A Simple AI Strategy

Artificial Intelligence (or AI) has meant different things at different times, all through my career. I started working in AI back in the 1990s, when the most prominent use of a neural network was to decode hand-written post code (zip code) digits on letters, and if an organisation was using AI, they had probably implemented an expert system.

This was during an AI winter, when the hype of AI had overtaken expectations, and calling something AI was not considered a positive. Things like the discipline of data science and the technology of speech recognition emerged from this period without being explicitly labelled as AI, and organisations stopped talking about “using AI”.

I worked on implementing intelligent, autonomous agents, and then speech recognition-based personal assistant services. Think of a rudimentary Siri that could arrange meetings by calling people up and asking simple questions about their availability. I also developed a speech-based recommender system that would match people to local restaurants. It didn’t end up going anywhere though.

But AI itself came back in a big way, and organisations started talking about “using AI” when deep learning burst onto the scene in the 2010s. This use of multi-layer neural networks, trained on huge amounts of data with readily-available GPUs, was able to produce results that met or exceeded the results of humans. Seemingly overnight, AI had been redefined to mean deep learning, and all of the data scientists had to wearily explain why their statistical methods should be considered AI too.

My teams used this new AI for a range of novel applications, including training smart cameras on drones to find people lost in the wilderness, detecting when car doors were being opened in front of cyclists, and counting the number of desks in an office that were in use during the day. Additionally, we explored the ethical implications of these new AI capabilities and how an organisation can use them responsibly.

Now it seems AI has been redefined all over again, and generative AI is what people mean when I talk to them about AI. Which is a lot at the moment. Almost every professional conversation seems to turn to AI at some point. It’s a very exciting time, and there seem to be revolutionary announcements every month concerning generative AI.

Of course, this hasn’t escaped the notice of Boards and CEOs, who are asking their people to come up with an AI strategy for their organisations. Key suppliers are also putting pressure on these organisations to adopt their AI-enabled products and services, often with additional fees involved, and no CEO wants to fall behind competitors who are presumably “using AI” in everything.

It reminds me of the quip about teenagers and sex – and there are similar incentives here to talk about doing it, even if you’re not sure about it, and in fact aren’t doing it at all.

Actually, most organisations don’t need to get too worked up about it. It will be an evolutionary technology adoption for them rather than a revolutionary one, assuming they are already on the AI journey (AI meaning data science and deep learning).

This post is an outline of what a simple AI strategy can be for many organisations. Essentially, if an organisation is (i) not building software itself that appears in the user interface of its products and services, and (ii) has already adopted best practices for the previous generation of AI, it can likely keep things simple.

What’s new?

Generative AI can be considered an application of deep learning where new content is created, specifically audio, imagery or text that is similar to what a human would create. The recent AI boom has been brought about through a technology called a transformer architecture – the T in GPT stands for Transformer. Even before the excitement around OpenAI’s DALL-E 2 or ChatGPT services, you may have unknowingly used this technology in Google’s Translate service or Grammarly’s authoring tool.

While previous AI technology has been used in enterprises in decision-making tools, Gen AI has obvious application in creative tools. In a real way, the latest form of AI simply brings human-level capable AI-enabled features to a new set of enterprise tools. The insight is that you can treat this latest AI revolution as an update in enterprise tools. It may even be less disruptive than the time when enterprise tools moved to the cloud to be provided under SaaS (Software as a Service) arrangements.

When I say creative tools and decision-making tools, here’s what I mean:

  • Creative tools are not just tools used by “creatives” but any tool used by people to create something new for the organisation. They include software development tools, word processing tools, graphical design tools and inter-personal messaging tools.
  • Decision-making tools are any tool that provides data and insights that aid in making a business decision, such as to find the correct policy document, highlight the best applicants for a role, or report on monthly financial figures. The enterprise document repository, timesheeting system, or monthly dashboard are decision-making tools.

There are also some tools that are a mix of these two, for example Microsoft Excel allows people to create new financial models for their organisation that aid in making business decisions. That said, this hybrid category can be practically treated as a subset of decision-making tools.

