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Kenya case study: Driving innovative partnerships for the SDGs

Kenya’s impressive recent development trajectory has upgraded the country to a lower middle-income status. Yet access to concessional financing at scale remains a deep-rooted challenge, as traditional Official Development Assistance (ODA) is declining, which makes innovative public and private, national and international partnerships critical to deliver Sustainable Development Goals (SDGs) solutions at scale.

With the leadership of the empowered and independent UN Resident Coordinator in Kenya, supported by 26 UN agencies, a range of innovative partnerships for SDG delivery has been developed by leveraging all sources of financing to accelerate progress towards the SDGs, especially for those most left behind.

This case study showcases two areas where the Resident Coordinator has helped drive innovative partnerships for the SDGs: to advance health outcomes and to sustain peace.  

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Improving the lives of adolescent girls: a case study in rural and urban Kenya

Senior Associate in Kenya, Population Council

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Karen Austrian works for Population Council. She has led Population Council grants from the UK Department of International Development which funded AGI-K.

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Adolescent girls in Kenya face a range of challenges that compromise their ability to learn, earn and thrive. Girls who live in cash-poor environments are at risk of dropping out of school, sexual violence and early sexual initiation, early and unintended pregnancy, and early marriage.

It’s critical to intervene before events like this take place. An initiative in two marginalised areas of Kenya – the country’s largest slum Kibera in Nairobi and Wajir County in Northeastern Kenya – is attempting do just that through a combination of interventions intended to empower girls and keep them safe.

The initiative, called the Adolescent Girls Initiative - Kenya, began in 2014 and reached 6,000 girls aged 11-15. It involved the Population Council and African Population and Health Research Center. Save the Children and Plan International implemented the program in Wajir and Kibera.

It tested the effects and measured the financial costs of various interventions – from violence prevention and economic empowerment to health and education. The evidence emerging from the study is encouraging. Results show that positive changes for adolescent girls are possible in these two very different, marginalised settings. And it reinforces the point that context matters, and that interventions must be tailored to different settings.

The interventions

The project involved four interventions —- violence prevention, education, health and wealth creation —- aimed at addressing particular problems. Each was designed to deal with the particular circumstances of communities.

Violence Prevention: Groups of adult stakeholders participated in community dialogues on the challenges facing girls in their community. They were then given funding to develop and implement a plan to address at least one of those challenges.

In Wajir, for example, most of the challenges identified related to girls not enrolling in or dropping out of school and the communities wanted to improve the poor infrastructure in the schools. Therefore, most of the projects involved building classrooms, latrines, or pipe water into the schools.

In Kibera, the group created libraries or resource centres to respond to the girls’ need for a safe place to do homework.

Education: A cash transfer, conditional on school enrolment at the start of each term and regular attendance throughout the term, was made to cover schooling costs. Participating families received a bi-monthly incentive payment ($15 in Wajir; $11 in Kibera), direct payment of a portion of school fees (about $7 per term for primary school and $60 per term for secondary school), and a schooling kit for the girls that included sanitary pads, underwear, soap, a pen and a notebook.

Health: Weekly girls group meetings – called safe spaces – were held. These were facilitated by a young woman from the community and covered a range of health and life skills topics including sexual and reproductive health.

Wealth Creation: Financial education was provided in group meetings. In addition, savings accounts were opened in the urban site and home banks distributed in the rural site. A small amount (USD$3 per year) was also given as an incentive to save.

What worked

At the end of the two year programme, the project’s results show positive impact for girls across a broad range of health, social, educational, and financial indicators in both Kibera and Wajir, though results varied across both sites.

Education: The effects of the conditional cash transfers were positive. The point of impact depended on context. In Kibera, where enrolment in primary school was already close to 100%, cash transfers improved completion of primary school and transition to secondary school. But in Wajir, where only 75% of girls were enrolled in school at baseline, cash transfers served to increase primary school enrolment to 95%. This was a huge improvement clearly demonstrating the power of education cash transfers to bolster school enrolment in under-resourced and marginalised communities.

Health: The health intervention results were also different in the two regions. In Kibera, girls participating in the safe spaces groups were more likely to have improved knowledge, seek help and know how to use a condom and have social safety nets. Girls who actively participated in the safe spaces groups were also more likely to stay in, or complete, school.

