All posts by Dan Rose Johansen

I hear a lot about how the 5G mobile network technology will change the world and especially be a big enabler for applied AI. But is that really true? Will 5G be an AI gamechanger? 

The short answer is no. 5G is nice. It's a convenient technology that will be nice for AI but in no way a big deal. The best comparison I can make is that 5G is like new roads. Asking people behind AI solutions if 5G is a gamechanger is like asking a shopkeeper the same about new roads. And that being in a location with already decent roads. The shopkeeper would probably answer something along the lines of: "It'll be nice. It'll be a little easier for my suppliers to bring goods and a little easier for my customers to reach me. And that might be especially for shops in the city, where the new roads are prioritized due to being more used. But it won't be something that can immediately be seen on my stores profits.". 

So what does that mean? It means that better infrastructure is always nice, but if it's already decent like 4G, it’s not a huge difference. 5G is an incremental improvement to the infrastructure surrounding AI. As the infrastructure is just one of many parameters that affects AI solutions and grocery stores alike, it’s nice but no biggie. 

How 5g works

So why is it just an incremental improvement? Isn't 5G ground breaking technology ? 

Well no. 5G is like 4G radio waves transmitting data. 5G radio waves are just different from 4G waves by being shorter wavelength that can carry way more data as a result. And shorter wavelengths are not groundbreaking. We have actually been able to make short waves for a very long time. 

So why haven't we done this before? Well there’s a lot of practical problems with shorter wavelengths. They reach a shorter distance and they have trouble going through walls and other material. To counter that problem you have to put more towers closer together. More towers means more costs associated with setting up and maintaining the towers. Furthermore having towers closer together brings another problem. Radio waves interfering and disturbing each other and as a result making the data transfer unstable. 

So why now?

Finally 5G is here. And that's for two reasons. I might sound like I’ve been putting 5G down as an unimpressive technology that deserves no credit. But 5G is impressive. Just not on the immediate effect on society. 5G is impressive like building a big bridge. When you build a big bridge you have to go through way more trouble figuring out how massive concrete can hold up in the conditions it’s given. That’s impressive engineering compared to a small bridge.

And that is one of the reasons we are getting 5G now. Some really impressive engineering have gone into getting around the problems with shorter wavelengths. Making the short waves reach enough ground to make 5G practically viable and making it not interfere too much is really impressive.

The other reason we get 5G now is simply that we are so many people using so much mobile network that we can share the costs of all those mobile network towers and make a good business case. 

That’s it plain and simply. 5G is nice and an improvement but it’s no crazy invention that will change the world tomorrow.

Sometimes a very impressive algorithmic achievement is done and it should be celebrated. GPT-3 is a great example. GPT-3 is amazing engineering and data science and very well deserved it gets a lot of media attention. But for every GPT-3 there are hundreds of thousands of AI solutions, that are based on standard algorithms and not necessarily a genius achievement but a school book approach. 

It might sound like I’m having a go at many AI solutions out there but in fact it’s the other way around. Going for a groundbreaking genius solution is for the vast majority of AI-cases not the right way to go. The standard algorithms can in most cases easily be sufficient for the task at hand and everything beyond that effort is usually bad business. 

Beware when someone claims a genius or even special algorithm

Given all this I still hear a lot about the “unique”, “genius” or “special” algorithm that some company has developed to solve a problem. I often hear terms like this in the media and the fact that this is so popular makes a lot of sense. When you have a business that you want to market and sell your product at a high price. It also helps to scare competition away when they think that the entry barrier to make a certain product or solution is very high. But that is what the genius algorithm is 99 out of a 100 times. A marketing message.

In reality much of the AI out there is standard algorithms such as CNN’s, Random Forest or even logistics regression that some would claim isn’t even AI. These algorithms can be used by most novice developers by using freely available frameworks such as Tensorflow or Scikit learn. 

My primary reason to write this post is the same as for a lot of our posts I’m writing. I want to demystify AI and by killing the narrative about the genius algorithm I hope more people will have the chance to utilize AI. 

So when you hear these claims, be critical and don’t let it be the reason not to get started with AI. 

The media is at fault

I’m not usually one to call “the fake media”, but in this case of AI I fell that the media has not lived up to it’s responsibility and a a naive way followed the hype and corporate press releases without taking the critical look that is in many ways what separates the news outlets from other information sources. 

I often wonder how the danish(Where I live) news stations can have an Egypt correspondent but not an AI or even deep tech correspondent. The events in Egypt might not be as important to the everyday life in my and many other countries than AI is starting to have. 

I really hope the media will improve here and not keep AI in a mystified aura.

