The business of the future is adaptive

Manufacturing is in a state of flux. From supply chain disruptions to rising costs, tougher environmental regulations, and a changing consumer market, the sector faces a series of competing challenges.

But a new way of operating offers a way to tackle complexities head-on: adaptive production hardwires flexibility and resilience into the enterprise, drawing on powerful tools like artificial intelligence, digital twins, and robotics. Taking automation a step further, adaptive production allows manufacturers to respond in real time to demand fluctuations, adapt to supply chain disruptions, and autonomously optimize operations. It also facilitates an unprecedented level of personalization and customization for regional markets.

Time to adapt

The journey to adaptive production is not just about addressing today’s pressures, like rising costs and supply chain disruptions—it’s about positioning businesses for long-term success in a world of constant change. “In the coming years,” says Jana Kirchheim, director of manufacturing for Microsoft Germany, “I expect that new key technologies like copilots, small language models, high-performance computing, or the adaptive cloud approach will revolutionize the shop floor and accelerate industrial automation by enabling faster adjustments and re-programming for specific tasks.” These capabilities make adaptive production a transformative force, enhancing responsiveness and opening doors to systems with increasing autonomy—designed to complement human ingenuity rather than replace it.

These advances enable more than technical upgrades—they drive fundamental shifts in how manufacturers operate. John Hart, professor of mechanical engineering and director of MIT’s Center for Advanced Production Technologies, explains that automation is “going from a rigid high-volume, low-mix focus”—where factories make large quantities of very few products—“to more flexible high-volume, high-mix, and low-volume, high-mix scenarios”—where many product types can be made in custom quantities. These new capabilities demand a fundamental shift in how value is created and captured.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Enabling human-centric support with generative AI

It’s a stormy holiday weekend, and you’ve just received the last notification you want in the busiest travel week of the year: The first leg of your flight is significantly delayed.

You might expect this means you’ll be sitting on hold with airline customer service for half an hour. But this time, the process looks a little different: You have a brief text exchange with the airline’s AI chatbot, which quickly assesses your situation and places you in a priority queue. Shortly after, a human agent takes over, confirms the details, and gets you rebooked on an earlier flight so you can make your connection. You’ll be home in time to enjoy mom’s pot roast.

Generative AI is becoming a key component of business operations and customer service interactions today. According to Salesforce research, three out of five workers (61%) either currently use or plan to use generative AI in their roles. A full 68% of these employees are confident that the technology—which can churn out text, video, image, and audio content almost instantaneously—will enable them to provide more enriching customer experiences.

But the technology isn’t a complete solution—or a replacement for human workers. Sixty percent of the surveyed employees believe that human oversight is indispensable for effective and trustworthy generative AI.

Generative AI enables people and increases efficiencies in business operations, but using it to empower employees will make all the difference. Its full business value will only be achieved when it is used thoughtfully to blend with human empathy, ingenuity, and emotional intelligence.

Generative AI pilots across industries

Though the technology is still nascent, many generative AI use cases are starting to emerge.

In sales and marketing, generative AI can assist with creating targeted ad content, identifying leads, upselling, cross-selling, and providing real-time sales analytics. When used for internal functions like IT, HR, and finance, generative AI can improve help-desk services, simplify recruitment processes, generate job descriptions, assist with onboarding and exit processes, and even write code.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Pairing live support with accurate AI outputs

A live agent spends hours each week manually documenting routine interactions. Another combs through multiple knowledge bases to find the right solution, scrambling to piece it together while the customer waits on hold. A third types out the same response they’ve written dozens of times before.

These repetitive tasks can be draining, leaving less time for meaningful customer interactions—but generative AI is changing this reality. By automating routine workflows, AI augments the efforts of live agents, freeing them to do what they do best: solving complex problems and applying human understanding and empathy to help customers during critical situations.

“Enterprises are trying to rush to figure out how to implement or incorporate generative AI into their business to gain efficiencies,” says Will Fritcher, deputy chief client officer at TP. “But instead of viewing AI as a way to reduce expenses, they should really be looking at it through the lens of enhancing the customer experience and driving value.”

