It is well known that artificial intelligence (AI) enables better, faster and more automated decisions. Indeed, it has been proposed that AI is driving a resurgence of interest in redesign business processes.[i] That’s partly due to the ability of certain AI tools, such as robotic process automation (RPA) which when combined with machine learning as “intelligent process automation,” can automate information-intensive processes. It has also been argued that AI fits well into improvement methods such as Lean Six Sigma and can be applied at each stage of the so called DMAIC process (Define-Measure-Analyze-Improve- Control).[ii] Note that Six Sigma and Lean Six Sigma are highly codified and structured methods of process improvements which have a strong bias towards incremental improvement within organizational boundaries. The integration of AI into process improvement may have the potential to reignite interest in more major change – targeted at large enterprise processes – perhaps even reengineering.
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While excitement about generative AI is high, some questions persist as to how much value is being delivered. AI has been used by leading firms such as Amazon and Netflix to improve shopping recommendations, but examples of significant applications to improve overall business performance are not abundant. One area where AI has considerable potential is new product development (NPD). The NPD process has not changed much in most organizations for decades with fewer than 30% of new product projects becoming commercial successes. Yet only 13% of firms are using AI in NPD.
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While randomized controlled trials (RCTs) have traditionally been considered the gold standard for drug development, it is widely recognized that RCTs are expensive, lengthy, and burdensome on patients. According to some estimates, it takes more than a billion dollars in funding and a decade of work to bring one new medication to market. Despite exciting advances in genomics, patient-centric awareness, decentralized clinical trials, and the application of artificial intelligence (AI), there is a lack of compelling evidence to date that these trends have had a significant impact on the time and cost of the typical oncology clinical trial.
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In a recent LinkedIn post, David Rogers rightly described digital transformation as a combination of both digital strategy as well as organizational transformation. His simple formula reveals why so many companies struggle with their digital programs. It is: DX = D strategy + organizational X. It’s not enough to craft a business strategy enabled by digital. Organizational transformation is also needed. There’s the rub.
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We have known for over a decade that a major advantage of digital maturity is that higher digital maturity drives better financial performance. More recently, a survey of over 1,200 executives revealed that digitally mature companies were three times more likely than lower maturity companies to outperform their industry average on key financial metrics.
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The business benefits of focusing on customer experience have been known ever since 1954 when Peter Drucker wrote that “there is only one purpose of a business: to create a customer.” However, it wasn’t until 1989, when Jan Carlson, the chief executive officer of Scandinavian Airlines (SAS), published Moments of Truth advocating a focus on customer experience (CX) and providing practical guidance. He famously coined insights such as:
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Why do so many companies talk about digital transformation, and yet they deploy digital technologies for modest incremental improvement? They automate simple, repetitive, rule-based tasks. They don’t redesign. They tinker at the margins with AI through small proof of concept projects and pilots and fail to deploy models at scale for true economic value.
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Large scale change has never been easy. Nearly three decades ago, leadership guru Dr John Kotter reported that 70% of all major change efforts in organizations failed. Just a couple years later, the late Dr. Michael Hammer estimated a 70% failure rate for the radical reengineering efforts. Now, that transformational efforts are often driven by technology, the recent success rate is equally bleak according to research by BCG. The root cause of failure with large scale digital change is captured by George Westerman’s first law of digital transformation, which states that: Technology changes quickly, but organizations change much more slowly.
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There has been more than a modicum of buzz around what IDC calls intelligent process automation and what Gartner calls hyperautomation. In both cases, these terms refer to the integrated deployment of digital technologies such as robotic process automation (RPA), intelligent business process management suites (iBPMS), artificial intelligence, process mining, etc. Integrating digital technologies is far from a new concept. MIT and Deloitte advocated this approach back in the day when everyone was focused on social, mobile, analytics, and cloud (SMAC).
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The analyst community is having a field day with hype around “low code.” IDC has predicted that there will be more and more low code used and that the worldwide population of low-code developers will grow with a CAGR of 40.4% from 2021 to 2025. Gartner predicted that low code will increase nearly 30% from 2020 to reach $5.8 billion in 2021. Forrester has also jumped on the low-code hype wagon and forecasted that by the end of 2021, 75% of application development will use low-code platforms.
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There is a great deal of excitement these days around “digital transformation.” Instead of being preoccupied with “transformation,” companies may be better off paying attention to their level of maturity in deploying digital technologies – and then working diligently to become more digitally mature.
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Accurate, complete, and timely data has always been required for success with digital programs. This is even more the case when it comes to large, enterprise-wide digital transformations. Yet, a recent New Vantage survey reported that..
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A recent survey on Big Data and Artificial Intelligence (AI) reported that cultural challenges, not technological ones, were the biggest hurdle to overcome around Big Data and AI initiatives. According to this 2021 survey, the vast majority of respondents — 92% of mainstream companies — continued to struggle more with cultural challenges than with technological ones.
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The track record for transformations has been disappointing for over three decades. In 1995, Dr. John Kotter found that only 30% of transformations succeeded and it’s been pretty much that way in survey after survey ever since. A recent McKinsey survey found that digital transformations may be even more challenging.
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Over three decades ago – in 1988 – Phil S. Ensor coined the phrase, "functional silo syndrome” to describe a top down managed organization with vertical departmental silos, characterized by “mistrust” and a lack of cooperation. The negative and destructive impact of “silo” thinking has been known for some time. Yet, while most executives recognize the importance of breaking down silos – they struggle to make it happen.
There’s so much at stake, yet companies continue to struggle with digital transformation. A recent BCG study found that most digital transformations fail, and only 30% of transformations met or exceeded their target value and resulted in sustainable change. This should not come as a total surprise. Back in 1995, Dr. John Kotter…
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