Plazabridge Group Model for AI-Driven Digital Transformation: Decision Framework

 In Executive Strategy, Global Trends, Innovation, Musings, Uncategorized

Digital transformation for most companies is still a vision unrealized. With so much hype around AI and Machine Learning, primarily driven by vendors of software, company CEO’s are often at a loss on where to start in seeking competitive advantage through a digital strategy.  The effort to dazzle the marketplace with acronyms and technical jargon does little to clarify the advantages. Digital transformation efforts can yield real value in improved processes, in discovering market advantages not discovered by competitors and bring clarity to decision making where confusion prevailed. This is only possible if CEO’s understand the basic approach to turning data into gold.

AI is irrelevant without data

RECOMMENDATION #1: Institute a concentrated effort to inventory all internal and external data sources even remotely applicable to your business.

Find public datasets from your industry or reflective of your operations. Seek out private datasets that can provide value to your efforts. Private datasets must be worth the cost and should be purchased when the question or problem is clearly identified. Mine the internal data sources wherever you can find them. No internal data source is too small or irrelevant. The amazing thing about data is that it can always be used in digital efforts.

RECOMMENDATION #2: Build a digital mining team to organize the data in preparation for analysis

Use historical data and even data from other companies that may correlate to your operations. The volume of data can be as equally as important as the relevancy of the data.

RECOMMENDATION #3: Treat the data minors different than the statisticians.

Their skills are often quite different. Great IT professionals understand how to discover data sources lurking in the many operational systems. Algorithm developers learned on clean data sets and may have few skills on how to organize raw data for analysis.

Business challenges drive the need for AI.

Every MBA at your company should have had a fair amount of study in statistics and operations research. These are the people who could unleash insight into transforming business problems into their statistical equivalent.

RECOMMENDATION #1: Find and cultivate the employees who see the math behind business processes

Business process improvement techniques are the cousin to business AI. The difference is that the statistical learning algorithms are more sophisticated these days and venture well beyond the basics of Queuing Theory and Calculus.

RECOMMENDATION #2: Challenge each business function to come up with ways to employ AI tools to improve results.

Every function can learn from their data in new, sophisticated ways. Challenging employees to apply these tools is the first place to begin any digital transformation program. From customer behavior analysis to predictive sales patterns that yield greater results; from timing of supply chain management decisions to predictive maintenance processes on the factory floor; and from improving hiring practices from old school methods to determining through analytical means the best leaders in the company; all are available to mine for accelerating results.

RECOMMENDATION #3: Remember that gold is often found in seemingly unrelated business challenges.

The power of AI comes from extracting insight into areas that humans may overlook. Processes that may seem to be completely unrelated may in fact have a distant relationship that, if understood, can bring innovation to your company. What does the behavior of your inside sales team have to do with logistics planning other than anticipating future demand? Insights might be found in predicting regional buying behavior, in positioning warehouse inventory for “beat the competition” delivery times or in uncovering the role of inbound call behavior to dangers of disrupting logistics efficiency (e.g., buying changes due to regional illness).

AI is advanced algorithms put to practice.

Machine Learning, as a subset of AI, enables the continuous training of algorithms with new or real time data streams. The power of AI is in the ever-improving accuracy of the probabilities.

RECOMMENDATION #1: Combine your data streams with business challenges to set up a continuous learning environment to achieve all that AI has to offer.

Supplementing static data with dynamic data to improve AI probabilities is critical for companies interested in competing on the time dimension. All streaming data feeds should be optimized for speed and accuracy. This often puts pressure on IT resources, especially if they are not use to integrating cross-functional information and interacting with diverse organizational teams.

RECOMMENDATION #2: Implement unsupervised learning systems to create a comprehensive analysis program for your company.

Unsupervised learning uncovers patterns in raw data that makes for interesting insights. This is the part of AI that gets so much media attention because the insights seem strange when mathematically realized. Your sales cycles could be correlated with how often you walk the dog! Not relevant but illustrative of the connects that may prove useful to stretching your organizations efficiency.

RECOMMENDATION #3: Let the analytical folks be creative in their approach to your company’s data.

They are the experts in analytics. They are the subject matter experts in how to apply increasingly sophisticated techniques to both structured and unstructured data. From NLP to QML, they are at the forefront of how business will be done in the 21st Century. Let them run free!

Plazabridge Group are experts in implementing digital transformation strategies across business functions. Our experience is especially useful to CEO’s who need to understand the implications of AI and Machine Learning in business and how the trends will impact the operations of their own firms.

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