Privacy challenges are inherent, given AI’s need for data to function. Data fuels every stage of the AI lifecycle, from training to insight reports, and as firms scale their use of AI, they must confront the reality that value creation and data responsibility are now inseparable.  

Why AI and Privacy Considerations are Critical  

AI systems increasingly shape how organizations collect, process, and apply data. While these technologies offer significant opportunities, they also introduce complex privacy, legal, and ethical risks.  

As prompt injection remains a persistent threat and agentic browsers require broader access to sensitive files, robust system safeguards are more important than ever. Consumers and employees alike face heightened exposure when personal data intersects with AI systems, making strong privacy initiatives essential to maintaining trust and regulatory compliance. 

Consumers 

AI privacy initiatives are critical for protecting consumers, upholding ethical standards, and maintaining legal compliance. Without proper consent or transparent data practices, organizations risk significant fines and reputational harm. 

A prominent example is Clearview AI, which recently faced multi-district litigation for scraping publicly available photos to train their facial recognition AI. The database included up to 50 billion images taken from social media, websites, and other public sources. Because the system was used primarily by law enforcement, its practices raised deep ethical and legal concerns, eventually resulting in a $51.75 million settlement and numerous privacy law violation claims.  

Cases like this underscore why responsible data use is so important. As global regulatory scrutiny intensifies and consumers grow more privacy-conscious, companies are pressured to build trustworthy AI systems that protect individuals’ rights while advancing technology responsibly. 

Employees  

Privacy risks are equally critical within organizations. When employees use AI tools, especially large language or generative models, there is a risk of exposing confidential or proprietary information. For instance, if a worker inputs sensitive client data into an AI chat interface, that information may be stored, logged, or used in ways that increase the risk of unintended disclosure, depending on the system’s data policies and controls.  

While data leakage is not a new concern, the scale and speed at which information can now be shared with AI systems significantly amplifies the risk. 

A 2024 study found that about 8.5% of GenAI prompts contained sensitive information; nearly 46% involved customer data, and 27% involved employee information, with the remainder tied to legal, financial, and security data. These findings illustrate how easily private material can enter AI systems at scale. 

Additionally, the use of AI for employee surveillance and performance monitoring raises significant ethical concerns. Automated systems can perpetuate bias and unfair treatment, as illustrated by Amazon’s scrapped AI hiring tool, which reportedly discriminated against women. Beyond privacy implications, such biases erode employee trust and increase the risk of workplace inequality, reputational damage, and legal exposure. 

How To Ensure the Safe Use of AI  

From data scraping to accidental exposure of sensitive information and biased outputs, AI systems can introduce significant privacy and governance risks if not properly managed. To address these challenges, organizations must combine strong governance frameworks with privacy-preserving technical strategies. 

Firm-Wide AI Governance 

Safe AI use begins with clear, organization-wide policies. Effective governance defines which tools are approved, what types of data may be used, and which use cases are restricted or prohibited. These guardrails reduce the risk of confidential data leakage while empowering employees to use AI responsibly. 

Beyond tool approval, governance frameworks establish standards for compliance, oversight, and accountability. Clearly defined AI policies function as a form of risk management, guiding responsible and effective AI adoption across the organization.  

Organizations using AI for monitoring or performance analysis must uphold three non-negotiables: transparency about data practices (what data is collected and how it is analyzed), open communication with employees about how AI-driven insights may affect them, and robust data protection policies. 

Decentralized and Controlled AI Architectures 

Many privacy risks stem from excessive data centralization. While aggregating data in a single environment can simplify analytics and model training, it also raises the stakes for privacy, security, and regulatory compliance. A single breach or instance of misuse can expose information from multiple sources at once.  

Architectural strategies that minimize data movement help mitigate these risks. Federated learning, for example, decentralizes model training by keeping raw data on local devices or systems. Rather than transferring sensitive information to a central server, only model updates are shared and aggregated to improve a global model, thereby reducing exposure associated with large-scale data concentration. 