In this discussion, I am assuming that the organisation in question has already done the usual things for the previous generation of AI. For example,

  • evolved the data warehouse into a data lake that is able to store both structured and unstructured data ingested from operational and customer-facing platforms,
  • established data governance processes and data management/ownership policies consistent with a relevant responsible AI framework (e.g. the Australian government ethical AI framework), and
  • provided training around privacy, data sovereignty, and cyber security practices to people who handle business and customer data, or develop and test applications using it.

It is likely that the responsibility for doing all those things was with a part of the organisation that also had responsibility for the decision-making tools used in the enterprise, namely the IT team. Understandably, the IT team is probably where the Board and CEO are looking to get the AI strategy from.

Before we continue, let’s be clear about what AI will bring to creative tools. The following table provides examples of AI-enabled features used in different types of enterprise tools:

Type of toolExample AI-enabled feature
Decision-making toolForecasting
Anomaly detection
Creative toolSummarisation

What a particular feature does in a particular tool will be very tool-dependent. For example, in Adobe Suite, a composition feature might in-fill a region of an image to seamlessly replace the part that has been removed, while in Microsoft Powerpoint, a composition feature might provide initial layout of text and images on a slide. However, the high-level user experience is the same in both cases: the user provides a text prompt and receives new content in response.

Some decision-making tools are gaining a creative layer on top of their existing AI-enabled features, such as summarisation being added to search tools to save the user having to click on results, or language translation being added to recommendations to supported a wider user base. However, existing AI policies and procedures that have focused on decision-making tools will have likely picked-up these cases, and those tools that are a hybrid of decision-making and creative tools are well.

So what?

Organisations that produce creative tools will already have had to include Gen AI features in their products, driven by the customer/market demand for these and competitive pressures. These organisations will have had to skill-up in Gen AI already and have a good handle on the technologies and issues. This post is not for them.

Additionally, organisations that develop customer-facing software outside of creative tools will be considering how and whether AI-enhanced features like summarisation and translation could be incorporated in their user interfaces. The speed of innovation in this area is daunting. A year ago Meta’s foundation Gen AI model called Llama was leaked, initiating widespread development of such models in the research and startup communities, and now alternative models are beating OpenAI’s own models on public leaderboards (see here or here). There also also many complex factors to be considered. At the very least, such organisations should be performing upskilling in this area for their people and have a Gen AI sandpit environment for experiments. Given the speed of change in the marketplace, most organisations will need extremely quick ROI on any Gen AI projects or risk a waste of their investments. Due to all of that, this post is not for these organisations either.

If an organisation doesn’t build software that appears in the user-interface of its products and services, and given that Gen AI created text, imagery or audio will appear in user-interfaces, such organisations will be consumers of Gen AI rather than producers of it. I contend that the most common way for such organisations to consume Gen AI will be via tools that embed Gen AI, and hence avoid the costs and risks of building their own custom tools. Hence Gen AI technology adoption becomes a question of tool adoption and migration, and if an organisation has already tackled the question of AI before, it will have covered decision-making tools, leaving only creative tools to be dealt with in its plans.

Focusing on AI-enabled creative tools, these will have a number of common issues that an organisation will need to consider as part of adopting them:

  1. Copyright. New content is covered by copyright laws, which are similar around the world, but are not identical, and AI tends to play in the parts that are not globally consistent or well-defined, such as the concept of “fair use“. The data that has been used to train Gen AI models might turn out to have legal issues in some countries, impacting the use or cost of a model. The output of a Gen AI model may not be copyrightable, and hence others will be able to copy it without breaching copyright. This may limit how such AI models are able to be used in an organisation.
  2. New users. While the IT team has had its arm around the enterprise data warehouse and data lake, when it comes to creative tools, the IT team may not have been so involved, and adopted more of a light touch approach. The users of creative tools may not have received the previous round of data training, and may not be enrolled in data access systems intended to comply with data sovereignty controls, etc. From the point of view of AI, a Word document or corporate video is just as much “data” as the feed from the CRM.
  3. Data leakage. The latest Gen AI features in creative tools currently do not typically run on the desktop or on a smartphone, but are a SaaS feature that involves sending content to the cloud, and possibly off-shore. This is in many ways a standard SaaS issue rather than something new, but the nature of AI models is that they improve through training on data, so many tool providers seek to use what might be confidential content in the training of their models in order to continue to stay competitive. For example, Zoom modified their terms of service so that if a meeting host opts-in, the other participants in a meeting may have their meeting summary data used for training. Organisations are having to implement measures to manage this risk, such as Samsung choosing to restrict the use of ChatGPT after employees leaked confidential data to the tool last year.
  4. Misrepresentation. AI-enhanced creative tools might be used to produce content that others mistakenly think was produced by people or was otherwise authentic content. In the worst case, “deepfakes” may be created of an organisation’s public figures in order to dupe investors, customers or employees into bad actions. Scammers used this technique to trick a Hong Kong employee into transferring HK$200M. Still, a simpler case is where a chatbot on the Air Canada website made a mistake in summarising a company policy, a customer relied on this, and Air Canada was liable. Some organisations are taking care to carefully distinguish AI content from human-created content to help limit risks here.

Despite these issues, there is some optimism that AI-enhanced creative tools will bring a productivity boost to their users. The finger-in-the-air number is typically something like a 20% improvement. Microsoft’s recent New Future of Work Report (always very interesting!) includes some findings that Microsoft hopes will lead to uptake of their new AI-enhanced tools called Copilot:

  • Copilot reduces the effort required. Effects on quality are mostly neutral.
  • New or low-skilled workers benefit the most.
  • As people get better at communicating with [AI tools], they are getting better results.

The Wall Street Journal covered some scepticism about the benefits of AI, highlighting that errors in AI output take additional effort to catch and correct, and there was a 20% drop in usage of some AI tools after the initial month of enthusiasm. This indicates that early adopters need to go into this with their eyes open.

Now what?

For organisations not building software that surfaces in the user interface of its products and services, the main impact of Gen AI will be on how and when to migrate to AI-enabled creative tools that their employees will use. Since the previous AI boom will have resulted in foundational AI procedures and governance in the organisation that can be reused for Gen AI, a simple AI strategy is to treat this shift to a new toolset as a change management exercise.

Further, instead of treating the migration of each tool as a separate exercise, it is worth managing this in a single program. There is a lot that will be common around managing the issues and conducting the training, so it will be more efficient to do it together.

An organisation will typically have a standard or preferred change management approach or blueprint for implementing technology change. This can be re-used for driving the migration to AI-enabled creative tools. No need to reinvent the wheel. (As an example, see the Bonus Content below for how the Kotter 8-step process might be tailored for this.) Note that the existing data governance processes will need to be leveraged in this process exercise. Additionally, the IT team will be fundamental in driving good use of Gen AI adoption.

In tackling the issues mentioned above, here are some questions to help work through the right path:

  1. Copyright. Which legal jurisdictions does the organisation and its creative tool suppliers operate in, and how do copyright laws vary (particularly the concept of “fair use”)? How important is having copyright over the output of creative tools, and are there other IP protection measures (e.g. trademarks) that mitigate any risks?
  2. New users. What degree is an organisation’s creative work done within the organisation, or done using external agencies/firms? How well do the legal agreements covering this work (whether employment or agency agreements) anticipate the issues of Gen AI? Is there consistency between how creative tools and decision-making tools are treated and managed in the organisation?
  3. Data leakage. Do people in the organisation understand how prompts and images given to Gen AI tools can leak out? What regulatory data compliance rules apply to data shared with or generated by these tools? How well do either “fine tuning” or “RAG” approaches to AI model customisation sit within an organisation’s risk appetite?
  4. Misrepresentation. How well do the official communications channels used by the organisation provide authentication? Are human and AI generated watermarking standards in use, e.g. Adobe Content Credentials or IPTC Photo Metadata standards? To what extent are misrepresentations of people at the organisation tracked and detected on social media? Which Gen AI web-scraping tools are blocked from ingesting the organisation’s public content?