But in Wajir, girls participating in safe spaces groups were not likely to know about sexual and reproductive health. This could be due to the socially conservative cultural norms in Wajir. However, participation in groups did lead to small improvements on girls’ belief in more equitable gender norms and self-efficacy, or a girl’s belief about her ability to succeed in a given scenario.

Wealth Creation: Financial literacy and savings improved in both regions. At baseline, less than 1% of girls reported having savings. For girls who received all four interventions (health, education, violence prevention, and wealth creation), the number rose to 42%. The combination of financial education sessions with savings mechanisms and $3 incentives confirms that having the opportunity to immediately put into practice the new skills helps the theoretical training to “stick.”

Violence Prevention: All groups received violence prevention, therefore, it was not possible to evaluate its community-based impact. However, researchers concluded that conversations on gender inequalities, education cash transfers and safe spaces in Wajir laid the groundwork for a shift in thinking about girls in the community. For example, after the intervention, community members increased their expectations that girls would complete secondary school and increased the age at which families expect girls to get married.

What’s next

Evidence from the project reinforces the theory that helping adolescent girls build social, health, education and economic assets through a multi-sectoral approach, involving communities and households, is more cost effective than any one singular intervention component – and leads to a larger impact and positive change on girls’ lives.

The design of future interventions for vulnerable girls needs to account for the context to maximise efficiency of spending resources –- particularly on education cash transfer programs.

An end line survey, will be conducted in mid-2019 to confirm if improvements in social, health, savings, and educational attainment for girls in the medium term will have a longer term influence on the timing and choices of marriage and sexual relationships. These results are expected in 2020.

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Bridging the digital divide in Kenya

The Kenyan education system received a shock when its government closed all schools and colleges nationwide in response to the first positive test of COVID-19 on 15 March 2020.

Nearly 70 per cent of children who attend school in Kenya live in rural areas, where most learning resources are scarce. While a quarter of Kenyan learners—especially those living in urban settings—were able to access virtual classes, the majority experienced significant challenges to continuing their education, predominantly due to poor connectivity.

We quickly worked with the Kenya Institute of Curriculum Development (KICD) to make OUP titles available via their Kenya Education Cloud , which provides all students nationally with high-quality educational content anywhere and anytime, and in online classes for KICD’s TV and radio stations. We also established new channels to make our learning materials digitally available to more children and parents, using platforms such as Snapplify, Ekitabu, and Juza.

However, this did not address the needs of the many children without reliable access to the internet so we partnered with the Standard Media Group to make our revision material available through their e-learning platform, Tutor-Soma . In addition to this, we provided free access to a number of our books as a result of the Kenyan government’s agreement with Google Loon—the implementation of “floating cell towers” through use of balloons, with just one balloon estimated to give coverage up to 200 times the reach of the average cell phone tower—to increase connectivity across the country.

Simultaneously, we worked with our distributors to deliver OUP books nationwide at an affordable price, ensuring that learners could still access the resources they needed at home, without having to go out to purchase them in the midst of the pandemic.

Recognizing that supporting teachers as they adapted to a new way of teaching, the Kenyan team also embarked on an extensive programme of virtual professional development for teachers on the new curriculum, topics of wellness, cybersecurity, and using digital skills when teaching during the pandemic. The programme proved to be extremely beneficial, enabling OUP to reach and support a staggering 86,000 teachers. Parents were also invited to join some Zoom calls to better support their children in learning from home.

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Case studies of success stories in Kenya’s agribusiness sector

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Gatonye, M. and Adam, R. 2022. Case studies of success stories in Kenya’s agribusiness sector. Nairobi, Kenya: Ukama Ustawi: Diversification for resilient agribusiness ecosystems in East and Southern Africa (ESA) and Resilient and Aquatic Food Systems for Healthy People and Planet (RAqFS) initiatives.

