Process: a series of actions that produce something or that lead to a particular result

Workflow: the sequence of steps involved in moving from the beginning to the end of a working process

In other words, the process is what needs to be done and the workflow is how it gets done. Unfortunately, these terms are often used interchangeably. How often do you hear “how can we streamline this process?” when they are really asking “how can we streamline this workflow?” I hear it very often, and usually streamline is code for automate. You can’t automate a process, but you can automate a workflow. 

Consider a process for weekly meal planning. The goal is to minimize new ingredient purchases and consume food before it expires. My process actions: identify food expiring this week, determine which recipes I will cook, make a list of the ingredients I need to buy in order to cook those recipes, and then I’m off to the grocery store. Simple 3 step process I expect most people follow, either consciously or subconsciously. 

However, there are myriad workflows for this process. Each workflow options has pros and cons, and which you choose depends on knowing your userbase. If you’re like me and love to log things, you might keep your kitchen inventory logged in an app or a spreadsheet. This also means your inventory list is available outside your kitchen if needed for reference outside the house or to be shared with other family members. Many people keep a mental inventory of their kitchen, but memory can be faulty. You might know you have an ingredient but do you know if you have enough for your recipe? You may be left scrambling later to get more flour, or end up with excess because you bought more just in case. 

Here is my framework (or, dare I say process?) for turning a process into a workflow:

1. Document the process and the minimum number of actions required for a successful outcome

2. Identify process dependencies to determine which actions are sequential

3. Get to know your userbase and their collective ways of working

4. Inventory the tools and resources available

5. Create the workflow

Step 3 should not be underestimated in creating an efficient workflow. You may come up with a Ferrari of an automated workflow for your users; but it’s only successful if they use it. A highly automated workflow may be fast and productive but, if the users don’t feel they have enough control of the output, they will report a poor experience and may stop using your workflow. I find most people want automation, but want to be able to “fix” the output if it doesn’t meet their expectations, even if it is technically a valid output. 

Keep in mind that a process may have multiple workflows for different situations. For example, I am writing this article on a flight to LA. My process for getting on a plane is always the same: get the airport, get through security, find the gate, and board. Depending on cost, how much I’m traveling with, how much time I have, and traffic conditions / time of day, I am choosing between hiring a car and taking public transportation. Today I chose public transportation, and ended up walking right onto the plane after my group was called. While I was traveling abroad, I was hiring a car to get me to the airport early so I can enjoy the lounge. In each workflow, I successfully boarded the plane, but one of those workflows left the user (me) with a poor experience. Not building train delays into my getting to the airport workflow was a design flaw. (That’s a fancy way of saying I was running late…)

I encourage you to experiment with your workflows to find what works best for your users. Just don’t lose sight of the fundamental process. 

PS - if I inspired you to manage your kitchen inventory in an app, I built one for myself you can use: pantry.myweeklybread.com

But there’s also huge industry of machine-made or -assisted products people buy every day. Machines make products well at lower cost and higher speed, leading to better affordability for the consumer. Sometimes, the machine-made product might be better quality than the hand-made product. I know the basics of sewing, but I would not trust my hand sewing over a sewing machine. The machine will product a better result - especially if it’s used by a fashion professional and not me.

AI is just a machine. I don’t understand why people expect it to produce work better than artisans. I am not a musician so, using AI to write a song, will create a better result than me trying to compose. I have to admit to really enjoying videos posted on social media of recipe comments set to song by AI. The creator who is posting recipe comments is not a musician, so that song is better than if they composed it themselves, but the song wouldn’t exist if the creator didn’t have a tool. They wouldn’t pay a musician to write a song for recipe comments, but AI is cheap and makes a good-enough product for this use.

If we are so accepting of machine-made goods, why aren’t we accepting of machine-made art? Call it what it is: creative junk food that is cheap and satisfying. Want something authentic to watch or listen to? Maybe something that will challenge you and push you out of your comfort zone? Go discover artists pouring their souls into their work. You get what you pay for. If you’re paying the streamers for access to authentic art, go use it for its full potential. Otherwise, AI can make you something cheap but bland.

Just as we pay bakers for high quality cookies and bread, let’s pay artists for high quality art. Human produced work is a luxury good that deserves to be valued for the expertise and time that goes into its creation.

Here’s the first issue: “create” has many definitions. Merriam-Webster has six:

Transitive Verb

1. to bring into existence: God created the heaven and the earth

2. to invest with a new form, office, or rank: She was created a lieutenant

3. to produce or bring about by a course of action or behavior: Her arrival created a terrible fuss

4. to produce through imaginitive skill: create a painting

Intransitive Verb

1. to make or bring into existence or something new: an artist who is good at imitating but not at creating

2. to set up a scoring opportunity in basketball: create off the dribble

Let’s get a quick refresher on transitive vs. intransitive verbs. A transitive verb requires a direct object to receive the action of the verb, typically answering the question of what or whom. An intransitive verb is not used with a direct object, typically answering the question of where, when, how, or how long.