You don’t need to over-bake it. For many organisations, the adoption of Gen AI will be through its enterprise tools, so it can be treated like a migration exercise. Just keep it simple.

(Thanks to Sami Makelainen, who provided comments on an earlier version of this post.)

Bonus content – Kotter 8-step process example

Here’s an example of how you might include activities within the Kotter 8-step change management process to help an organisation migrate to AI-enabled creative tools:

  1. Create a sense of urgency. Identify how the use of Gen AI tools links to the organisational strategy (improve staff experience, greater productivity, etc.) and an answer to “why now” (CEO directive, culture of leadership, existing strategic program, etc.).
  2. Build a guiding coalition. Ensure senior stakeholders have bought in to this rationale, with a single influential stakeholder willing to represent the activity. Ensure parts of the organisation outside of IT are represented, such as vendor management, legal, and parts of the organisation that use creative tools, e.g. anyone with “manager” in their title. Ensure the working group is suitable trained about Generative AI technology and its emerging issues, such as those outlined above.
  3. Form a strategic vision. With the stakeholder group, develop a view of how the organisation will be different once it has migrated to new AI-enabled tools, e.g. include use cases. This should be tangible and time-bounded, so should ideally be informed by previous tool migration exercises.
  4. Enlist a volunteer army. Leverage internal organisational communications tools to promote the vision, build a cross-organisation community of supporters. People are generally pretty excited about this new application of AI. The stakeholders and community can together help expand the community so it is truly cross-organisational. Task them to identify the creative tools that are used across the organisation (including “free” tools), which ones already have AI-enabled features, what types of data are consumed and generated by these tools, which suppliers provide them, and where the data is processed. Identify simple metrics that would highlight if the features of these tools successfully bring the expected organisational benefits.
  5. Enable action by removing barriers. Ensure the community gets training about the issues relating to AI-enabled creative tools. Leverage the community to consider the risks of different uses of these tools in their different parts of the organisation, determine what constraints should be applied around the use of these tools, e.g. when can confidential information be shared with the tool. If the constraints are onerous, identify if alternative tools exist that could have fewer constraints.
  6. Generate short-term wins. Focus on one or two tools, prioritising those with the most benefit and easiest to migrate. It may be that it is easiest to start with something like GitHub Copilot and some software engineering teams, or maybe it will be easiest to use something like Microsoft 365 Copilot and some people with “manager” in their titles. Gain agreement to migrate these initial tools and learn from them. Ensure the users of these tools are trained to use the tools under the constraints, and specifically on writing good prompts. People who are already using AI-enhanced tools in the community may be a good source of training information.
  7. Sustain acceleration. Track the metrics to see where the migration to AI-enhanced tools has brought the expected benefit. Use the learnings to build a business case for migrating more tools and leverage the stakeholders to drive the wider adoption of AI-enabled creative tools.
  8. Institute change. Not everything will have gone smoothly. Update policies and procurement practices to accommodate learnings. Provide organisation-wide training on Generative AI technology, and use the community, stakeholders and metric data to bring the rest of the organisation up to speed on the new tools.

Lessons from NYT on innovation

The Kindle New York TimesWhatever the circumstances that led someone at The New York Times to leak their report on Innovation, I am thankful. Published (internally) in March, it is the fruits of a six month long deep-dive into the business of journalism within a company that has been a leader in that industry for over a century, and provides an intimate and honest study into how an incumbent can be disrupted. It is 97 pages long, and worth reading for anyone who is interested in innovation or the future of media.

The report was leaked in full in May, and I’ve been reading bits of it in my spare time. Just recently I completed it, and felt it was worth summarising some of the lessons that are highlighted by the people at the Times. As it is with such things, my summary is going to be subjective and – by nature – highly selective, so if this piques your interest, I encourage you to read the whole thing.

