Achieving mechanical growth through innovation & automation

With the polemic around ChatGPT, I felt compelled to write about automation, AI and machine learning. Advanced automation methods can be the initial phase for consistent and automated redesign of essential operations, forming an instinctive cycle in which technology enhances operations, and vice versa. Basically, your business keeps spinning.

#futuretech #automation #AI #machinelearning

---

By leveraging new automation tools and techniques, alongside longer-term IT modernization strategies, operations teams are streamlining the last steps of internal processes and quickly digitizing them. Combined with current development in IT (e.g., agile software development and microservices) as well as organizational design (like more sophisticated learning platforms and deploying agile structures over traditional matrices), operations managers are able to rapidly switch up their strategies to build personalized tech - which they can control, maintain, and upgrade - achieving over a 60% reduction in expenses while simultaneously improving quality and speed.

Despite having low ambitions and limited success, many operation teams, with their senior leaders, still try to make changes. Without a clear goal in mind or consistent commitment to alter their operational model, companies cannot move forward and have no choice but to keep testing strategies on individual projects rather than creating an efficient system to ensure continual improvement.

If utilized without forethought and an organized plan, it can result in not only inefficiency but operational disruption: malfunctioning robots that disrupt processes, an over complex network of workflows that do no improve with time, and managers who are unable to effectively manage their digital team. But when subjected to careful planning and the correct methodology, these tools represent the beginning of hastened, technical-enabled process improvements across many operations, providing better resilience while improving productivity.

In order to overcome these pervasive problems, businesses must:

• Create an easy-to-grasp cycle that aids operations teams in understanding what a constant development system would look like while using automation and the advanced operating model.

• Figure out what the primary challenges that managers typically encounter when attempting to start up or speed up the operations-to-technology process are

• Design a workable short-term plan that will help managers overcome adversities, attain sustainable outcomes in the near future and long run, generate new prospects for personnel and institute an overhalling ambition regarding enhancement in the organization.

Despite there being a long-standing tradition of businesses merging technology and operations, many companies still encounter difficulties with intricate IT systems and complicated, expensive, and frequently inaccurate management.

Through Lean and other operational excellence initiatives, operations can improve dramatically by placing focus on customers, reducing wastefulness, creating visibility, and cultivating an atmosphere that fuels continuous progress. Yet the achievement of these goals may be extremely limited because of IT systems that are too slow to deploy, expensive, and hard to maintain correctly. Consequently, operations must still grapple with highly manual and disjointed processes.

The future is unpredictable

The latest developments in tech, like process-mining and natural-language-processing tools, robotics (RPA and RDA), accessible AI and ML, refined data-management systems, and microservices formed with APIs have presented innovative opportunities. Companies can now upgrade complex systems, automate laborious tasks and interpret data swiftly to diagnose issues' sources of lag or other issues.

We label this continual refinement process as "transforming operations into tech." This streamlined phrase applies to the codification of all the manual steps and regulations that are found in many operations sites, as well as the imaginary playbook that resides merely in the minds of fundamental operations personnel. The result is a collection of enacted commands that robotic bots can easily and swiftly test, update, and execute. The automated devices can also develop proof of records and produce fresh data: both from bottom up, for exact labor being processed, and top-down, on how these task processes are running. This in turn opens up possibilities for newer exploration, perceptions, and effects.

By taking advantage of machine vision, process mining, and natural language processing tools, companies can gain valuable insights from various sources, including desktop actions, log files, and call transcripts. This allows operators to benefit from real-time mentoring and increases the potential for further optimization activities. The implementation of this method can trigger a cycle of continual improvement that operations teams are able to maintain. The melding of these tools and competencies, deployed appropriately, is exceptional, especially in combination with the latest tech such as vector databases, in the interest of efficiency.

In the past few years, this progression has become possible because automation and machine learning have seen fast-tracked development. The rate at which programs can be innovated has soared significantly and the extent of activities that can be automated has burgeoned: from just plain data entry manipulation to sophisticated cognitive tasks such as concluding implications from data. A major international bank has automated considerable portions of the equity analyst's function which includes comprehending fiscal records, comparing same-store sales, deducing insights and forming remarks.

Still too early?

In many operations stores, process improvement can be delayed due to a lack of clarity around the processes; while if they are properly defined, there might not be enough data to find out the exact issue or areas that require improvement. Additionally, when teams wait for a future platform that could bring drastic changes to an area or process, then chances of improvement as we speak can decrease as well.