Doing this requires solving two intertwined challenges: empowering live agents by automating routine tasks and ensuring AI outputs remain accurate, reliable, and precise. And the key to both these goals? Striking the right balance between technological innovation and human judgment.

A key role in customer support

Generative AI’s potential impact on customer support is twofold: Customers stand to benefit from faster, more consistent service for simple requests, while
also receiving undivided human attention for complex, emotionally charged situations. For employees, eliminating repetitive tasks boosts job satisfaction and reduces burnout.The tech can also be used to streamline customer support workflows and enhance service quality in various ways, including:

Automated routine inquiries: AI systems handle straightforward customer requests, like resetting passwords or checking account balances.

Real-time assistance: During interactions, AI pulls up contextually relevant resources, suggests responses, and guides live agents to solutions faster.

Fritcher notes that TP is relying on many of these capabilities in its customer support solutions. For instance, AI-powered coaching marries AI-driven metrics with human expertise to provide feedback on 100% of customer interactions, rather than the traditional 2%
to 4% that was monitored pre-generative AI.

Call summaries: By automatically documenting customer interactions, AI saves live agents valuable time that can be reinvested in customer care.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Delivering the next-generation barcode

The world’s first barcode, designed in 1948, took more than 25 years to make it out of the lab and onto a retail package. Since then, the barcode has done much more than make grocery checkouts faster—it has remade our understanding of how physical objects can be identified and tracked, creating a new pace and set of expectations for the speed and reliability of modern commerce.

Nearly eighty years later, a new iteration of that technology, which encodes data in two dimensions, is poised to take the stage. Today’s 2D barcode is not only out of the lab but “open to a world of possibility,” says Carrie Wilkie, senior vice president of standards and technology at GS1 US.

2D barcodes encode substantially more information than their 1D counterparts. This enables them to link physical objects to a wide array of digital resources. For consumers, 2D barcodes can provide a wealth of product information, from food allergens, expiration dates, and safety recalls to detailed medication use instructions, coupons, and product offers. For businesses, 2D barcodes can enhance operational efficiencies, create traceability at the lot or item level, and drive new forms of customer engagement.

An array of 2D barcode types supports the information needs of a variety of industries. The GS1 DataMatrix, for example, is used on medication or medical devices, encoding expiration dates, batch and lot numbers, and FDA National Drug Codes. The QR Code is familiar to consumers who have used one to open a website from their phone. Adding a GS1 Digital Link URI to a QR Code enables it to serve two purposes: as both a traditional barcode for supply chain operations, enabling tracking throughout the supply chain and price lookup at checkout, and also as a consumer-facing link to digital information, like expiry dates and serial numbers.

Regardless of type, however, all 2D barcodes require a business ecosystem backed by data. To capture new value from advanced barcodes, organizations must supply and manage clean, accurate, and interoperable data around their products and materials. For 2D barcodes to deliver on their potential, businesses will need to collaborate with partners, suppliers, and customers and commit to common data standards across the value chain.

Driving the demand for 2D barcodes

Shifting to 2D barcodes—and enabling the data ecosystems behind them—will require investment by business. Consumer engagement, compliance, and sustainability are among the many factors driving this transition.

Real-time consumer engagement: Today’s customers want to feel connected to the brands they interact with and purchase from. Information is a key element of that engagement and empowerment. “When I think about customer satisfaction,” says Leslie Hand, group vice president for IDC Retail Insights, “I’m thinking about how I can provide more information that allows them to make better decisions about their own lives and the things they buy.”