(My summary ended up being longer than I’d originally intended, so apologies in advance.)

Organisational Division

Because of the principle of editorial independence, the Times has clear boundaries between the journalists in the newsroom and those who operate “the business” part of the newspaper, which has been traditionally about selling advertising. This separation is even known as “church and state” within the organisation, and affects everything from who is allowed to meet with whom (even during brown-bag lunch style meetings) to the language used to communicate concepts. This has worked well in the past, allowing the journalism to be kept at the highest quality, without fear of being compromised by commercial considerations.

However, the part of the organisation that has been developing new software tools and reader applications is within “the business” (not being journalists), and has hence been disconnected from the newsroom. Hence new software is not developed to support the changing style of journalism, and where it is, it is done as one-off projects. Other media organisations are utilising developers more strategically, resulting in better tools for the journalists and a better experience for the readers.

Lesson: Technology capability needs to be at the heart of an innovation organisation, rather than kept at arms-length.

Changing Customers

For a very long time, the main customer of the Times has been advertisers. However, print media is facing a future where advertisers will not pay enough to keep the organisation running. Online advertising pays less than print advertising, and mobile advertising even less again. Coupled with declining circulation due to increased digital readership, the advertising business looks pretty sick. But there’s a new type of customer for the digital editions that is growing in importance: the reader.

While advertising revenues had the potential to severely compromise journalism, it’s not so clear that the same threat exists from reader revenues. In theory there is a good alignment: high quality journalism results in more readers. But if consideration of attracting readers is explicitly kept away from the newsroom as part of the “church and state” division, readers may end up being attracted by other media organisations. In fact, this is what is happening at the Times, with declines in most online reader metrics, and none increasing.

In the print world, it was enough to produce a high quality newspaper and it would attract readers. However, in the digital world this strategy is not currently working. Digital readers don’t select a publication and then read the stories in it, they discover individual articles from a variety of sources and then select whether to read them or not. The authors of articles need to take a bigger role in ensuring those articles are discovered.

Lesson: When customers radically change, the business needs to radically change too (many true-isms may be true no longer).


The rules for success in digital are different from those of traditional print journalism, although no-one really knows what they are yet. That said, the Times newsroom has an ingrained dislike of risk-taking. Again this made sense for a newsroom that didn’t want to print an incorrect story, and so everything had to be checked before it went public. However, this culture inhibits innovation if applied outside of the news itself.

Not only does it a culture of avoiding risks prevent them from experimenting and slow the ability to launch new things, but smart people within the organisation risk getting good at the wrong things. A great quote from the report: “When it takes 20 months to build one thing, your skill set becomes less about innovation and more about navigating bureaucracy.”

Also, the newsroom lacks a dedicated strategy and operations team, so doesn’t know how well readers are responding to experiments, or what is working well for competitors. Given that competitors are no longer only other daily newspapers, it’s not enough to just read the morning’s papers to get insight into the competition. BuzzFeed reformatted stories from the Times and managed to get greater reader numbers than the Times was able to for the same stories.

Lesson: If experimentation is being avoided due to risk, then business risks are not being managed effectively.

Acquiring Talent

It turns out that people experienced in traditional journalism don’t automatically have all the skills to meet the requirements of digital readers. However, the Times has a bias for hiring and promoting people in digital roles based on their achievements as journalists. While this likely worked in the past to create a high quality newspaper, it isn’t working in digital. In general, the New York Times appears to be a print newspaper first, and a digital business second. The daily tempo of article submission and review is oriented around a daily publication to be read in the mornings, rather than supporting the release of stories digitally when they are ready to be published. Performance metrics are still oriented around the number of front page stories published – a measure declining in importance as digital readers cease to discover articles via the home page.

The lack of appreciation for the digital world and digital people in general has resulted in the departure of a number of skilled employees, according to the report. Hiring digital talent is also difficult to justify to management given that demand has pushed salaries higher for skilled people even if those people are relatively young. What could be a virtuous circle, with talent attracting talent, is working in the opposite direction with what appears to be a cultural bias against the very talent that would help the Times.

