Think 2021: IBM Looks to Evolve Digital Transformation Processes

    A decade ago, I attended a session with Harper Reed, President Barack Obama’s chief technology officer during his successful first run for president. I was impressed with his competence and wondered why he wasn’t chosen to successfully set up the Obamacare website. During the session, Reed also said something that caught my attention: big data, the dominant term for what the industry was driving at the time, was stupid.  

    From Reed’s perspective, big data was inspiring people to do stupid things like collect tons of data without knowing what to do with it. The efforts were consuming resources with little to no benefit. Eventually, data collection shifted to analytics, which focused on information needed for decision making.  

    I feel the same way about digital transformation: it isn’t a goal, but a process used to achieve a goal. Like any process when poorly done, it becomes a money pit. 

    During Think 2021, IBM’s digital customer conference focused on game-changing world technologies, the company’s interpretation of digital transformation implied a more complete process while using the same term. IBM effectively changed the focus of the term “digital transformation” from zeroing in on the technology first to analyzing what needs to be done. This focus on analysis seemed poetic at a conference named “Think.” 

    In the context of Think 2021, let’s examine why the word “intelligent” should be added to digital transformation.  

    Also read: APM Platforms are Driving Digital Business Transformation

    Defining Problems and Solutions

    Vendors want to sell products and tend to gravitate to concepts that imply more sales. Big data was an example of that — it focused people on buying tons of storage. AI focuses people on buying tons of computing power, and digital transformation focuses people on buying various hardware.

    AI isn’t an end goal, but a tool. Much like when working on a car, you don’t pull a screwdriver before figuring out the tool you need. Similarly, you shouldn’t jump to AI until you define the problem first. 

    If anyone tells you to need to transform or provides advice on applying any new process, your first question should be, Why? You need to first define and prioritize the problems you are trying to solve.  Often they are to reduce costs, improve sales and customer satisfaction, improve quality, and improve the firm’s valuation.  

    During Think 2021, IBM presented example after example  of the company working with its customers to first, understand the problem that needed solving, and then offering the best solution. For instance, IBM’s hybrid cloud push isn’t all IBM; it is a managed solution that allows a customer to pool and use resources from various cloud providers to address problems like price, security, compliance, and productivity.  

    Once done, the business is successfully digitally transformed, but only because the plan was focused on company priorities. Thus, the measure of success was addressing those priorities, not just automating a manual process.

    Also read: Using Responsible AI to Push Digital Transformation

    Roots in IBM History 

    When I was at IBM, I learned this lesson from one of the mistakes made there. We’d put up a line of storage hardware and automated the quality control (QC) process, which substantially reduced labor cost. However, every system coming off that line was defective. Painfully, the problems were so bad that it was cheaper to start over than repair the defects. IBM kept building and discarding defective products until someone came and shut the line down. 

    An analysis team was bought in, and they determined the problem was that each QC check only checked the process just completed, and the entire system wasn’t checked until the end. Several automated assembly processes broke things that had already been tested and passed. Still, since the interim tests weren’t comprehensive, the breakage wasn’t caught until the system failed at the final test. The overarching problem was that automation and cost-cutting were the goals when a quality system’s primary goal should be quality.  

    The goal isn’t the process; it results from trying to accomplish and understanding the business problem you are trying to solve. Assuring milestones and measurements are tied to that goal, not just the purchase and application of technology. IBM’s stated advantage isn’t digital transformation. It is assuring that your application of the technology will address the problems you need to be solved, not the revenue goals IBM has. This result is why, even though IBM didn’t say intelligent digital transformation, they meant it.  

    Solutions First

    Today, tech companies’ goal  is to sell hardware, though that is changing as as-a-service models proliferate.  But while driven by hardware sales, the industry will continue to use tag lines that focus on the sale of the solution, not its application. That approach generally changes to reflect the customers’ needs over time. Thus, big data became data analytics, and, I expect, digital transformation will change as well, perhaps to intelligent digital transformation.  

    IBM’s nuanced positioning, and heavy use of customer advocates, showcased the company isn’t solely focused on selling hardware, but also assuring the success of the project’s outcome. That is old-school IBM and the kind of attitude that once had the company at the top of the tech market. It is poetic that at a conference named Think, my biggest takeaway was IBM effectively added the word “intelligent” to business digital transformation.  

    Read next: AIOps Trends & Benefits for 2021

    Rob Enderle
    Rob Enderle
    As President and Principal Analyst of the Enderle Group, Rob provides regional and global companies with guidance in how to create credible dialogue with the market, target customer needs, create new business opportunities, anticipate technology changes, select vendors and products, and practice zero dollar marketing. For over 20 years Rob has worked for and with companies like Microsoft, HP, IBM, Dell, Toshiba, Gateway, Sony, USAA, Texas Instruments, AMD, Intel, Credit Suisse First Boston, ROLM, and Siemens.

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