Similarly, on-device and edge AI shift model inference closer to the user. By processing data locally rather than transmitting it to external servers, organizations limit interception risks and strengthen user trust. 

Advances in smaller, more efficient models further enable decentralized deployment. Small language models (SLMs) require less computational power, making them practical for edge environments and enterprise systems. Together, these approaches reduce data exposure, narrow the attack surface, and support privacy-conscious AI implementation. 

Organizations may also choose to self-host AI models within their own infrastructure. As open-weight models near commercial-grade performance and operating costs continue to decline, enterprises gain a viable path to deploying in-house LLMs. Retaining control over hosting, fine-tuning, and post-training reduces third-party dependency and mitigates cross-border data risks, while providing greater visibility and control to support regulatory compliance. In this way, deployment strategy becomes a critical lever for balancing innovation with oversight. 

Differential Privacy and Mathematical Safeguards 

While architectural strategies limit how data moves, differential privacy protects information at the mathematical level. By introducing carefully calibrated statistical noise into datasets, training processes, or analytical outputs, it makes it extremely difficult to trace results back to any individual. 

Unlike traditional anonymization techniques, this approach provides measurable privacy guarantees. Organizations can extract valuable insights while significantly reducing the risk of exposing sensitive personal information. 

Differential privacy has been implemented by companies such as Apple (for usage pattern analysis), Google Maps (to estimate traffic and location popularity), and LinkedIn (for aggregated analytics). It’s particularly valuable in centralized data environments, where organizations must balance large-scale analytics with strong individual privacy protections. 

By embedding privacy directly into the mathematics of analysis, differential privacy demonstrates that innovation and confidentiality do not have to be mutually exclusive. 

The Bigger Picture 

Ultimately, AI privacy considerations aren’t just about data protection; they’re about sustaining trust, enforcing accountability, and ensuring ethical alignment between technology and human values. Keeping the bigger picture in mind, organizations that proactively embed privacy into their AI governance frameworks will not only comply with regulations but also safeguard their most valuable asset: public confidence. 

Ensuring that a firm is responsibly partaking in privacy-first initiatives is critical in all industries. The future of AI privacy will depend not only on policy frameworks, but on architectural design choices and privacy-enhancing technologies built directly into AI systems. 

To learn more about how your firm can ensure the safe use of AI, contact our team at Clarkston today.  

The post AI and Privacy: What’s Happening and What’s Next  appeared first on Clarkston Consulting.

In contrast to the volatility-driven messaging of prior years, this year’s presentations reflected something different: intentional recalibration. Leaders spoke about concentrating resources behind core brands, refining portfolios to compete in an increasingly polarized consumer economy, embedding AI into operating workflows rather than treating it as an experiment, elevating revenue growth management from a margin protection tool to a strategic growth lever, and modernizing distribution networks to capture share with greater precision. 

In total, the signal is clear: in a slower-growth environment, focus matters more than breadth. The winners will be those who align capital, capabilities, and operations around a smaller set of improved competitive advantages. 

6 CPG Strategic Priorities from CAGNY 2026

Below are six key themes from the conference that are shaping strategic priorities for CPG as we move deeper into 2026:

1. A Return to the Core: Depth Over Breadth

The center of gravity has moved back to the core portfolio.  

Across the industry, leadership teams are narrowing attention to the brands and platforms where they have durable competitive advantage. Non-core assets are being exited, SKU counts are coming down, and investment is concentrating behind brands that hold meaningful consumer loyalty and retailer leverage. The underlying assumption: complexity has crept too far ahead of consumer orientation. 

In a slower-growth environment, these power brands create greater strategic flexibility because they’re more efficient for investment in innovation and marketing. They travel better across channels and geographies, and importantly, renewed focus creates clearer alignment across these large complex organizations. 

However, this shift requires more than portfolio pruning. It requires a reassessment of the industry’s fixation on continuous optimization. Organizations cannot indefinitely do more with less while simultaneously managing broader portfolios and greater channel complexity.  