Lesson: An organisation pays for the talent either by paying market rates for capable people or paying the cost in lost opportunities.

Final words

When I first came across the NYT Innovation report, I expected to read about another example of the innovators’ dilemma, where rational business decisions kept them from moving into a new market. Instead, the report is the tale of how the organisation structure, culture and processes that made The New York Times great in the past are actively inhibiting its success in the present. Some of these seem to have become sacred cows and it is difficult for the organisation to get rid of them. It will require courage – and a dedication to innovation – to change the organisation into one that is able to compete effectively.

Book Review – Good to Great

I’d been aware of this book for a while, but it still seems to be available only in expensive hardback format, so I was waiting until it got cheaper. Recently I found it for $15 (still hardback) and this was enough for me to give it a go.

Good to Great

Research-based guidance for established companies to excel in their markets.

I came to this book by Jim Collins with some interest in reading about a new research-based attempt to find a winning corporate formula, but also scepticism due to the unsuccessful attempts that have come before. Perhaps the most infamous was In Search of Excellence which purported to find the recipe for excellence, but gave Atari (had to sell key assets in 1984) and Wang Labs (filed for bankruptcy in 1992) as examples of excellent corporations. Although, that book identified 43 “excellent” companies, so it’s probably not too bad for only a couple of bad apples to end up in their list.

Collins improves his odds by identifying only 11 “good to great” companies. But this is perhaps an uncharitable comparison, as his team appears to have done an extensive job in analysing these companies, and there are only 11 because only 11 companies out of the 1,435 US-based “Fortune 500” companies from 1965-1995 met their criteria. Then to identify the features that relate to being “good to great”, these had to be possessed by all “good to great” companies and lacked by all 17 close-but-not-quite-good-to-great companies also identified by the team.

The book explains the basis for these features, and is engaging and well-written. For me, the most surprising was the feature of “first who.. then what” which is basically the idea that hiring well becomes foundation for all corporate strategy, and not, say an analysis of competitors, technology, financials, or other market fundamentals. I do like this idea, despite its fuzziness, as it says that people aren’t fungible and that they can make a big difference. There are five other features, making six in all, but none were as counter-intuitive as this one. In any case, I will now be paying attention to these features in my workplace and future employers.

However, I can’t bring myself to adopt them as fundamental tenets since despite the rigorous research, the conclusions remain essentially unproven. From my point of view, there are three weaknesses in the research: the set of “good to great” companies is arbitrary, the set is small, and the conclusions are untested.

Taking the first problem, “good to great” companies were defined as having a transition to “great” performance of at least three times the general market (from a point of transition). If, instead of three times, it had been five times or even two times, a different set of companies would’ve been found. Since the features needed to be possessed by all “good to great” companies, a different set would’ve produced a different set of features, e.g. potentially larger or smaller. Hence, perhaps the features found are sufficient for a good-to-great transition but some weren’t actually necessary.

The problem of a small sample is tackled in the book, referencing “two leading professors” who think the sample of 11 companies wasn’t small. Unfortunately, this is not convincing. For example, one professor says that the 11 companies wasn’t a sample as it was 100% of companies that met the criteria – although I would respond that the book promises that these principles are universal, so there will be more such companies in the US-market in the future, and they should also apply to non-US-based companies, hence the 11 companies don’t represent 100% of all possible “good to great” companies.

Lastly, the conclusions are untested. The research team could’ve, say, looked for a couple of companies outside the US that met their “good to great” criteria and then checked that those companies possessed all of the six features. Except they didn’t. The only companies examined as part of the study were those that informed the conclusion. The use of comparison companies gives me a level of faith in the conclusions, but these can’t be validly re-used in testing that conclusion. So, really the conclusion remains a hypothesis for now.