2D barcodes can help by connecting consumers to online content in real time. “If, by using a 2D barcode, you have the capability to connect to a consumer in a specific region, or a specific store, and you have the ability to provide information to that consumer about the specific product in their hand, that can be a really powerful consumer engagement tool,” says Dan Hardy, director of customer operations for HanesBrands, Inc. “2D barcodes can bring brand and product connectivity directly to an individual consumer, and create an interaction that supports your brand message at an individual consumer/product level.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Readying business for the age of AI

Rapid advancements in AI technology offer unprecedented opportunities to enhance business operations, customer and employee engagement, and decision-making. Executives are eager to see the potential of AI realized. Among 100 c-suite respondents polled in WNS Analytics’ “The Future of Enterprise Data & AI” report, 76% say they are already implementing or planning to implement generative AI solutions. Among those same leaders, however, 67% report struggling with data migration, and others cite grappling with data quality, talent shortages, and data democratization issues. 

MIT Technology Review Insights recently had a conversation with Alex Sidgreaves, chief data officer at Zurich Insurance; Bogdan Szostek, chief data officer at Animal Friends; Shan Lodh, director of data platforms at Shawbrook Bank; and Gautam Singh, head of data, analytics, and AI at WNS Analytics, to discuss how enterprises can navigate the burgeoning era of AI.

AI across industries

There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings. However, with generative AI, these personalized offerings are elevated by incorporating tailored communication that considers the customer’s persona, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual handler might overlook, enhancing efficiency and maximizing recovery potential. Banking and financial services institutions are leveraging AI to bolster customer due diligence and enhance anti-money laundering efforts by leveraging AI-driven credit risk management practices. AI technologies are enhancing diagnostic accuracy through sophisticated image recognition in radiology, allowing for earlier and more precise detection of diseases while predictive analytics enable personalized treatment plans.

The core of successful AI implementation lies in understanding its business value, building a robust data foundation, aligning with the strategic goals of the organization, and infusing skilled expertise across every level of an enterprise.

• “I think we should also be asking ourselves, if we do succeed, what are we going to stop doing? Because when we empower colleagues through AI, we are giving them new capabilities [and] faster, quicker, leaner ways of doing things. So we need to be true to even thinking about the org design. Oftentimes, an AI program doesn’t work, not because the technology doesn’t work, but the downstream business processes or the organizational structures are still kept as before.”  —Shan Lodh, director of data platforms, Shawbrook Bank

Whether automating routine tasks, enhancing customer experiences, or providing deeper insights through data analysis, it’s essential to define what AI can do for an enterprise in specific terms. AI’s popularity and broad promises are not good enough reasons to jump headfirst into enterprise-wide adoption. 

“AI projects should come from a value-led position rather than being led by technology,” says Sidgreaves. “The key is to always ensure you know what value you’re bringing to the business or to the customer with the AI. And actually always ask yourself the question, do we even need AI to solve that problem?”

Having a good technology partner is crucial to ensure that value is realized. Gautam Singh, head of data, analytics, and AI at WNS, says, “At WNS Analytics, we keep clients’ organizational goals at the center. We have focused and strengthened around core productized services that go deep in generating value for our clients.” Singh explains their approach, “We do this by leveraging our unique AI and human interaction approach to develop custom services and deliver differentiated outcomes.”

The foundation of any advanced technology adoption is data and AI is no exception. Singh explains, “Advanced technologies like AI and generative AI may not always be the right choice, and hence we work with our clients to understand the need, to develop the right solution for each situation.” With increasingly large and complex data volumes, effectively managing and modernizing data infrastructure is essential to provide the basis for AI tools. 

This means breaking down silos and maximizing AI’s impact involves regular communication and collaboration across departments from marketing teams working with data scientists to understand customer behavior patterns to IT teams ensuring their infrastructure supports AI initiatives. 

• “I would emphasize the growing customer’s expectations in terms of what they expect our businesses to offer them and to provide us a quality and speed of service. At Animal Friends, we see the generative AI potential to be the biggest with sophisticated chatbots and voice bots that can serve our customers 24/7 and deliver the right level of service, and being cost effective for our customers. — Bogdan Szostek, chief data officer, Animal Friends

Investing in domain experts with insight into the regulations, operations, and industry practices is just as necessary in the success of deploying AI systems as the right data foundations and strategy. Continuous training and upskilling are essential to keep pace with evolving AI technologies.

