Host, Jimmy Hewitt, sat down with Jeff Goodhue, Americas Technical Sales Leader for Business Automation at IBM to discuss the evolution of Business Automation. You will hear about the significant development within systems, people, and processes overtime. The two also discuss uniting the business side of an organization with the technology side, and all that entails. From planning and setting a baseline, to finding the right balance of analysis, you will hear ample advice on how to unite the business and IT as one entity!

Watch Episode 20 interview now!

Follow along with the episode now!

Guest: Jeff Goodhue, Americas Technical Sales Leader for Business Automation at IBM

IBM Americas team: North America, South America

~80 people

Utilizes Client Engineering team

Solution architect at heart

Education:

Engineering degree: Systems engineering

Focused on requirements, big problems, big systems, multiple things working together, and the requirements that allow them to design, execute, and build those systems

Focused on people and how they interact with systems, not just the systems alone

Prior Career:

Worked in various technical roles and business roles dealing with requirements analysis and design
Loves design work

Requirements: Relationship between people and requirements led Jeff to the business side of requirements

Has a background in software and engineering
Could help bridge the gap

Modeling:

Learned modeling for technologies and notations like UML, BPMN, DMN, etc.

The case and document notations led Jeff to talk to clients about bridging the gap of business in IT

Current position: Business Automation

Focus: Three ideas that come up often with clients…

Systems: Technology aspect

Processes: Methodologies

People: Being able to work across systems

Jeff is in charge of ~80 IBM teams

Each technical team member/specialist/architect like Jeff works with ~10 different clients a quarter
Equates to ~800 clients a year
Extends outwards to other IBM teams who focus on automation as a whole, not just on business automation
Includes Workflow, RPA, process mining, content and extraction, etc.
Working with business partners extends reach

Automation Trends and Evolutions over past 5 years

Systems

A lot better tools

Moved from code to models to low-code, no-code capabilities

Changed from drawing out a picture of a process and slowly turning it into low code, no code space to bringing the principles of low code, no code into everything a company does

Bring in business people early, in the middle, and after deployment

Artificial intelligence brought in capabilities that allow us to move quicker, do tasks we couldn’t before, or do tasks in an easier way
Continue to improve software and platforms
AI has made progress and be useful in certain ways but can do more in future
Continue to modernize underlying containers and integration to make systems work
- Containers example: Client is designing a new version of an existing application
- It is currently running on premises in a pair in one of their data centers across Metro region and in another on the other side of the city
- New version: Synchronous instead of asynchronous
  - Solutions active from far away outside of the metro pair
Kubernetes and hyper scaler cloud are topics that are coming up with clients

People

Still a lot of challenges with people. People get in the way of themselves

A lot more understanding by people of the methodologies and processes

Improved tooling has brought more people in

Brings people in that wouldn’t have been able to communicate, build, and execute themselves otherwise and now they can with the tools

People are learning how to be smarter than themselves

We used to expect the computer to tell us what was next, walk us through a process, etc.

Now we are also letting people make their own decisions using the information, tasks, recommendations with AI, content extraction from documents, etc. at hand
People are training the systems to be able to give them the right data and recommendations
Asking for more from systems

People are asking for more

People are looking for work that is interesting more often, and they’re asking systems to help with that

Many partners are looking to see how they can build something to help you more

Examples: Natural Language and Artificial Intelligence developments

Asking a chatbot to find a number from a report, combine it with other data, and return it in a visual way

Intersections of multiple API calls, but in a natural language sense

Process

Processes have improved as a whole

Used agile and iterative playbacks
Uptick in process mining
New methodology of design thinking
- Design thinking has been a big proponent
- Applying process mining, thought process, data discovery, etc. to the original modeling types of methodologies and processes
Agile and design thinking can help drive the way we are building solutions, applications, and our own processes
Process of training systems is becoming easier
Finding different ways to do the same tasks
Breaking down silos across divisions and departments is becoming more important

Uniting Business and Technology

Start immediately with planning:

Design thinking

Training

Modeling

Work with your business partner

- Pick the right methodology for your team and the challenge you are solving
Prioritization what is important for the particular project
Involve the business throughout planning

Find the right balance of analysis:

Don’t do too much or too little, or you will get stuck

Will help you not do as much rework and fixing later on

Set a baseline:

Examples: model mining, user interviews, KPIs, metrics

Multiple baselines are helpful

Without a baseline, you will face big challenges
Help bring the business and IT folks together
- Can have a common baseline to work together to improve

Time to Value

Baselines help us figure out time to value:

Can do assessments either up front or right at the beginning or in the initial phases
Pilots of projects can really help

Involve business early and often:

The business is much more likely to involve themselves, feel ownership, move forward with the solution, and continue to improve over time if they see time to value

Continually reassess:

You need to prove to yourself that you are still delivering value and going to get the payback, RTO (Recovery Time Objective), TCO (Total Cost of Ownership), etc. in whatever timeframe you determine

Reassess the most important priorities

Make adjustments where needed

Define value and use digital business automation software to achieve improvements on those value metrics

KPIs and Process Mining:

Both are helpful, especially when used together

But KPIs don’t tell you the how

We have gotten very good at operations, intelligence, and dashboarding as well as mining and data discovery
- Now bringing the two together

Process mining can help with continual reassessment

If you have gotten the baseline and metrics in your systems from version one, then you can feed them into mining

Can see a process model

Can see a different understanding or overlay on cost, time, dollars, or whatever other business metrics that you are interested in

Can help you decide in your continuous reassessment

Mining can provide you with insight and even recommendations in the future
Mining is not only helpful on the upfront process discovery and establishing your baseline, but it also helps with the continuous monitoring of that baseline after you’ve implemented your automation

Use case:

Client: Healthcare payer and provider space

Challenge: looking at different ways to predict or recommend information to doctors and nurses
- Get access to data in a secure manner in a healthcare setting, and then being able to use the data in a timely manner
Solution: Workflow for decisions
- Make timely decisions and recommendations
Proof of Concept (POC): client comes into doctors, data is there, their history is known, their current presentation at the clinic is known, and the technology can make a recommendation to the doctor/client immediately
- Recommendations can be iterations, reminders to follow-up with a client, etc. so doctors don’t have to remember everything

Key Ideas from Episode:

Systems, processes, and people have all evolved over the last 5 years

Systems:
- Moved from code to models to low-code, no-code capabilities
- Better tools, software, and platforms
Processes:
- Agile, iterative, and design thinking are big proponents
- Applying process mining, thought process, data discovery, etc. to the original modeling types of methodologies and processes
People:
- Looking for work that is more interesting, and asking systems and new tools to help with that
- Better understanding of methodologies and processes

To unite the business and technology sides of your organization: start with planning, find the right balance of analysis, set a baseline, involve the business early and often, and continuously reassess

Focus on time to value
KPIs and process mining can work in junction for baselines and reassessments

Link the practices and processes of the people and tooling (systems)

Common pain point: rework

Figuring out what process, decisions, or tasks can be individually automated and made better
Reworks: what issues are causing the problems in the middle of a process

Artificial Intelligence has a lot of individual success currently, but has a lot more potential

Natural Language
Recommendations

WATCH EPISODE 20 INTERVIEW WITH Jeff Goodhue NOW

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