Is AI Data Driven, Algorithm Driven, or Process Driven?

There are those that think that data is the oil of AI and the focus should be clean data, data science and deep understanding of what the data means. There are those that say data is meaningless without context that can be with other data, models/algorithms or processes. Let’s explore the arguments in a concise fashion to discover the advantage of each view.

The Case for Data Driven:

Data is the starting point as it is a very useful asset. True or not, it is assumed that data carries knowledge and that tapping that knowledge will give advantage to those that study data well. It just makes sense for AI to start with data and leverage an advantage can be had by learning from it. This is especially true in the age of big and fast data. It seems especially true when sensing signals and patterns that are occurring in emergent situations. Businesses have had a long history with business intelligence and a great deal of the effort surrounds data. Why would it be any different with AI?

The Case for Algorithm Driven:

Understanding the advantage that algorithms have over static data in the wild is important. In fact, organizations can gain the upper hand by having an algorithm that optimizes their business. In fact, finding the right formula, statistical model or projection that is appropriate for the situation is the real art of business. These algorithms are guarded by organizations and are often considered the secret sauce for success. While they are dependent on clean data, the rules implied in the math or the logic are the real differentiators for many industries. Where would the insurance industry be without actuaries and their prized algorithms? AI will be no different.

The Case for Process Driven:

The importance of sequence and president is crucial in doing the right steps or tasks in the proper order to obtain results. It makes no difference, if the process is static and repeatable or dynamic and emergent. Knowing the next best action is the key to getting the best business results. Bringing to bear the right data and algorithms at the right time is what process is all about. With the precision of process, business outcomes are sure to be on point and appropriate adjustments can be made with a transparent feedback cycle that employs various forms of monitoring.

Net; Net:

The real story here is that you need all three for long term success. You may start with one and add the other. AI is truly starting with data this time as machine learning ramps up to its power curve.  As AI progresses, it will have to cooperate with both algorithms and processes. Data based AI is working well today and it will likely lead to rule based AI again as the sophistication and scope of the problems expand.  A triangle needs all three sides.

Combining Digital Technologies for Success

We have enough experience in leveraging digital technologies to be able to add value with a great chance for success. While nobody can guarantee results, there is enough evidence in organizations that have emerged to say what digital technologies deliver and what combinations are particularly potent. This post will take identify these potent combinations, but I would suggest understanding the ingredients first by clicking here for a helpful blog post on the most common successful digital technologies employed today. Teaming technologies together is a secret that many have been using to their advantage.

Putting Powerful Pairs to Work:

There are some common pairings that have led to some interesting case studies and tangible or intangible benefits for several organizations. I expect these combinations to deliver for the organizations that pursue them.

    •CJM & BPM Delivers a balance of customer satisfaction & efficiency

    •BPM & AI Delivers processes that enable smart resources and actions

    •BPM & RPA Delivers automation of boring or time-consuming tasks

    •BPM & PM Delivers reality-based incremental improvements

    •IoT & AI Delivers IoT management and recognition of actionable patterns from big and fast data

Teaming Up Teriffic Triplets:

There are some common triples of digital technologies that have proven to work well together and have delivered business outcomes consistently. Again, I expect these combinations to accelerate their collaboration.

    •BPM / PM / RPA Delivers targeted automation

    •BPM / IoT / AI Delivers intelligent actions at the edge

    •Arch / Low Code / RPA Delivers incremental transformation of legacy

    •Workflow / Content / Collaboration Delivers team outcomes on cases

    •Unified Communication / AI / BPM Delivers Improved Customer Sat

Net: Net:

While these combinations have proven to deliver, there will be more combinations that will emerge for a strong delivery. Eventually, these powerful combinations will aggregate to even larger digital platforms that may specialize. Examples of specialty platforms include Automation, Sales Engagement, Data Science, Digital Twin Transformation, Infrastructure Management and more

So How Goes that AI Spring?

While AI hasn’t reached its full potential or its eventual impact yet, AI is making good progress in many directions simultaneously. Let’s examine some of the progress to date. While I’m sure that AI is adding value, I’m also sure there is more progress that is not visible yet as it is in the labs or being pioneered in several scientific avenues. Let’s look at the value add of AI to date.

Copyright Jim Sinur

Turning Data into Knowledge: 

AI is contributing to digesting large amounts of fast or slow data to create information or knowledge to assist the contextual situation, and resources accomplishing work. Work resources are only as useful as the knowledge they are provided to make decisions and take action. AI is playing a substantial role in assisting and can accelerate learning to suggest where to direct current and future actions.

Problem Recognition:

AI can sense various signals at the edge, or not, and recognize patterns. These patterns can represent emerging issues or problems. These patterns can be associated with well understood and prepared scenarios ahead of time so that AI can recognize the presence of threats or opportunities. Also, AI can find new and emerging scenarios and bring them to the attention of the right resources for decisions and possible actions.  

Taking Actions:

Once appropriate actions are selected, AI can help with appropriate responses by human, software or physical bots. By leveraging natural language, humans can kick off actions that are either pre-packaged and sequenced or parallel and emergent.  Sophisticated AI Agents or Bots will bid on tasks that are necessary to complete organizational outcomes within governance constraints.