When companies have many priorities at once, they may neglect to create a process map or put into practice microeconomic strategies. Furthermore, there might not be a solid argument for automating the final steps of the process if there aren't often used or do not involve many people. Once an automation solution is built, it may be too rigid to adapt to shifting business conditions.

These executives are not taking into consideration the whole expenditure of unfinished automatization. For instance, they don't measure the extensive internal conversations regarding how to address a specific exception. These can involve senior personnel "retracing" how to handle the job, testing and trial in traversing dated systems, duties passing between teams who are not familiar with managing lower-scale tasks, the danger and price of rectifying mistakes, and the venture in keeping incomplete, out-of-date, and possibly unused guidelines, submission to regulation, and report materials.

Taking advantage of this technologically progress-fueled flywheel

Leaders in automation have a different point of view. With self-documenting process-automation tools, companies can minimize risks and costs associated with relying on one person to remember everything or manually creating documents. Additionally, the ROI from these tools allows businesses to quickly become proficient in process mapping and instrumentation.

As it founders, it creates figures relating to pauses in action and regions of gradually inadequate productivity, arousing the tech-as-ops turbine. As a result, the funds saved are not merely from expunging manual labor, but also from controlling teams with an enhanced view of day-to-day performance issues and disparity. With sufficient pliability built in, an enterprise can utilize this data to enhance the present procedure or reduce dangers imaginable information technology alterations. The regulation updates necessitated to modify the practice are usually quite modest and right away extensive across analogous procedures. Employees don't have to be retrained. Brought out of repetitive tasks, specialists can focus on implementing more process renovations and computerizing fewer occurrences.

Achieving prominence

Although the ops-to-tech cycle is easy to explain across operations teams, there are often numerous difficulties that arise during every step. In perceiving and assessing, as a componant of spotting openings and recruiting IT, the need is to meet the proper degree of particularization, so that the procedure holder can aptly oversee procedures and comprehend their proficiency. Not always the case. If particularization is too great, companies miss fresh possibilities to mechanize, on account of there are too many physical exclusions. When it's low-pitched, the intricacy (and resultant commercial outcome) can seem overpowering.

One should commence by interrogating the need for variations at a broader degree. Spot chances to simplify, amend and normalize procedures- while reducing diversions- ahead of looking into the next detail level. Doing away with disparities with meagre added value is a noteworthy opportunity for constant progression. Execute this job in accelerated phases, instead of endeavoring to diagram all occasions and disparities entirely on the initial run-through, as it is often unreflective of reality.

With the intention of accomplishing ambitious projects, a zero-based approach to the process with cutting-edge tech in consideration helps. Several fundamental inquiries form the job: what is keeping us from digitally transforming or automating the processes from beginning to end? Could rule-based activity declutter the current abnormalities and manual labor? Is any elaborate thinking paramount? Would any of the latest automation technologies help here? Is a new digital front or set of ordered inputs necessary? Are there legal or regulatory issues obstructing continuous processing? Apprehending each probable obstruction, coupled with a credible view on practicability and needed effort, works to prioritize activities. Even when digitization requires some time, various near-term automation options might deprive us of the cumulative risk and installment costs of executing digital transformation—and could even prove to be self-subsistent.

The rapid evolution of modern technology is propelling the influence of automation and digitization, meanwhile generating data for on-going improvement. For instance, potent automation resources and methods developed from collaborative work like forums between automation professionals allow companies to rapidly solve shared difficulty based on general setup requirements, practices, and accessible web facilitating, not to mention the extremely powerful tools put at the disposal of anyone with an internet connection (I'm not only thinking of ChatGPT, but also about the plethora of free services offered on Microsoft's cloud platform Azure, for instance).

Operations managers must take ownership and act with urgency to successfully use technology to drive operations in impact and data analysis. Advanced-automation tools, potentially combined with process-mining and natural-language-processing tools, can provide more data than conventional sampling exercises permit - revealing possible openings, setting priorities and offering ongoing surveillance. Performance objectives ought to be altered as necessary, circulated across the entirety of the organization, and owned by the operations team.

Once this redesign flywheel is set in motion, organizations can aim higher and achieve greater success. By dramatically cutting costs and improving reliability and customer-facing capabilities, they can unlock a world of endless possibilities, simply by running on the inertia created by the previous innovation.

by Leo Florea for Claireity consulting © 2023