To realize value from this shift, organizations will need to realign structure, process, technology, and investment around a smaller set of true priorities and resist the pull back toward complexity. 

2. Preparing for a K-Shaped Consumer Economy 

The search for growth now focuses on both ends of the consumer spectrum with equal intentionality. 

Leaders described pressure in the middle of the market, while both value-oriented and premium segments continue to expand significantly. Households are trading down in some categories and trading up in others, often within the same basket. Sustained inflation, SNAP reductions, and channel shifting are real. So too is demand for indulgence, functionality, and premium experiences. 

Companies’ responses have gone far beyond discounting into deliberate portfolio re-design and focused investment. For example, smaller pack formats are protecting accessibility, club and value-channel presence is expanding, and at the same time, premium sub-lines, bold flavor extensions, and function-forward innovation are driving margin accretion at the top of the portfolio. 

Executing this balance requires more precision than expansion cycles of the past. Price pack architecture, channel-specific assortment design, elasticity modeling, and supply chain flexibility now sit at the center of strategy rather than at the edges. 

Sustaining performance in this environment will require organizations to design portfolios, pricing, and operating models that can flex across both ends of the income spectrum without eroding margin.

3. Innovation Anchored in Demographic & Consumer Shifts 

Innovation pipelines are becoming more population-led and less trend-driven. 

Across companies, product development themes reflect demographic and consumer market changes. The most cited examples were Hispanic-inspired flavors aligned with population growth, protein- and fiber-forward offerings responding to aging consumers, and clean-label and ingredient-conscious platforms aimed at younger households.  

Rather than chasing the latest trends, organizations are grounding innovation in more durable population trends and health behaviors. That shift requires stronger translation between insight teams and R&D, more disciplined prioritization of innovation resources, and deeper cultural sales and marketing competency. 

The companies that tie innovation to demographic reality are building resilience into the portfolio. Making this durable will require stronger investment and integration across analytics platforms, product lifecycle management, and cross-functional planning processes, ensuring that insight, R&D, and commercial teams are aligned around the same growth priorities. 

4. AI Embedded Into the Operating Model

The conversation around AI has matured from years prior. 

AI is no longer positioned as experimentation across every facet of the business. It has been mentioned in contexts where it is being embedded directly into the operating model. The primary applications repeated across organizations include accelerating innovation cycles in R&D (e.g. formula finding), scaling marketing content with greater targeting precision (e.g. ad generation), and strengthening forecasting accuracy and integrated business planning. 

Leaders are focused on speed, precision, and productivity. AI is increasingly integrated into existing core systems rather than deployed as stand-alone tech stacks. The priority is no longer isolated use cases but rather interoperability across ERP and other planning systems, supply chain infrastructure, and commercial processes. 

To sustain competitive advantage, organizations will need to invest not just in AI and other advanced analytical tools, but in data governance, systems integration, change management, and capability development that allow these technologies to become part of the operating culture rather than a new advanced capability. 

5. Revenue Growth Management as a Core Discipline

Revenue Growth Management has moved to the center of commercial strategy. 

In a flat to declining volume environment, precision matters. Leaders repeatedly described refined price pack architecture, strategic price calibration, promotion optimization, and more disciplined trade allocation. The objective is to stretch the promotional dollar further, in line with the “return to the core” strategy described above. 

RGM is increasingly the mechanism that allows portfolios to stretch both value and premium tiers simultaneously. It enables accessibility at the entry tier while sustaining pricing power where brand equity supports it. 

Executing this well requires stronger coordination between sales, finance, and analytics teams. It requires modernization of Trade Promotion systems and tighter integration across the ecosystem of core systems. It also requires clearer visibility into elasticity by channel and cohort utilizing advanced analytics, tighter trade governance, and improved forecasting accuracy. 

Sustaining performance will require the embedding of revenue management capabilities and discipline into everyday decision-making rather than treating it as a standalone team or periodic exercise.