The future of AI-algorithms

I’m pretty sure the future for AI-algorithms are a given. The big tech companies like OpenAI, Deepmind, Google, Facebook and Apple will be the ones to develop the genius algorithms and very often release them into the wild for everyone to use. It’s already happening and we will only see more of this. So claiming to have a genius algorithm is just not very likely a true claim in the future.

One of the most important questions when starting to work with and implement AI in your organization is also one of the most complicated to answer: Should you buy off-the-shelf AI products, build your own in-house or have it built custom by consultants?

There’s no one size fits all answer here, but there are some considerations that can help you to understand what is best for you. I’ll try to go through the considerations and let you decide in the end what suits your business the best.

Is AI strategic for your business?

First of all I believe you should ask yourself: Is AI development a strategic feature to my organization? That can be a bit of a vague question so I’ll boil it down to this: Will AI solutions provide you with a competitive advantage that you will try to protect and keep improving to stay a head?

If the AI is just something that is meant to make an improvement that it’s likely your competition can easily copy then you should definitely buy the solution off-the-shelf or have made from experts you hire in. Building up the needed know how and organizational capabilities to make an AI that is only here for a small tactical advantage is not necessary. That will take your focus away from the more important problems. So ask yourself the hard question: If the business would need to do cutbacks, would you keep investing in building your own AI as a strategic priority? If not, you should consider not to do it in the first place.

On the other hand if you believe that one or more AI-solutions can be a competitive advantage that your competition can not easily copy then you should try to build it in-house. In this case you have to be clear on what makes it hard for your competitors to copy. Do you have some access to data that they don’t? Do you have a better position to build the AI capability or something else? Make very sure that you are actually in a position to be competitive here. If not, your competition will copy you by buying from an experienced vendor at a lower cost than you paid to build your own AI.

Research the market

You will be surprised how many off-the-shelf AI solutions there are out there that solve all kinds of problems. People tend to in my experience not do the research and end up making expensive investments that take forever to get done and still it won’t compare to the products already on the market. You really have to have scale to make a business case for building your own solution when there’s already a lot available out there.

I actually once met someone building a solution in-house that was exactly what my AI company was doing. We needed massive scale to get anywhere near a good business case and yet these guys tried to do it themself. We had more than 14.000 business customers at the time and this one business wanted to make the same AI for their business only. They of course had to close their project since it was too big an investment but they still spent a lot of money. Once a project has been kicked off it can be hard to pull back since a lot of ego and prestige can go into corporate projects.

In for a penny in for a pound (of AI)

I have a rule of thumb that never fails me. “When an organization does something it doesn’t do regularly it will execute it poorly”. I made this rule of thumb to explain to myself why very competent organizations sometimes completely flops relatively simple endeavours. I guess the reason is that working in a new domain for an organization is not only not supported by the current processes and culture but might require the organization to work against them. Whatever the reason I see it consistently and I also see it being the case with AI. If you don’t do AI projects regularly you will see massive overhead and probably fail it. So if the frequency of your AI projects are low you should probably look to outsource as much as possible. This is not an attempt to scare anyone away from AI projects, but it takes effort to build the AI capability and that’s a conscious choice you have to make here.

Size matters

AI projects require a minimum investment that is usually larger than traditional IT projects. In AI the skills from engineers, machine learning developers, data scientists and product managers are quite unique. So as a result your organization just has to be a certain size for in-house AI projects to make sense. AI usually also is a trial and error workflow that doesn't promise revenue or profit right away.

There’s no fixed amount of employees or revenue but when the AI team has to be 4-5 people at minimum then you probably shouldn’t do it before you can handle a team of that size for a while not providing any revenue or cost saving for a good while.

Get your data straight

Data is a big part of many AI projects and I always recommend that you get your data straight before you go into the actual AI development. In my mind it’s more important(And more competitive) to get a smooth data operation with low costs and high quality data. I would always prefer to get the data operations in-house and the AI-development is second priority. Getting the data operations right is more of a competitive advantage than building the AI. It’s like supermarket chains competing - The chain with best purchasing of goods and more low cost warehouse operations can provide cheaper consumer prices and are more competitive. Data is the same way. If you can get better data at a better quality and a lower cost, your AI projects will be superior to your competitors even if their AI capabilities are superior to your businesses. So make data the priority if you have to choose.

Building AI is getting easier

One last thing I think you should take into account is that AI projects are getting easier and the barrier to get started is getting lower. AI used to be a very difficult domain to work in, requiring both Phds in data science, machine learning engineers and often thousands of hours of coding to make a useful AI. Today a lot of that can be done at a much lower buy-in with techniques such as Transfer learning and AutoML. It also seems that the bar for getting started is getting lower and lower. As a result building AI in-house is clearly becoming more accessible and with time more business should have a go at it.