To put this into practical terms, let’s use Andy Warhol’spainting and prints as an example. Andy Warhol used his hands to create an image of Marilyn Monroe, then used a printing press to create multiple copies of the image his hands created. Warhol “original prints” are now sold for thousands of dollars because, even though the prints are not the image directly created by Warhol’s hands, they are considered to be his creations. If I paint an image that is an exact copy of Andy Warhol’s Marilyn print, I am the creator of a replica that (should) require me to pay royalties to Warhol regardless of whether I used my hands or a machine to generate the replica. I may be the creator in the transitive definition but I did not create the image in the intransitive definition. What about the print my siblings and I had created for my dad 20 years ago that is a photo of us rendered in Andy Warhol’s style?

Creating new work based on past concepts is not new. Isn’t everyone learning from the works that came before and building upon it for their own work? Haven’t all the great masters built upon the work of artists that came before them? Don’t all great musicians get inspired by music that came before? So when AI is generating writing or images or sounds, prompted by a human with an idea, is the AI creating or the human? AI is a tool that human creators use to get their ideas out into the world.

There is an intellectual property question about the line between influence and usage, but it’s not a new problem. Everyone (or, everyone of a certain age…) remembers the infamous interview with Vanilla Ice about sampling “Under Pressure” that they added a note, so it’s different. [Side note: we can debate separately if he was really making a joke that was taken out of context.] We, the people, influence those laws by electing officials that represent our views and lobbying or protesting for our values. AI is vectorizing art and music in a way that we can mathematically measure the differences between works. Let’s use that math in our laws. How similar do two works need to be to considered imitating or sampling and require royalties?

Let’s also be honest about something: artists would not be mad about AI using their work to generate new works IF THEY GOT PAID FOR THE USAGE. How do we create systems that measure which works are used by the AI to generate something new, then pay those artists for the use. Today, artists are agreeing to exploitative terms of use for services they need to survive, especially independent artists. Giving Google the right to use any content uploaded to YouTube in its AI training models is exploitative. Artists are uploading their copyrighted work to YouTube, and then those copyrights are effectively nulled by the terms of use. This is what is meant by people‘s life work being stolen by AI. Not just that it’s being used to train the models, but it’s using copyrighted works without royalties. When music gets sampled, like “Under Pressure” in “Ice Ice Baby”, the music industry found ways to make sure that the original artist got credit. How do we apply similar thinking to AI sampling? And not just for music, but for all creative arts: visual, text, and audio.

So what does it mean to create in the age of AI? Is creating different from generating? And how do we respect copyrights and intellectual property when AI companies are winning lawsuits with “fair use” arguments?

The focus on different points for each headline is an important reminder that LLMs are nondeterministic. That means each time you ask it to complete a task with the same inputs, you will get different results. This is not inherently “good” or “bad”, but it is a very important factor when deciding how to deploy AI. Here’s what Claude has to say about it:

Susan: I’m writing an article on how LLMs are nondeterministic. Can you share an explanation, in 5 sentences or less, about why I get different results when I ask you for the same task more than once?

Claude: When generating text, I predict the next token (roughly a word or word-piece) one at a time by producing a probability distribution over my vocabulary. Rather than always picking the single most likely token, I sample from that distribution — introducing randomness at every step. A parameter called temperature controls how much variation is allowed: higher values flatten the distribution and let less-probable tokens through more often. Because each token choice shifts the context for the next prediction, small differences early in a response compound into meaningfully different outputs by the end. This is largely intentional — it’s what makes language models generative rather than deterministic lookup tables.

Wow, that last sentence is hitting me hard: “it’s what makes language models generative rather than deterministic lookup tables.” It says a lot about what LLMs can do, and cannot do.

Companies I’ve worked with or talked to want reliably reproducible results from their systems. An example from my recent past is building an LLM-based translation system, for which the model needed to follow our brand guidelines per country and language, including a dictionary of terms that must be used. When we first built the system, it was AI alone and, while it followed the brand guidelines overall, we weren’t reliably seeing our dictionary of terms used. As Claude says above, it is not a lookup table. Our solution was to pair the AI translation with Retrieval Augmented Generation (RAG). It allows the AI model to retrieve (i.e., lookup) information from your knowledge base before generating the response. After adding RAG, our AI translations received significantly higher acceptance rates from the translation users.

I recommend focusing AI on non-deterministic tasks, like translations, that do not have or need a “right answer”. If your company has a low-risk profile, turn that temperature parameter down to 0. For all other tasks, let deterministic systems manage the rule-based automation. AI is too expensive to use when you don’t need it, and a waste of resources if it isn’t generating results you can use. I’ll keep using AI for my news summaries, but I’m going to check in on my temperature parameter, and maybe ask for more than the top 5 topics.