6. Distribution and Channel Engineering as Competitive Edge

Distribution strategy has re-emerged as a source of competitive advantage.  

As opposed to conversations about assurance of supply, growth discussions center on how products reach the right channels with the right economics, whether through hybrid DSD and warehouse models, convenience expansion, away-from-home growth, or geographic whitespace. 

Companies are aligning pack formats, network design, service models, inventory placement, and pricing structures to the realities of each channel. As channel economics diverge in a “K-shaped economy”, route-to-market design and demand planning must evolve alongside them. 

Physical distribution scale still matters, but competitive advantage is increasingly defined by how well companies align their network design, supply chain flexibility, and channel-specific commercial strategies to serve each route to market effectively and profitably. 

Final Thoughts

The messaging from this year’s CAGNY conference suggest that the industry is entering a more disciplined era of growth. Rather than radical transformation or total reinvention, the largest CPG players are strengthening the foundations of their businesses. 

The common thread is focus. In a world of slower baseline consumption growth, consumer economic polarization, and sustained cost volatility, profitable growth will favor companies that can extract more from their core power brands. The next phase of competition will be defined less by breadth and more by precision in portfolio focus, in pricing, in channel strategy, in innovation targeting, and in capital deployment. 

For CPG leaders, the implications are significant: growth will be earned through sharper execution and integrated capabilities rather than expansion for expansion’s sake. Those who align portfolio, commercial discipline, digital enablement, and distribution will be best positioned to deliver consistent, compounding growth in the years ahead. 

The post 6 Key Themes Shaping CPG Strategic Priorities: CAGNY 2026 appeared first on Clarkston Consulting.

Download the full 2026 Pet Care Industry Trends Report here.

This free trends report outlines industry perspectives and expert advice from our team of consumer products consultants. You can view an excerpt of the report below, and if you’d like to discuss any of the trends or other challenges in the pet care space, connect with our team today.

Key Pet Care Industry Trends

As the pet care industry moves into 2026, shifting economic realities and consumer values are reshaping how pet owners spend. The growing influence of Gen Z pet owners, continued humanization of pets, and sustainability pushes are all shaping how pet care organizations develop their products. As these influences take hold, manufacturers need to consider how to appeal to each market segment.  

Further, as the market becomes more polarized in 2026, pet care companies should make clear efforts to demonstrate how their product delivers value. Investments in digital engagement, functional nutrition, value-based offerings, and sustainable packaging will be critical for capturing consumer spending. Organizations that align their strategies with these shifts will be better positioned for success.

Clarkston’s pet products consultants have highlighted the top pet care trends that businesses should consider and keep top-of-mind throughout the year:

1. Generational Preferences Driving Pet Owner Engagement Tactics
2. Tension Between Premium Preferences & Price Sensitivity
3. Rising Costs for Pet Experiences and Insurance Cutting into Product Spending
4. Sustainability & Recycling Impacting Packaging Decisions
5. Pet Food Recalls and Industry Responses

Trend 1:

Generational Preferences Driving Pet Owner Engagement Tactics 

Throughout 2025, Gen Z showed significant growth in pet ownership. They are more likely than any other demographic to have a wide variety of pets, including cats, birds, and reptiles, and on average spend more per month on their pets than other generations.  As Gen Z represents a growing share of the pet owner market, their preferences are increasingly shaping engagement strategies across the industry. 

Gen Z is more likely to shop online and through mobile apps, making strengthened omnichannel pathways essential. Seamless mobile checkout experiences and mobile-friendly payment options are critical to meeting their expectations.  

Social commerce also plays a central role in influencing purchase decisions for younger generations. One report shares that 97% of Gen Z customers report using social media as their primary source of shopping inspiration, making these platforms a necessary pathway for discovery and conversion. Enabling a seamless transition from social posts to product pages to purchase can help improve engagement and sales among this generation. 