My grumblings notwithstanding, I was impressed with the analysis in the book and the methodology that used comparison companies to filter out features that were shared by both the “good to great” companies and also those that didn’t perform so well. It has shifted my thinking about what a successful business can look like.

Rating by andrew: 3.5 stars

A rort by any other name

Collection of modern safety razors: Gillette F...
Image via Wikipedia

I seem to have lost my collection of shaving instruments in the recent move. I’m not sure where they went. Perhaps wherever all the biros, sunglasses and good TV shows have all disappeared off to.

When it came to replacing my razors I was amazed at the prices. There’s something known as the “razor and blades” strategy and I think there’s a lot of evidence that it needs a new name.

The basic idea is that the razor is sold cheap, i.e. at a loss, but the single loss is more than made up for by the series of blade purchases over the life of the razor. Of course, blades are designed to work with only one type of razor, so if you switched, you’d need to buy a new razor.

The concept is well known in business, and a number of other industries have apparently copied the strategy pioneered by the razor. For example, there was Polaroid cameras and their film cartridges, games consoles and their games cartridges (now discs) and Printers with their ink cartridges.

However, what amazed me about the recent prices was that the price of the razor was massive compared to the price of the blades. That’s not how it’s meant to work in the razor and blades model.

According to figures gleaned this very evening from Coles Online, razors and blades don’t appear to be following the razor and blades strategy…


  • Schick Quattro Freestyle Kit – $16.34
  • Schick Quattro Razor Kit Titanium – $14.16
  • Gillette Fusion Razor Kit – $13.72
  • Gillette Fusion Phenom Razor – $13.72
  • Gillette Mach 3 Razor Kit – $13.07
  • Schick Quattro Razor Kit – $13.07
  • Gillette Sensor Excel Razor Kit – $7.95
  • Schick Xtreme 3 Razor Kit – $7.62
These all typically include 2 blades also. Median price = $13.40


  • Gillette Fusion Razor Catridges 6 pack – $32.37 ($5.40 ea)
  • Schick Quattro Razor Catridges Titanium 4 pack – $17.10 ($4.28 ea)
  • Gillette Mach 3 Razor Cartridges 8 pack – $26.65 ($3.33 ea)
  • Schick Quattro Razor Catridges 8 pack – $25.06 ($3.13 ea)
  • Schick Xtreme 3 Razor Cartridges 4 pack – $11.43 ($2.86 ea)
  • Gilette Sensor Excel Razor Blade Cartridges 10 pack – $27.46 ($2.75 ea)
  • Schick Ultra Plus Razor Cartridges 5 pack – $8.70 ($1.74 ea)
Median price = $3.13


  • Schick Quattro Razor Disposable with Aloe & Vitamin E 3 pack – $9.80 ($3.27 ea)
  • Gillette Mach 3 Disposable Sensitive 5 pack – $16.01 ($3.20 ea)
  • Gillette Sensor 3 Disposable Razor 8 pack – $11.99 ($1.50 ea)
  • Schick Xtreme 3 Razor Disposable Sensitive with Aloe 8 pack – $11.98 ($1.50 ea)
  • Gillette Blue II Plus Sensitive Pivot Head Disposable Razors 16 pack – $14.16 ($0.89 ea)
  • Schick Extra II Razor Disposable Sensitive with Vitamin E 18 pack – $11.98 ($0.67 ea)

Note that the cost of a disposable (razor + blade) is less than the corresponding razor or blade. Even if you accept that the quality of the disposable will be lower than the ordinary razor, it’s hard to believe that the quality of, say, the Gillette Mach 3 Razor Kit ($13.07 with two blades) is four times the quality of the Gillette Mach 3 Disposable ($3.20 with one blade).

Although I know nothing about it, I would guess that Gillette and Schick are making out like bandits selling the razors. As a comparison, Catch of the Day recently sold a pedestal fan for $13.95, and selling something out of plastic with fewer materials, no moving parts, and no electronics for a similar price cannot be making a loss.

So, if the razor and blades strategy is no longer following the razor and blades strategy, what should we be calling it?

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