That’s it. From here, the decision is yours.

You might not be aware or do this unconsciously but, if you work with AI you also work in the decision science space. 

Imagine this: You have made an AI model that can take in support tickets and classify them into different subjects and sentiments. With that you can prioritize support tickets by how critical they are and have them directed to the appropriate support team. Sounds great right? But is it really that simple? No. With the AI model in place we are really only halfway to the finish line. If you decided to make an AI like the one I just described you must have had the goal of optimizing the support ticket workflow. Either for happier customers or to lower costs or maybe some other business objective. Either way, the way we choose to act on the data we get as a result of the AI is equally important to the actual AI, if not more. When we take a stand on how to act on the data we get we actually make a decision model. The science that goes into these models are not as simple as it might sound. Look at this example:

The support ticket AI suggests that with 60% likelihood a new ticket is about termination, 30% about a new feature and scores medium critical on the sentiment analysis. Now it doesn’t seem so easy anymore does it? How do we handle this information? Who should get this ticket? And isn’t a termination critical no matter the sentiment score?  

I’m in no way a decision scientist and cannot teach anyone much here. But what I can tell you for certain is that the decision models on top of AI are way too often left to be a secondary priority with no conscience or strategic approach. And even worse - The decision model is only discussed after we are finished with the AI models. I would argue that it is in the making of the decision model that we actually get to understand what data we really need, so making the AI first rarely makes sense since we don’t know what we actually need. 

There’s also a lot of traps to be aware of in decision making such as survival bias(Thinking you made the right decision because you got the right result) and many of us think we are better decision makers than we really are.

If you want to learn more about decision science my best advice is to follow the Chief Decision Scientist at Google Cassie Kozyrkov. She really succeeds at taking decision science to an understandable level. 

So to sum up. If we want to have better results with our AI solutions we should pay more attention to decision making and in many cases start with that before we go modelling. 

I recently surveyed danish CIO’s(Chief information officers) about their relationship with AI and I had some interesting results. One of the results was that one of the biggest barriers to get started on AI projects is that building the business case is difficult. I completely understand the issue and I agree with the CIO’s. Building an AI business case is difficult and if you try to build it as a traditionnel IT business case it’s down right impossible. 

Building a business case is all about understanding the cost and revenue drivers well enough to work them into a model that yields a profit with high certainty within an agreed timeline. When building AI solutions or even buying them off-the-shelf that whole process turns out to be way more challenging than what you will experience with traditional IT-projects. In my experience this is for many a lesson hard-learned by many in the IT business that naturally grabs their well-known tools and methods but quickly fails. This often results in AI being disregarded as being a too immature technology. With the right approach, that I’m going to show you here, you can actually build a business case that makes sense. The technology is ready and at a stage where most businesses can successfully utilize it. New technology just requires new approaches.

Before moving on to how you build an AI business case, let’s understand why this is such a difficult task. The reason is simply, that everything in AI is experimental in its natural form and as a result nothing is predictable. How much data you need, what algorithmic approach will work and how good the result will be is very difficult to know beforehand. You can look at a similar project but small differences in the problem, the data or the environment will often to much surprise make a big difference. So knowing the exact costs, results and the road there is just not possible.

The cost side

What will it cost to an AI? You just can’t know. In traditional IT we try to break down the project into smaller and smaller pieces until each piece is such a size that we can easily estimate the time and costs that go into it. In AI the process is experimental and we can’t even know the pieces in advance. 

To combat this problem there is a set of strategies that will make it a lot easier to control the cost side. On purpose I’m writing, controlling and not predicting the costs. In the AI paradigm, predicting costs should not be the goal. The goal should instead be to control it. I’ll get back to why that makes sense a little later.

The cost control strategies are the following:

Iterations

For years now we have been talking about the agile approaches in IT. Some have used it with success others have steered clear and stayed with traditional methods and some unfortunately have used it as an excuse not to have a plan at all. The agile approach suggests iterating through projects several times to account for new learnings during the project and changes in demand. Similarly AI projects should use an iterative approach to get a set of important learnings. Just by doing one very quick simply iteration you should get these learnings:

1. You understand the data better. You understand how much effort it requires to attain, how to attain it and get a sense of how much you will need. 

2. You can get a sense of how the users react to a certain quality and how difficult it will be to deploy

3. You get a good idea of potentially attainably quality.

Last point here should be seen as a stop-test. If you don’t see a close to acceptable quality in the very first iteration it’s very unlikely that you will see much better results in the near future with either attaining much much more data or putting a significant larger investment into the algorithm work. So many AI projects should be abandoned if the first iteration is not close to a useful solution. In some cases though this can be just a wrong algorithmic approach. This is where you have to rely on the tech people's judgement.