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Agribusinesses are an integral part of the Kenyan economy and, while facing many challenges, have shown to be adaptable to local and international conditions. These agribusiness case studies success stories provide insights into the constraints and opportunities of the sector. Most importantly, how CGIAR research centers can understand the different roles of women, men, and youth in agribusiness; highlight the constraints, barriers, and opportunities along the value chain; and exemplify the experiences and successes of seven agribusiness producers. Interviewees were selected based on attendance of the workshop and through referrals. The agribusiness case study success stories presented offer several lessons in opportunities, challenges, and solutions for agribusiness. Major common challenges are market access, transportation, and postharvest pricing. Market access has been exacerbated by the impact COVID-19, particularly international markets. Transporting produce to market— even to local markets—also poses challenges. Produce can spoil before reaching markets if not preserved for long distances, or if transported over poor roads. Low prices, especially from lower-priced government-subsidized goods, make it difficult for farmers to earn profits and reinvest in their agribusinesses. This can be offset to some degree by diversification, such as by growing various crops alongside apiculture or aquaculture, or by investing in other agrobusiness lines that yield regular income. Value addition, such as the use of cassava to make flour, chips, and crackers, is another avenue by which to enhance profits. Other challenges are lack of affordable or accessible essential tools and technologies, such as machines for weighing and sorting. Freezers are also needed; while there are low-tech preservation techniques, such as salting or icing, these are not optimal. Climate change exacerbates the difficulty of preservation, as higher temperatures mean produce spoils more rapidly. Climate change also contributes to drought and the spread of disease. Actual and proposed agribusiness solutions for some of these challenges are water reservoirs, crop diversification and rotation for soil health and disease control, trainings, and government-provided subsidies given directly to farmers—thereby bypassing current issues with cartels having access to these subsidies—to enhance access to inputs. In addition to these strategies, farmers have found other solutions that bring them together, such as by forming associations that provide trainings and workshops; sharing resources to farmer groups; helping to subsidize certain costs, such as seeds, to other farmers; and collective bargaining of market prices.

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Kenya Summary Case Study Report

Resource date: 01 Nov 2013

This summary report is based on a case study of the Kenya Joint Programme on Gender Equality and Women’s Empowerment (KJPGEWE). It is one of five case studies that form part of a wider Joint Evaluation of Joint Gender Programmes in the United Nations System, which was launched in May 2012.

The overall purpose of the joint evaluation is ‘to provide evaluative information for the strategic direction and use of joint gender programmes within the United Nations system reform process and support future policy and guidance on their design, implementation, monitoring and evaluation (M&E) for a more coordinated and effective United Nations system contribution to advance gender equality at the country level’.

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Inclusive Infrastructure Case Study Nairobi, Kenya

Nairobi is a city with a population of 4,397,073 people and the capital of Kenya. This case study explore the current state of the infrastructure provision - and makes recommendations for opportunities to embed accessibility and inclusion.

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Nairobi is experiencing rapid growth and investment in infrastructure which offers great potential to embed inclusion. The city has complex urban development challenges with roads and transportation commonly cited as major challenges. More than half of Nairobi’s residents live in the informal settlements in the city which are areas of high-density, poor-quality accommodation and lack basic infrastructure such as roads, water and sanitation infrastructure and power. Many persons with disabilities live in these communities due to reinforcing cycles of disability and poverty. However, there is vision and ambition in Nairobi to generally improve urban conditions. There is also a good policy basis to make progress towards disability inclusion. It is important these two agendas are coordinated.

Kenya has a strong legal framework to create inclusive cities and further disability inclusion and there is clear appetite from Government to take action represented through these legal frameworks. A major barrier for inclusive design delivery in the city is around good implementation, for which accountability and knowledge of inclusive design across project teams is important. Policy and practice stakeholders must be collaborative for more effective delivery.

The built environment is a vital part of creating access to AT and ensuring seamless use. In turn, AT must be designed to be fit for purpose for the environment and context of its use. Championing local production and local innovators in AT can help here, as there are working directly in the communities the AT is for.

Nairobi is a city of innovation - business opportunities and the start-up ecosystem is vibrant. Innovation must be inclusive and ensuring basic support and access to livelihoods must not be forgotten. 

An inclusive city is an accessible, healthy, resilient, gender-inclusive, agefriendly, child-friendly, sustainable city. Inclusive city aspirations intersect with many other global goals as set out in the UN’s 2030 Agenda and it is evident that disability and accessibility are cross-cutting issues across the SDGs. This is clearly seen in Nairobi where we see accessibility, health, climate-resilience and livelihood problems intersecting. Inclusive design is a tool for participatory urban development that can support action across diverse development targets and while delivering cobenefits through inclusive infrastructure that supports diverse disadvantaged groups, particularly due to the intersectionality of disability.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

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Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Why do women go through menopause? Scientists find fascinating clues in a study of whales.

