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Death of the modern value chain and birth of the true digital revolution

Death of the modern value chain and birth of the true digital revolution
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We are constantly asked, “When are things getting back to normal?” There is no going back. The world is different and forever will be. Countless studies across a wide range of disciplines show that things will not go back to normal. In contrast, we are expecting more uncertainty, more volatility, more disruption than ever.

Every enterprise must see changes they have had to make in their value chain over the past 60 days as an opportunity, a first step towards a new existence that will increase their viability, adaptability, and survivability.

The COVID-19 pandemic has forced every enterprise to rethink their entire world: from supply chain, to HR, to meetings, to what “going to work” means. Consider this: Enterprises have seen use of virtual tools like Microsoft Teams, WebEx, and Zoom increase 300 percent to 775 percent in one month.

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Almost instantly COVID-19 created a volatile and uncertain world for business owners. Today’s scenarios have greater say than forecasts; real-time decisions are being made constantly, as opposed to well thought out plans. Through proper implementation of artificial intelligence (AI) in a business’s value chain, there will be a shift to a more stable and reliable “new reality.” We will see the development of a remote economy. Businesses will rely on ultra-granular forecasts resulting in more targeted offers for consumers, creating more resilient operations.

This is not about surviving one disruption. It is about learning to live and thrive in a new normal of constant change, volatility and uncertainty. But what steps must occur between now and then?

Utilize AI to anticipate scenarios in the short- and long-term

The pandemic repercussions on the economy are impossible to forecast. Business leaders not only have to estimate the trajectory of COVID-19, but also predict political actions their governments will take to manage it — and the ensuing impacts those actions might have on markets.

AI makes predicting such scenarios possible. That’s why companies who already implemented AI engines are better equipped to move quickly and manage their channels, products, supply chain, and procurement process. Scenarios that evolve daily challenge companies that are not positioned to make immediate, real-time business decisions.

Boston Consulting Group estimates that during the four previous global economic declines, 14 percent of companies managed to improve their sales growth and profit margins. We may not know exactly how the world will look post-crisis, but AI will play a role in determining the most resilient and successful enterprises.

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The current crisis has created a massive shock in demand and supply, disrupting historical patterns and companies’ ability to anticipate future trends. The key is to break down the timeline of the crisis into manageable phases — in this case, the current lockdown period, a transition period lasting until the outbreak is brought under control, and the new-normal environment that will follow. For the next 12 to 18 months, if not permanently, business leaders will have to manage heightened volatility divergence between categories, channels, and micro-markets. In this context, relying on long trends of internal data and managerial experience is no longer enough; leaders need to add external, real time data and early signals that anticipate the evolution of demand.

Employ AI tools to disrupt manufacturing operations and logistical challenges

Economic nationalism and increasing trade barriers have pushed companies to examine their supply chain strategies and reconsider the efficacy of redundancy. The pre-crisis business world featured optimizing cost and time as the central focus of global manufacturing operations, supply chains, and logistical maintenance, which contributed to a reliance on high-volume factories housed in one or two countries providing cheap production.

COVID-19 has exposed flaws in this design. When Chinese factories producing necessary components for automakers shut down, it brought the European automotive industry to a standstill. Such problems highlight the value of reducing reliance on single suppliers or countries and instead operating several smaller, efficient facilities at home.

Redundancy and duplication are costly, but AI fosters resilience in manufacturing processes and supply chains while minimizing impact on margins. Through use of manufacturing technologies like 3D printing and automated robots, AI allows reliance on fewer workers, as well as better planning, and can provide predictive maintenance across more factories closer to consumers. This enables enterprises to rely less on a few individual suppliers in low-wage nations. Thus, avoiding the predicament of today.

What’s more, AI tools are far more capable of identifying emerging trends and consumer patterns than any businessperson. Companies best utilizing AI capabilities can better predict upcoming consumer interests in addition to quick response to changing trends.

AI can respond to subjective tastes as evidenced by its applications in customization, as well as personalized products and experiences as evidenced by Amazon’s personalized recommendations. With businesses and consumers operating remotely, AI allows for personalized engagement and sales.

Apply AI solutions to entire business models

While AI’s capabilities in analyzing consumer trends are unrivaled, companies falter by limiting use of AI tools to just the product end of supply chains. Smarter organizations take AI’s impact a step further by integrating it into their business model. Post-crisis winners will be defined by those that put software, data, and AI at the center of their company. But, to transform a company in such a manner requires senior leadership to accept AI as integral to entire business models.

About 85 percent of companies believe AI will impact their industry, yet only 20 percent of those companies have AI tools operating at scale. This is in part because they failed to properly estimate what it takes to scale those AI solutions. They should expect to devote roughly 10 percent to creating algorithms, 20 percent to integrating AI with their legacy systems, then 70 percent towards getting their business’s leadership buy-in to fusing AI into their business practices.

This means allocating resources so leaders can devote their attention to the project full-time, supplemented by support of data scientists and senior leaders. It also means adjusting your data management infrastructure by bringing AI tools in-house and populating those databases and investing in making use of that data.

Finding success in the new reality

COVID-19 has accelerated attention paid to disrupting supply chain practices, but AI solutions were already evolving as our world became increasingly digitally transformed. The last 60 days have shown how quickly our manufacturing processes, our needs, and our way of life can change. 

The importance of agility and the ability to make real-time decisions no matter the scenario is paramount.

Transforming traditionally siloed organizations takes time and effort but results in a more resilient operation; therefore, companies that respond to the pandemic by further integrating AI capabilities into their business model will excel.

Sylvain Duranton, a senior partner and managing director at The Boston Consulting Group and the Global Leader of BCG GAMMA, contributed to this piece.

Mark Minevich is president of Going Global Ventures. He is a global digital cognitive strategist and artificial intelligence expert and venture capitalist. He serves as a senior fellow of U.S. Council on Competitiveness, digital fellow to CEO of IPsoft and was appointed as member of the B20 digital economy taskforce in the G20 Presidency. He was also recently appointed Chair of AI Policy at the International Research Centre for AI, under the auspices of UNESCO. Follow him on Twitter @MMinevich.