For the first iteration you can in my opinion start very small by using AutoML solutions. AutoML is AI without coding that can be trained and deployed within hours just needing only data. There are pros and cons here to be aware of. I wrote another blogpost about this here.

Milestone funding 

I preach a lot about milestone funding in AI. This is a very effective strategy to control costs. In AI the milestones are natural and project funding should only be released for each milestone once a set of agreed criteria is successfully met. The milestone naturally would look like this:

Collect data

First step is to collect a certain amount of data at a certain quality at a certain cost. Collecting, cleaning and preparing data is almost always the most underestimated cost in relation to AI projects so making these first steps success criteria very specific is not a bad idea at all. 

An important aspect of data collection is the frequency you need in updating the data. Some projects require only initial or rare data collection and for others it’s required to build an entire data operation that in itself should be a good business case. Many projects die from costly data operations so take it into account early. The trick here is to measure a lot in the process.

Building models

Next step is building models. This is in the business case not that complicated. This is where tech people have to estimate but they will do so with great uncertainty and that is just how it is. As mentioned before the first iteration should be as quick as possible and if you don’t see potential for good results after this, you should be willing to stop the project or change technical strategies.

Deploying 

You should also try to deploy the AI models into a test or staging environment already in the early iterations. It might seem like overcomplicating the problem, but AI models are just more clumsy to work with in my experience than other code bases. The amounts of data also makes the dev ops challenge a bit more interesting.

Studies have also shown that up to 99% of the code in AI projects are all the “glue code” around the actual AI that makes it work in the given environment. So getting a sense of this early is also a good idea.

For deploying the model there is also very often a human aspect that should be a part of the criteria for success. People respond differently to AI solutions than other solutions since AI is harder to understand as a layman. 

Bundle your AI projects

My last advice for controlling costs in AI projects is to bundle more projects in one business case. As you can see, the risk of an AI project early on turns out to be too costly or not good enough, is there. There’s a tendency in IT to keep working on projects that have already shown signs of being a failure since we as people overestimate our abilities to improve the situation and we just want to deliver something. If we deliver nothing we feel as complete failures. 

To avoid this, put more projects in one business case so you can let the bad one die and the good once flourish. You might argue that people should just be better at calling it quit when failure is inevitable but to me changing the structures and letting people be people is a much superior strategy.

AI business culture

Before moving on to the revenue side I wanted to add some notes on AI and company culture. As I mentioned the possible is that some AI projects should be shut down early. It can look like a failure but with the right culutre this can be seen as a successful null-result. Collecting a ceratin amount of null-results is very valuable to a business, especially if done at a low cost. By knowing for sure what does not work a business can much easier navigate and plan ahead. The only problem is that null-results are not always cultural acceptable. Untill it is a lot of the AI business case strategies to control cost won’t be very easy to implement. So management has a very important responsibility to make sure that the company culture supports these approaches.

The same goes for the cost control. If there’s not a culture for controlling cost instead of predicting AI will hardly be good experience. AI ironacly doesn’t offer predicability. So a culture that instead supports budget or time boxing is much more effecient for AI.

The revenue side

When someone asks me if a certain problem can be solved with AI I answer “probably yes” since people are usually on the right track. The natural followup question it “how good will the AI be then?”. The right answer here is “I don’t know”. This is to many a hard to shallow pill. The people that demand answer here will rarely succeed with AI. Those that can work around that lack of information will be much more likely to successeed so naturally that should also go for the business case.

If the revenue, value or profit is based on the quality then you can’t calculate the expected profit since you can’t know the results. Even if the AI is sold at a predetermined prices it’s hard to predict since adoption among users is often based on quality. 

Who are you trying to beat? 

Besides utilizing the very quick interations to get an idea about the quality to expect you should also be clear on what to expect from your AI. Don’t try to make a business case for a perfect AI. Make one for a good enough AI that solves the problem at hand. Very often new technology is held to golden standards and the expected results will be out of this world. Be very specific here in your communication to avoid this. I also wrote about it here.

Presenting your business case

Now that you know that you can’t build a business case on AI projects as you would classical IT projects you all set right? Not quite. The last challenge is when you have to present the business case. Your peers that might need to review or accept the business case usually expect classical IT paradigm business cases. So my last piece of advice here is simple - Start by presenting the core principles of AI and how that makes the business case different. If you get a buying on your new approach everything will  be much more smooth.