The existence of menopause in humans has long been a biological conundrum, but scientists are getting a better understanding from a surprising source: whales.

Findings of a new study suggest menopause gives an evolutionary advantage to grandmother whales’ grandchildren. It's a unique insight because very few groups of animals experience menopause.

A paper published Wednesday in the journal Nature looked at a total of 32 whale species, five of which undergo menopause. The findings could offer clues about why humans, the only land-based animals that also goes through menopause, evolved the trait.

“They’ve done a great job of compiling all the evidence,” said Michael Gurven, a professor of anthropology at the University of California, Santa Barbara who studies human evolution and societies. “This paper quite elegantly gets at these very difficult issues.”

Whales might seem very distant from humans, but they have important similarities. Both are mammals, both are long-lived, and both live in family and social groups that help each other.

How long does menopause last? Menopause questions and concerns, answered.

Studying these toothed whale species offers a way to think about human evolution, said Gurven, who was not involved in the study.

In five species of toothed whales – killer whales, beluga whales, narwhals, short-finned pilot whales and false killer whales – the researchers’ findings suggest menopause evolved so grandmothers could help their daughters' offspring, without competing with them for mates.

Only daughters' offspring are aided because in these whales, while the males stay with their family group, they mate with females in other groups. But mothers do tend to give more support to their male offspring than to their female offspring.

Post-reproductive-age females help their family group in many ways. Off the coast of Washington state and British Columbia in Canada, grandmother killer whales catch salmon and "break the fish in half and share that catch with their families. So they're actively feeding their families,” said Darren Croft, a professor of behavioral ecology at the University of Exeter in the United Kingdom and senior author on the paper.

The whale grandmothers also store ecological knowledge about when and where to find food in times of hardship by using the experience they have gained over the lifetime of their environments.

“We see just the same patterns in (human) hunter-gatherer societies,” Croft said. “In times of a drought or in during times of social conflict, the people would turn to the elders of that community. They would have the knowledge.”

The 'grandmother hypothesis'

The researchers’ findings support what’s known as “the grandmother hypothesis .” It states that menopause is evolutionarily useful because while older women are no longer able to have children, they can instead focus their efforts on supporting their children and grandchildren. This means their family lines are more likely to survive, which has the same effect as having more children.

“What we showed is that species with menopause have a much longer time spent to live with their grand offspring, giving them many more opportunities for intergenerational health due to their long life,” said Samuel Ellis, an expert in human social behavior at the University of Exeter and the paper’s first author.

The difference in humans, Gurven said, is that both grandmothers and grandfathers contribute to the well-being of their children and grandchildren.

“In the human story, I think it’s multigenerational cooperation on steroids,” he said.

Though the study doesn’t prove once and for all that the grandmother hypothesis is the reason for menopause in women, it does lay out the evidence, he said. “It’s part of the story, but no one would say it tells the whole story,” Gruven said.

Does menopause lead to a longer life in humans?

There are two proposed pathways for how menopause evolved in humans: the live-long hypothesis and the stop-early hypothesis.

The live-long hypothesis suggests menopause increased total life span, but not how long a woman could have children. That leads to a prediction that species with menopause would live longer but have the same reproductive life span as species without menopause.

In the stop-early hypothesis, the theory is that menopause evolved by shortening the reproductive life span while the total life span remained unchanged. For this to be true, it would be likely that similar species without menopause would have the same life span as those that have menopause, but a shorter reproductive life span.

In looking at species of toothed whales that don’t have menopause and five that do, the researchers' findings make the long-life hypothesis seem most likely.

“This comparative work we’ve been able to do shows that females minimize this competition over reproduction by not also lengthening their reproductive period. Instead, they've evolved a longer lifespan while keeping a shorter reproductive life span,” Croft said.

This appears to be exactly what humans did.

“One of the striking features of this work is the fact that we find this really incredible and rare life-history strategy that we see human societies and in the ocean, but not elsewhere in mammal societies,” he said.

Whale study doesn't reflect men's life spans

The similarities with humans are not across the board, which is good news for men.

No one knows why in humans only females undergo menopause even though both sexes live to be approximately the same ages.

That’s not the case in some of these whales species, where male life spans are typically much shorter than those of females.

“In the killer whale population, for example, females regularly live into their 60s and 70s," Croft said. "The males are all dead by 40.”