The Top 5 AI & Machine Learning Trends for 2021 And Beyond

6 Common Pitfalls You Must Avoid To Succeed with Digital Transformation

October 29, 2020

A lot of money is flowing into Digital Transformation (DX) initiatives. Worldwide spending on the technologies and services that enable the digital transformation (DX) of business practices, products, and organizations is forecast to reach $2.3 trillion in 2023, based on an IDC spending guide. The IDC findings reveal that DX spending will steadily expand throughout the 2019-2023 forecast period, achieving a five-year CAGR of 17.1%.

Yet 70% of all DX initiatives do not reach their goals. A WSJ survey of business leaders and executives found that digital transformation (DX) risk was their #1 concern in 2019. Why do most transformations fail? What can business leaders do to improve the odds of success? George Westerman, principal research scientist, MIT, defines DX as “using technology to improve an organization’s performance and reach radically.” Research from MIT shows that businesses that have become digital masters are 26% more profitable. According to Gartner, DX success needs to operate at three levels – corporate governance, management, and execution, and companies often make mistakes at all levels.

Here are six common mistakes that leaders can avoid to beat the odds:

1. Not having the right digital-savvy leader in charge: Having the right leader with relevant digital experience can make all the difference. A Mckinsey survey reported that respondents are up to three times more likely to report successful digital transformation when key factors are in place. The involvement of people in critical roles, including senior leaders of the organization, dramatically increases the chance of transformation. A key trend is the growing importance of the Chief Data Officer (CDO) role. A 2018 survey from NewVantage Partners shows the advent of CDO with increasing demand in Financial Services, Insurance, Pharma-Medical, and Other sectors – 62.5% of participants confirmed that their organization had appointed a CDO, a convincing increase from the 12% level of 2012. Many CDOs are directly reporting to the CEO and have increasingly become the change agent responsible for data-driven transformation.

2. Data maturity and misalignment between strategy and technology: The business dynamics are always changing driven by market forces – regulations, supplier, customer, or competitive pressure. A change in business strategy warrants a corresponding shift in technology strategy. IT budgets and projects should be tied to the overall business strategy to support new initiatives. Suppose the business requires AI as part of digital transformation. In that case, IT needs to evolve and have a heterogeneous data processing architecture (GPUs, ASICS, Core Processors) in place to support AI solutions. If you don’t have the right data infrastructure, then an AI initiative will not succeed. Equally important is legacy IT infrastructure and how to integrate it into your new digital architecture. Too many organizations collect data only to ignore it in the end. Others analyze only a fraction of the data. The problem may not be in the algorithms or that state of the art AI platform. The fundamental problem lies in the lack of a scalable data infrastructure architected for end-to-end data flow, the organization’s data maturity, and the inability to make data accessible when needed.

3. Pursuing the wrong projects: Often, there is disagreement among stakeholders about which digital transformation initiatives to pursue. As a result, the business may end up selecting the wrong projects. If your business is new to AI and ML, starting with bold, ambitious projects may not be ideal. You may end up spending 3 -6 months collecting data only to realize that you don’t have the right data for the use case. It is far more prudent to select low hanging fruits, small projects that can create data-savvy teams and make them ready for AI. This approach will instill confidence and encourage others to follow.

4. Building the entire tech stack inside: The classic buy-vs-build problem – we have unique requirements, no one understands us, it is better to build the entire technology stack in-house. This thinking does not take into consideration the costs, resources, and timelines. So how do you incorporate AI and ML in your business application? The right approach popular with nimble medium to large companies is to use automated machine learning (AutoML) tools. AutoML tools make AI more accessible to everyone by automating complex manual data science processes. The traditional AutoML platforms automate only the machine learning component of the process. While useful, AutoML 1.0 platforms have zero impact on the most labor-intensive and challenging part of the data science process – Data Preparation and Feature Engineering. The next-generation platforms aka AutoML 2.0 include end-to-end automation. They can do much more – from data preparation, feature engineering to building and deploying models in production. With a few exceptions, an AI automation platform will almost always be faster, efficient, and deliver a better ROI for analytics initiatives.

5. Choosing incremental improvement over radical innovation: Businesses need to understand the meaning of transformation. A 7% increase in productivity may seem reasonable but is only a marginal improvement. While you may accept that kind of return, DX can enable radical innovation such as 50X improvement, opening up new revenue streams, and new business models. You must invest in some of these transformational opportunities that are true game changers and build a real competitive advantage.

6. Underestimating cultural challenges: The biggest threat to transformation is culture (or lack of it). DX leaders need to be intimately familiar with the company culture, beliefs, and how to connect the shift to the core purpose. The transformation has to align with the culture, address roadblocks, and eliminate challenges. Without the support and buy-in from the trenches and various organization levels, the change will not succeed. Clear goals, constant communication from the digital champion, and the executive leadership team can help. Many transformation experts have stated that DX is not about technology, but people and culture is at its center.

Sachin Andhare

Sachin Andhare

Sachin is an enterprise product marketing leader with global experience in advanced analytics, digital transformation, and the IoT. He serves as Head of Product Marketing at dotData, evangelizing predictive analytics applications. Sachin has a diverse background across a variety of industries spanning software, hardware and service products including several startups as well as Fortune 500 companies.