Now Next Later AI — Artificial Intelligence Strategy and Transformation

In today's rapidly evolving business landscape, companies are increasingly leveraging data to gain a competitive edge. As data-driven technologies like artificial intelligence (AI) and machine learning continue to advance, businesses that can effectively harness the power of data have a significant advantage over their competitors. In this blog post, we'll explore how companies can use data as a competitive advantage while also discussing the challenges and potential pitfalls they may face, using Stitch Fix as a cautionary tale.

Are you looking to adopt Artificial Intelligence in your organization? Do you have a risk management framework in place?
A portfolio approach can help companies successfully unleash the power of machine intelligence. And a well-balanced portfolio will include some quick wins, focused, for example, on the optimization of a touch-point, and some long-term projects transforming end-to-end processes.
The quick wins won’t transform your business, but they expose staff to the benefits and opportunities AI presents, and build confidence and momentum with key stakeholders like the board and management. For example, a quick win could involve a tool to schedule internal meetings, which allows you to use off-the-shelf packages, while you simultaneously build capability in areas such as hiring and training staff, large-scale data gathering, processing, and labeling.
Training staff and gaining AI capabilities over time is important, but to reduce risk and drive some momentum, we suggest that, instead of front-loading your costs, you scale them slowly and consistently, making use of off-the-shelf solutions (with suitable adaptations) to help keep costs manageable.
Data is without a doubt the key to machine learning projects, and the virtuous cycle of data collection means the rich get richer. So, a key risk of this new wave of transformation is the concentration of power in the hands of a few platforms. Knowing where to place your bets is tied to the data you can access that is of competitive advantage to your business.
Misconceptions can also be costly, with several studies suggesting that product-related AI innovation still struggles to deliver value. Knowing where to place innovation bets is less risky when management understands what types of innovations produce best returns.
Misconceptions also occur in regards to what to centralize and why, and related operating model choices. An AI Era business looks very different from an Internet Era one, and this transformation requires a considered approach and AI knowledge. The product-innovation focus and incrementality of the agile internet companies is replaced by strategic data acquisition, unified data platforms, end-to-end process automation and the adoption of new roles within the business.
Another misconception relates to expected job losses. Job numbers will remain about the same, but a rebalancing of roles will occur and you should expect to see fewer requirements for managerial roles as you scale AI within the organization.

Many of the best performing Machine Learning models are often highly opaque. Explainable AI is becoming a key requirement and in high demand. Machine Learning requires its own governance frameworks, with key lines of defense established right up front.
In addition, AI presents major ethical concerns related to privacy, bias, and discrimination. An organization must define the principles guiding their AI initiatives, ensuring they align with their values.

A 2020 global report from Harvard University evaluated 36 AI Ethics Frameworks from big companies, standards bodies, industry coalitions, and governments to identify eight common themes related to AI Ethics that you should consider:

Privacy, transparency and explainability are challenging for deep learning models.
AI presents tremendous opportunity and also high risks. The right risk management approach is critical, as the perils presented by AI differ significantly from previous waves of digital transformation.
A solid AI Risk Management Framework enables dialogue, understanding, and activities to manage AI risks, and responsibly develop trustworthy AI systems. It should address four functions:

Each broken down into specific actions and outcomes.

Are you looking to adopt Artificial Intelligence in your organization? Did you know that only one in ten companies generates significant financial benefits with AI?

A 2020 Global Executive Study and Research Project by BCG and MIT Sloan surveyed over 3000 businesses representing 29 industries and 112 countries. In this survey, 71% of the respondents said they had understood how AI will change the ways their organization will generate value. 59% said they had a strategy, and 57% were piloting or had already deployed AI solutions. 

The study found that while many companies are investing in artificial intelligence, only a small percentage can scale their AI initiatives and generate significant business value. Specifically, only 10% of companies get significant financial benefits from artificial intelligence technologies.

The study revealed that successful AI initiatives are typically accompanied by a rethinking of business processes and workflows, with the goal of optimizing them for AI-enabled automation, decision-making, and other capabilities. In fact, those engaging in extensive business process changes are 5 times more likely to realize significant financial benefit.

Process centered AI Transformation seems to be a critical component for benefits realization. Another study focused on AI and productivity by the University of Pennsylvania found that firms that use AI tools and are more oriented toward process improvement experience greater productivity gains. Where the lack of focus on process is likely to drive productivity losses.

The scale of the transformation is another key ingredient to high returns. A business just implementing AI in targeted applications is only 2% likely to achieve significant financial benefits. This percentage jumps up 19% once they invest in infrastructure, talent and strategy. Another 18% jump, once they are not only building solutions, but the business adopts them successfully. Solutions that incorporate AI in Digital Transformation use cases beyond just automation. But the real gains start when they scale AI broadly in embedded solutions, establish knowledge sharing between humans and AI, structure roles, and then continuously learn and adapt by growing the collective knowledge of the organisation.

However, the AI Transformation blueprint differs significantly from previous approaches to Digital Transformation. On a page, the words may look the same, but AI-led Business Transformation is nothing like previous waves of Digital Transformation, even concepts such as agility or innovation take a different shape in this new wave of change.

It's critical to understand both AI fundamentals and current market dynamics to design the right strategy, delivery, and operating model.
In this series, we’ll explore the ins and outs of how AI can drive value to your business, address current misconceptions, and provide you with insights on how to best ways to approach AI Transformation.

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