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5 Ways to Accelerate and De-Risk Business Transformation Through AI

Use Cases & Projects, Scaling AI Jerry Hartanto

At any given time, organizations are transforming their businesses to keep up with market dynamics and the competition. Notably, business transformations have three main objectives:

  • Operational change, meaning do what is currently being done in a way that is better, faster, or cheaper (e.g., using RPA to streamline back office activities)
  • Business model innovation, meaning do what is currently being done in a fundamentally different way (e.g., Netflix streaming videos versus sending DVDs and distributing their own content vs others’ content)
  • Domain expansion, meaning create new business beyond currently served markets and change the very essence of the company (e.g., Amazon moving from e-commerce to cloud computing)

AI can help drive step change improvements to transformation initiatives and, here, we’ll outline how that can be done in practice across five business transformation initiatives: process, digital, management, organizational, and cultural.


1. Business Process Transformation

These initiatives focus on the “how” things are done, including optimization and automation. It frequently involves moving from a function-oriented, vertical approach to a business-oriented, transversal approach. Processes like lead-to-cash or trouble to resolve are examples of end-to-end processes that aim to break down silos and, ultimately, improve end-to-end business processes. Traditionally, teams have approached business processes transformation through process mapping and methodologies such as Lean Six Sigma.

With AI, though, the value-add is that the technology can automate decisions (e.g., loan approval, machine maintenance, inventory reordering, supplier selection). To be sure, this is very different from rules-based automation where you need to explicitly tell the system what to automate. AI can customize the automation by learning from previous examples. It can also figure out what drives the process metrics (e.g., cycle time, quality, and costs) — AI can help you understand the inputs and how they drive the outputs, so you can get a better understanding of what specific inputs to prioritize in order to achieve the outputs you are targeting.

2. Digital Transformation

Digital initiatives involve aggregating and sharing new data in new, more meaningful and efficient ways. They are often focused on improving the customer experience, creating or improving products, and generating new offerings. Examples of a non-AI approach with digital initiatives include online ordering systems to drive new customer interactions and 360-degree marketing to get the complete view of the customer to more effectively upsell or cross-sell. With AI, organizations can improve their customer experiences through finer, more accurate customer tiering, personalization, churn reduction, sentiment analysis, and dynamic pricing, for example, based on numerous attributes that are too many for humans to consider or which don’t neatly fit into an equation. 

3. Management Transformation

Here, we’re referring to empowering decision making, pushing the ability to make decisions all the way down to people working on the day-to-day operations of a company. Importantly, though, these people need access to information and, the more transparent the information is, the easier it will be for those people to make decisions that balance customer needs and business objectives. Before AI, organizations focused on management initiatives through span of control and division structuring. With AI, organizations can prioritize their activities through risk stratification (e.g., for customer churn, teams could stratify which customers are likely to churn, if they would actually respond to an offer, and what offer would retain them). 

4. Organizational Transformation

Organizational initiatives involve assessing department staffing and structure so that the staff can succeed (e.g., breaking down silos, right-sizing in order to reach business objectives). Without AI, teams have performed organizational initiatives via stakeholder interviews and benchmarking, for example. With AI, teams can predict staff success and retention based on staff attributes and the organization’s environment. It can also provide customized recommendations on the structure to put in place (e.g., looking at staff capabilities, education, current projects, organizational composition, size, and geography, among other variables). 

5. Cultural Transformation

Cultural initiatives involve changing the organizational culture in order to better meet business objectives — which often involves getting people to buy in (i.e., providing conviction to change) and demonstrating what good behavior looks like. Without AI, teams have done this through team-building activities and the insertion of change agents so that people will follow the examples that they provide. With AI, teams can understand and identify which factors and types of behavior drive the objectives that they want and can quantify the impact of staff behavior on business outcomes to focus on the ones that matter most.

AI Mitigates Business Transformation Challenges

Here are three obstacles that organizations often cite when it comes to transformation initiatives and how AI can help address them.

1. Continuously changing business needs

On the one hand, you have customers who are continuously evolving and, on the other hand, you have the organization which is trying to keep up with those customers and may develop inefficient business processes doing so over time. AI can help address this challenge because it can continuously assess customers (e.g., through attributes that define and tier customer segments and customer sentiments, understand drivers of action, and predict behaviors and outcomes). Further, in terms of inefficient business processes, AI-enabled solutions can be used to continually discover and rapidly update business rules, including those not explicitly defined.

2. Risk and experimentation

Many organizations have a bit of a risk-averse culture and, if they are willing to take a risk and experiment, they don’t have the ability or foundation in place to experiment quickly. AI is able to reduce the perception of risk by predicting outcomes and transparently generating insights and cause-effect analysis on how situational factors drive those outcomes. With regard to experimenting more rapidly, teams can implement predictive models to identify the most promising experiments and  target those first.

3. Lack of collaboration and resources

There are four main challenges within this section:

  • Inadequate IT and line of business collaboration (which can be solved with a solution that enables all stakeholders to iterate on projects and share data assets and processes)
  • Lack of dedicated IT skills (addressed by an environment that enables reuse of data assets and self-service advanced data analytics for less technical personas)
  • Insufficient budget or budget constraints (significantly eased by by teams gaining productivity through self-service, asset reuse, and rapid collaboration across functional silos)
  • Legacy systems (the collaborative environment chosen should seamlessly connect to multiple data sources and operational systems)

So, as you can see, AI-enabled solutions can undoubtedly accelerate and change the game for business transformation initiatives. First of all, AI provides quantified results — it will tell you the likelihood of specific outcomes, helps you understand the impact of certain attributes and factors, provides correlations between attributes and factors, and drives predictions and insights in an explainable way. Next, it’s multipurpose and  aligns to many business  problems, whether you are trying to solve a risk stratification problem, detect anomalies, or perform recommendations, for example. Finally, it’s a machine — you can process volumes of observations (data depth), volumes of attributes and factors (data breadth), and volumes of permutations, and it can be leveraged at speed and at scale. 

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