Net; Net:

Because AI is showing progress, it will not likely fall into another AI Winter (see https://jimsinur.blogspot.com/2018/02/no-more-ai-winters-really.html).  Keep in mind that it is still early, and AI has a long way to go before it can reason, solve complex problems alone or learn enough to plan by itself. To end, AI is likely to partner up with other non-AI algorithms to take these areas of need down the road. AI will partner with data science platforms, automation platforms, and customer-centric platforms to help with next best actions. Spring will eventually lead to a full bloom summer and the fruits of a full AI harvest.

Digital Twins Can Reinvigorate Enterprise Architecture Efforts

We are hearing the tales of severely ill or dying enterprise architectural efforts in even the best of organizations. It really shouldn’t be a surprise as architectural efforts are long, slow and change before they are complete. Even if an organization manages to complete one, things are changing so fast the value of even a rare complete architecture is dubious at best. Leveraging digital twins can be a game changer for architecture. A digital twin is a digital replica of physical assets, processes, people, places, systems and devices that can be used for management purposes.

Links the Real World to Logical Architecture:

Architecture is important, but it tends to isolate and not connected to reality often times. Basing the current architecture on digital twins allows for a reality-based current state and helps make a case for making changes incrementally or in more significant portions. Target architectures could also be modeled from a base of current digital twin behaviors.

Keeps Up with the Speed of Change:

Digital twins are changing in real time, so having a current architecture linked to now gives an advantage especially when linked to some machine learning, simulation and other forms of predictive assists. Since the measurement of the digital twin can be amped up to real time, the link to reality can be instantaneous if desired.

Monitors Business Assets in Context:

Digital twins interacting in an animated fashion gives an accurate picture of what's going on in various levels of neighborhoods of interacting contexts to give a more exact sense of behavior and interaction results. This can allow for better and faster managerial intervention to indicators and tolerance factors.

Finds Emerging Patterns of Interest:

The behavior of an asset by looking at its state and isolated behavior can point to opportunities to adjust. Interactions with other assets, resources or contexts can point to patterns that may look like a new scenario that was not planned for and project where that scenario may lead. The emerging scenarios can point management to opportunities or threats.

Points to Down-Stream Impacts:

Combining digital twin behavior with algorithms like simulation can project down-stream impacts and intercept situations before they occur. Responses can be planned and put into place and new tolerances can be established.

Net; Net:

Old school enterprise architectures have limited value today, except for defining things like target business outcomes, target business competencies and target skills. Super-charging architecture with digital twins are the answer for the modern digital organization.

Causing Your Customers Unnecessary Pain?

Many organizations are causing their customer's significant pain when interacting with their businesses. At best they are costing customers unnecessary time and inconvenience when getting to the customers desired results. At worst, the customers are so turned off that they are searching for other options. This is because organizations are primarily interested in their own goals and outcomes and do not really understand the journeys customers want to take. To that end, we have created a customer pain index for a better understanding and we are completing a book for organizations to have a great customer excellence program. If you want to register for one of the first 300 copies of this new book click here.

The customer pain index above is adapted from the pain chart you are likely to see in a Doctor’s office, medical facility or a hospital. On the horizontal axis, the level of pain is measured from no pain to high levels of pain. On the vertical access, the level of loyalty is measured from loyal to “heading out the door”. This is a chart where you don’t want to be at the upper right portion of the chart, but many organizations are hovering there because they are not feeling any pain themselves. They believe that customers forget and won’t leave them. They say to themselves” After all, aren’t we the best? We design surveys to show that very fact.”

The Current State of Affairs:

Typically organizations buy or build unfriendly silo transaction systems and then accountants and business unit heads try to cut costs by letting the customer do as much of the work as possible. The accountants say “Let’s add a voice recognition system that upsets the customers to save labor costs” and “let’s manage the average call duration to zero.”  This is short term profit drive driven thinking that gets an organization in trouble as services and products have little or no differentiation. The more enlightened organizations add better processes and change the customer experience to make it a bit friendlier. This gets customers to a near neutral situation where they are not motivated to change as the pain is somewhat tolerable.

The End Game; Customer Excellence:

The better of the enlightened organizations start creating, mapping and measuring the customer journey throughout their organization while attaining the customer’s goals within organizational goals and tolerances. This is the beginning of the “customer first” kind of thinking. While this is a good start, the very best organizations that are aimed at customer excellence, look at the customer's journey from end to end, even if there are parts of a journey that do not occur within the confines of said organization. The best of the best study the end to end customer journey and define new and innovative business models that cater to the whole journey that really gets at what the customer wants.

Net; Net:

Many organizations are fooled into thinking cost containment and not profitability based on balanced revenue and cost targets. Customers will care for your organization that has their best interests in mind. This means a broader and longer-term view when it comes to customer excellence. Don’t be that organization that wants to be in the red and orange zones of the customer pain index. Do focus on customer excellence, not just customer experience. If you might, click on this link for one of our books for free.

Co-authored by Jim Sinur, Gero Decker & Mark McGregor