Ensuring AI trust and transparency

Creating trust in generative AI implementation requires the same mechanisms employed for all emerging technologies: accountability, security, and ethical standards. Being transparent about how AI systems are used, the data they rely on, and the decision-making processes they employ can go a long way in forging trust among stakeholders. In fact, The Future of Enterprise Data & AI report cites 55% of organizations identify “building trust in AI systems among stakeholders” as the biggest challenge when scaling AI initiatives. 

“We need talent, we need communication, we need the ethical framework, we need very good data, and so on,” says Lodh. “Those things don’t really go away. In fact, they become even more necessary for generative AI, but of course the usages are more varied.” 

AI should augment human decision-making and business workflows. Guardrails with human oversight ensure that enterprise teams have access to AI tools but are in control of high-risk and high-value decisions.

“Bias in AI can creep in from almost anywhere and will do so unless you’re extremely careful. Challenges come into three buckets. You’ve got privacy challenges, data quality, completeness challenges, and then really training AI systems on data that’s biased, which is easily done,” says Sidgreaves. She emphasizes it is vital to ensure that data is up-to-date, accurate, and clean. High-quality data enhances the reliability and performance of AI models. Regular audits and data quality checks can help maintain the integrity of data.

An agile approach to AI implementation

ROI is always top of mind for business leaders looking to cash in on the promised potential of AI systems. As technology continues to evolve rapidly and the potential use cases of AI grow, starting small, creating measurable benchmarks, and adopting an agile approach can ensure success in scaling solutions. By starting with pilot projects and scaling successful initiatives, companies can manage risks and optimize resources. Sidgreaves, Szostek, and Lodh stress that while it may be tempting to throw everything at the wall and see what sticks, accessing the greatest returns from expanding AI tools means remaining flexible, strategic, and iterative. 

In insurance, two areas where AI has a significant ROI impact are risk and operational efficiency. Sidgreaves underscores that reducing manual processes is essential for large, heritage organizations, and generative AI and large language models (LLMs) are revolutionizing this aspect by significantly diminishing the need for manual activities.

To illustrate her point, she cites a specific example: “Consider the task of reviewing and drafting policy wording. Traditionally, this process would take an individual up to four weeks. However, with LLMs, this same task can now be completed in a matter of seconds.”  

Lodh adds that establishing ROI at the project’s onset and implementing cross-functional metrics are crucial for capturing a comprehensive view of a project’s impact. For instance, using LLMs for writing code is a great example of how IT and information security teams can collaborate. By assessing the quality of static code analysis generated by LLMs, these teams can ensure that the code meets security and performance standards.

“It’s very hard because technology is changing so quickly,” says Szostek. “We need to truly apply an agile approach, do not try to prescribe all the elements of the future deliveries in 12, 18, 24 months. We have to test and learn and iterate, and also fail fast if that’s needed.” 

Navigating the future of the AI era 

The rapid evolution of the digital age continues to bring immense opportunities for enterprises globally from the c-suite to the factory floor. With no shortage of use cases and promises to boost efficiencies, drive innovation, and improve customer and employee experiences, few business leaders dismiss the proliferation of AI as mere hype. However, the successful and responsible implementation of AI requires a careful balance of strategy, transparency, and robust data privacy and security measures.

• “It’s really easy as technology people to be driven by the next core thing, but we would have to be solving a business problem. So the key is to always ensure you know what value you’re bringing to the business or to the customer with the AI. And actually always ask yourself the question, do we even need AI to solve that problem?” — Alex Sidgreaves, chief data officer, Zurich Insurance

Fully harnessing the power of AI while maintaining trust means defining clear business values, ensuring accountability, managing data privacy, balancing innovation with ethical use, and staying ahead of future trends. Enterprises must remain vigilant and adaptable, committed to ethical practices and an agile approach to thrive in this rapidly changing business landscape.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.