine learning, big data, model training, and so on.

 

AI Ethics and Responsibility

Looking at AI ethics and responsibility from an organizational perspective, it’s crucial to explore how AI will affect not only the company’s products or services but also internal relationships and team dynamics. We must recognize that these systems are currently trained on biased data sets; therefore, they often produce biased responses. Educating our teams on these potential biases and inaccuracies is important as we integrate these tools into our daily work.

 

“We’re in uncharted waters,
and we must tread carefully.”

 

There are also much broader concerns pertaining to AI ethics and responsibility. Many have been pushing back on AI development, with tens of thousands signing an open letter to slow down the training of more powerful AI systems. Another report recently released about a top-level engineer at Google who expressed concern about the dangers of the AI chatbot he is helping to create.

We have entered uncharted waters, and we must tread carefully.

 

Data Management and Privacy

As organizations train AI systems on specific data sets to prompt deeper analysis, they must securely manage data to protect user privacy and confidential company information. Who can access this data to train the system, who will have access to use the system itself, and where will the system be hosted — these are all questions companies must consider from a data management and privacy perspective.

 

 

Human-AI Collaboration

On an individual level, we must ask ourselves what our core skills are and how AI tools can complement them. Keeping up-to-date with the latest AI tools and getting creative with how we utilize them to accelerate our productivity and improve our output will be a major skill as we look ahead.

 

AI System Design and Development

The number of applications designed with AI support will grow exponentially, highlighting the importance of people who can design and develop these large language models and AI systems. Even becoming proficient with key programming languages like Python will have short-term and long-term benefits, allowing you to interface with existing APIs to create customized systems that enhance AI collaboration. Programming will become a real superpower, especially if you’re in a technology-oriented space.

 

AI-Driven Decision-Making

As AI systems become more complex and intricate, their ability to generate actionable insights will increase as well. Going forward, combining and utilizing the right AI tools to generate these insights and draw conclusions from them will be an essential skill.

 

“Many of the roles we
fill will be transformed.”

 

Change Management and Adaptability

The disruption that will take place as AI systems become more integrated within organizations will be significant. Leadership must be able to manage this change and help their teams adapt to new skill sets, new collaboration methods, new organizational structures, and so on. Many of the roles we currently fill will be transformed and rescaled — adapting will be vital for success.

 

AI-Enhanced Creativity and Innovation

Regarding creative work, we already see potent tools that can generate high-quality writing and images. Naturally, these tools will continue to reach other creative domains like video, animation, and music to the same effect. And with the help of these tools as AI collaborators, the creative process will accelerate and transform; There will likely be less time spent on ideation and labor and more time spent on art direction and editorial decision-making.

 

AI Policy and Regulation

It’s absolutely critical for organizations to understand policies and regulations surrounding AI systems. What are the legal implications of using these tools? What are the creator’s rights? These are just a few of the many questions companies must consider when developing internal policies and regulatory frameworks.

When we take a closer look, there is quite a departure from the skill sets of the knowledge and digital economy we previously discussed.

And I think there is a major opportunity here, from both an organizational and individual perspective, to proactively develop our skill sets so they align with what the AI economy will demand.

We have a choice to make:

Either we self-initiate this disruption and get ahead, or we wait for AI to inevitably cause disruption for us.

That’s all for this week.

TL;DR AI has introduced a force of such radical disruption that most of the skills of the knowledge and digital economies have quickly become historical artifacts. Which skills will the AI Economy emphasize?

When I first got my hands on ChatGPT in November of last year, I was blown away. Its ability to summarize and synthesize with such a high degree of quality was a major eye-opener for me — I couldn’t believe we were here.

And as AI technology continues to advance at breakneck speeds, we must brace ourselves for the radical disruption that lies ahead. Every week that passes, it feels like an exponential leap has been taken forward in both an understanding and appreciation of what is changing for good.

Large language models like ChatGPT are challenging the very foundations of the knowledge economy, calling into question the core competencies we have come to rely on within creative roles and knowledge jobs.

 

“Every knowledge job
is at risk.”

 

I would even go as far as saying: every knowledge job is at risk. And I can guarantee that 9 months from now, our technological landscape will have advanced so dramatically that this moment will feel like a distant memory.

So how do we stay ahead of the curve? We’ve got to change our mindset. We must try to evolve from the old ways and explore the new opportunities that AI provides us.

Since the late 80s/90s, we have lived in the eras of the knowledge and/or digital economy. I think a great starting point is to explore the essential skills of the knowledge economy that are radically disrupted by AI.

 

Digital literacy

Technical proficiency with computers, smartphones, and the internet is now a baseline prerequisite. Digital literacy will take on new meaning as AI-driven tools and systems become more sophisticated; staying current with rapid developments in AI will be essential.

 

Data analysis

The ability to collect, analyze, and interpret data as it is formerly known will no longer exist. AI algorithms will reach a level of data analysis proficiency that eliminates the need for human involvement and devalues this skill altogether.

 

Critical thinking and problem-solving

AI’s capacity to analyze vast amounts of information more quickly and accurately than humans, along with its ability to take over routine and mundane tasks, will free up time and energy for us to focus on more complex problems that demand critical thinking. This is an area that is still in our hands, however, we will become increasingly reliant on AI-assisted methods going forward.

 

Emotional intelligence

High emotional intelligence was a real focus for great leadership in the knowledge economy. But with increased reliance on AI technology, it is very possible that we will see less face-to-face human interactions — which may cause emotional incompetence when dealing with teammates and clients.

 

Collaboration and teamwork

The collaborative process will be completely different when an AI system becomes a team member. Team dynamics will change, and in some contexts, AI will eliminate the need for human collaboration completely. And what will happen when a member of the team is replaced by AI? Could this spawn a new form of workplace paranoia? These are questions we will have to contend with going forward.

 

Adaptability and learning agility

The incredibly fast pace of AI development will likely continue to accelerate, making adaptability and rapid learning essential skills. How fast people can adapt to change and whether or not they have the aptitude for rapid learning are defining questions that will dictate survival in this new economy.

 

Leadership and management

A strong understanding of AI technologies and a willingness to adapt to the latest advancements will be a continuous point of emphasis for leaders of the future. They must learn how to effectively collaborate with AI systems, integrate them into their decision-making processes, and gain a deep understanding of how AI will impact their employees and the business as a whole.

 

Cultural awareness and global mindset

This is an important area where AI-powered tools can enhance cultural awareness by providing access to different information sources, perspectives, and insights. When AI systems are sensitive to cultural diversity, we can use them to educate and train our teams on the cultural impact of their decisions and improve relationships.

 

“Embrace the AI change to stay ahead.”

 

As we are discovering, there is a wide range of both positive and negative effects that follow the rapid development of AI.

It is crucial to embrace this change to stay ahead — not run from it.

Lean into what AI can offer you and your organization, leveraging it to support and improve your processes.

And while many of these areas will be strongly impacted by AI systems, it is unlikely that these will be the key characteristics of the AI economy.

That is a conversation for next week.