In so many corporations today, teams and leaders are pitted against each other for resources and attention. We are competing to be the highest performer, adding more value to the company than our peers. Corporations believe the only way to increase their value is to prioritize the “highest performing“ work(ers) and cutting the lowest performing work(ers). [Note: there is a big difference between under-performance for the role and applying a bell curve to your workforce, which is a topic I will save for another thought post.] This all lands in a tribalistic mentality within the corporation that us versus them culture. It breeds corporate politicians who spend most of their days in meetings, trying to convince leadership why their idea or their teams work is the most important thing to prioritize.

You may find a few quiet people who ask “how do we work as a team instead of competing with each other?”. Instead of spending so much energy competing, we could be working together as one company to win against the market, to fight our external competition. This is the startup mentality so many CEOs try to rebuild as the grow larger, but they do not break down the system that fosters the internal competition. They want people to act as a startup while they are still fighting each other for resources and priority.

At the best performing companies I’ve been at, leaders bring people together by ensuring that everyone in the company knows how their work contribute to the greater court goals. The mechanism was top down OKRs. (If you are unfamiliar with OKRs, I recommend this article.) I have come to the conclusion that top down OKR is the best method because it allows the CEO and their leadership team to align on what the company will work on together, then cascading those goals down through teams to individual contributors. At each cascading step, people decide how they contribute to the overall goals and align with their manager before cascading to the next level. Leaders say they want to empower their people, and this is an excellent approach to empowerment while ensuring everyone is working toward same goals.

Read that definition again. Let it sit with you for a minute. What stands out to you?

For me, the key word in this definition of efficiency is “effective”. Efficiency is not simply a comparison of production with cost, or ROI. In order to be efficient, your operation must be effective. If you have a low-cost, high-volume widget factory but the widgets produced do not work, your widget factory is not efficient because it is not effective.

AI has a great capacity for efficiency, but how do we ensure effectiveness? Expert human judgement.

When evaluating how I apply AI, I consider 2 questions:

1. Do I get value from doing this task myself?

2. Can AI make this task more efficient?

If the answer to question 1 is “Yes”, then stop and keep doing that task yourself. Do no automate away high-value tasks.

If the answer to question 1 is “No”, then move on to question 2. If the answer is “Yes”, go for it! If the answer is “No”, stop. Sometimes the answer is “I don’t know”, for which I suggest deciding how much time you want to invest in testing AI efficiency.

Let me demonstrate with a simple personal example. I am a time blocker. Every morning, I would go through my tasks and add time blocks to my calendar as a plan to get the work done. Do I get value from doing this task myself? Not really, I just need to have a plan in my calendar. Can AI make this task more efficient? I wasn’t sure, so I blocked 2 hours to build a time blocker agent with Claude. The agent pulls my calendar events and tasks, then generates task time blocks for the day between existing events. I, the human expert, decide if that plan is acceptable. Are there days when I decide to change the plan because of information Claude doesn’t have, like my morning mood? Absolutely, but most days AI’s plan is good enough. My time blocker AI agent meets the efficiency bar because it successfully creates a plan for me every day so I spend less time planning my day, and more time doing work.

You might now be thinking “well, that’s nice, Susan, but I’m making decisions for a company at-scale”. I’ve been there too, and the framework holds if you and your colleagues honestly answer the two questions. In my experience, companies right now are skipping the Value question and going right to the AI efficiency question. In my opinion, it is a mistake to automate away your high value “secret sauce”. Do not turn your human experts into AI reviewers. Figure out how to use AI to make all your human experts more efficient. Let them review the low-value tasks, freeing up more time for high-value tasks.

At a large company scale, there will also be a balance between individual efficiency and company efficiency. It is important to ensure the people implementing AI technology and operations understand the system effects. For example, generating more copy in multiple languages with AI may make your growth marketer more efficient, but may create a bottleneck in the Brand and/or Legal teams with approval reviews. Releasing copy that doesn’t meet the Brand and Legal requirements will not be effective. You now have broken widgets.

I am energized by finding ways to use AI for efficiency, but don’t forget to make sure AI production is effective. And please don’t automate away the things that bring you value. AI should be enriching your life, not taking away from it.

Have you loved using a tool at one company that was clunky and didn’t work well, then moved to another using the same tool and loved it?

What do these two things have in common? Both tell a story of missing, meeting, or exceeding expectations. The best experiences happen when expectations are exceeded; the worst experiences when expectations are missed. Do you know what your customers expect, or just what they “need”?

Corporations are always talking about meeting customer needs, but I rarely hear how they’re setting customer expectations. There are signals, like labeling a new feature as Beta, that tell customers to lower their expectations, but often company marketing says some form of “this is the next best thing since sliced bread” or “we have everything you could ever want to [watch/listen/read/whatever]“, setting customer expectations high in order to convince them to join or buy. But is the product meeting or exceeding that expectation? To be honest, probably not; but, still, the marketing team wonders why people churn before the free trial ends.

A challenge for you: next time you ask “what does the customer need?”, pause and change the question to “what does the customer expect?”.