I am attending the IBPMS Expo in Chicago this week, and first keynote was from Jim Sinur, who presented some of the ideas behind his new book (with Jim Odell and Peter Fingar) on distributed intelligent agent oriented BPM. I included some notes from his talk.
Whatever we do, we need to remember CEOs just want Revenue. Our job in BPM is to show CEO how to get higher revenue. At this show, the standard case study is account opening, and that fits well with this idea. Business is in a battle of high revenue per hour worked. In China, they have traditionally felt they have lots of people to work and don’t need to worry about optimum, but even in China that is where the next revolution will come from: optimizing that resource.
It used to be that a business model would be in effect for many years. But now executives need to be on top of it, because there is no such thing as a static business model. Who likes change? Most people hate change, and it is a struggle for everyone. But, how else will you get the edge on the competition?
Processes evaporate in an agent world. Full cycle is observe, orient, decide, and act. (Reference to John Boyd) Processes are only the “act” part. It is going to work like a fighter jet. Every two years someone says “BPM is dead”. Jim doesn’t buy that. There are plenty of healthy BPM vendors. However, BPM is not dead, but they need to be more responsive in real time.
There are many terms used for BPM, and they all mean slightly different things.
- forms driven workflow – for eliminating manual tasks – want to follow existing forms flow, and a focus on data and content capture
- straight thru processing – rule guided machine processing – want automation of repetitive tasks with no human interaction
- content collaboration – shared tasks and content – want to share work and leverage knowledge for speed.
- guided navigation – self-service with guard rails – want work displacement to client and ease of use.
- case management – multi-role/emergent/knowledge intensive – want completion of complex work and emerging situations.
- intelligent processes – context sensitive/agile/poly-analytic – want situational awareness using human skills with technology assists, and near real time responses.
(These map reasonably well to my 7 domains of predictability, maybe I can work with Jim and come up with more detail to map them.)
We need some real world example, that get into messy reality. People and agents will be more interchangeable in the future. If someone is too busy, you might need to give it to an algorithm. When it gets a lot more important, it may be allocated back to a human. These are interchangeable.
People fight about “should I model or should I not model“. In the future you will have models that show what happened. There is nothing wrong with planning by starting with a model. What is wrong is turning that into a theology. It is a starting point, and that is all. Do it, try it, fix it. Ok to start with a model, but not OK to try to make it perfect.
Strong recommendation to get all of these parts from an integrated system. Jim does not recommend composing this by yourself.
If we want to talk about intelligent agents, we need to find a scientific way of measuring intelligence. Jim introduced the Cumulative Process Intelligence Quotient. There are five dimensions: (1) raw intelligence, (20 social intelligence, (3) agility, (4) autonomy, and (5) visualization. Autonomy is the thing that made him think that distributed agents is the right thing.
He presented a couple of use cases:
- a dynamic surgery center where all of the participants, including the people waiting in the lobby, were tagged with sensors that tracked their situation, and allow everyone to keep informed. Given the current situation (how many supplies, how much to do) the system does a forward simulation of what might happen going forward, and assess potential outcomes.
- airport operations in the UK. Start tracking flights when they are 35 minutes away, and do forward simulation of how the terminal will be affected. This is intelligent process. Increased predictability of handling passenger flow.
- a farming operation that uses sensor to determine how much moisture there are in the different parts of the field, and how much fertilizer. Usign this information, they can fine turn the watering that goes, and time the application of fertilizer to when there is enough moisture. Claim to track “by plant” the moisture and are working on figuring what the optimal moisture for a given plant.
- goal modeling: this use case from Compsim which does goal modeling which tells you how goals are related in the predator operation program. (He compared this to Whitestein.) Some goals conflict with other goals. Showed a dashboard taht visually displayed the relationships between goals, adn these relationships can be tweaked.
IBO (Intelligent Business Operations) – collaborative distributed intelligence with machine learning. Example is a security trading desk with many different specialized agents.
Jim calls this a tidal wave of intelligence. Dive in!