Trust dynamics further differentiate this generation. More than half of Gen Z consumers say they trust influencer recommendations over traditional brand advertisements, creating opportunity for pet care brands to build credibility and loyalty through authentic partnerships.  

Looking to 2026 and beyond, companies that embed digital and social-first tactics into their broader omnichannel strategies will be better positioned to capture and retain this growing population of pet owners.  

Trend 2:

Tension Between Premium Preferences & Price Sensitivity 

In recent years, we’ve seen a rapid rise in the number of premium and ultra-premium pet product offerings, stemming from the continued push to humanize pets and make them a focal part of pet-owning families. More recently, as shoppers have tightened their budgets due to inflation and difficult economic conditions, we’re also seeing a shift in spending towards budget-tier items.  

This spending disparity can be seen through the disappearance of the middle consumer. Wealthy consumers continue spending more while middle- to low-income shoppers seek value-based products. This means pet care companies need to either focus on premium offerings or turn to budget goods and impactful discount strategies to win over the average consumer.  

Premiumization and Alternative Diets 

Premium offerings for pet food will continue to expand since transparency and “good for you” value drivers are core to human choices for their own diets, and as such, owners translate that mentality to their pets’ diets. High-income shoppers aren’t afraid to spend money on food and items they believe will improve their pets’ quality of life. Consumers who choose premium usually look for ingredients focused on health benefits like overall wellness, digestion, and pet life longevity, with an extra focus toward healthy, high-quality foods that are vet approved. 

We’ve seen large growth in fresh, dehydrated, air dried, and frozen foods, with refrigerated and frozen dog food alone growing sales by 13.4% (compared to -0.2% growth of the total dog food category). Additionally, kibble-plus, gently cooked, and personalized diets are on the rise. As with any growing category, these expanding options have led to an overall rise in the number of brands producing fresh food products, such as Freshpet expanding its premium line offerings of all-natural dog food and companies like General Mills and Nestlé Purina adding fresh pet food lines.   

As expanding options for premium food offerings become more mainstream, these pet food options will become an expectation for consumers. In 2026, brands that succeed will ensure food quality and continue to enhance their range of offerings.  

Budget-Friendly Products 

At the same time, 64% of US customers, 43% globally, cited “too expensive” as the main reason for not feeding alternative diets to their pets. Across the CPG industry, we’re seeing customers shift down in spending to search for value-based offerings; the pet industry is no different.  

Due to increasing inflation and economic uncertainty, discretionary consumer spending has been declining among non-high income consumers. This has strengthened the demand for budget-friendly pet products, often shifting consumer spending to private label, and made impactful promotional strategies a necessity. From 2024 to 2025, private label pet care unit sales grew 3.5% vs a 0.6% decline in unit sales for national brands, highlighting an opportunity to see success for brands who can best highlight their budget-friendly products. 

Strategies to Reach Price Sensitive Consumers 

Just as consumer budgets are tightening, so are marketing and trade budgets for companies. Every dollar spent needs to be stretched further. Due to this, many companies are employing new strategies to capture their shoppers’ spend, such as offering affordable entry points without abandoning premium positioning to drive value for the consumer.  

For example, consider how to focus on framing value as cost per day / per use and frame the long-term value of investment in higher-priced, premium pet products. Pet owners want to ensure the best quality of life for their pet and will be more willing to focus on the value over the price if it means providing risk-prevention and care. Additionally, almost all pet retailers are leveraging loyalty and subscriptions as value tools instead of direct price cuts, providing an opening for brands to align with retailer strategies to win on the shelf.  

Beyond promotional strategies, brands need to invest internally to ensure they are capturing useful data points and utilizing tools that can directly impact ROI and the bottom line. The best ways to increase internal impact is through quality data strategies and upgrading Trade Promotion practices through a new system or more effective business processes. 

Looking ahead, this divide between premium and budget pet food segments will continue to shape the industry in 2026. As the middle-ground market segment continues to shrink, brands need to choose a direction to stay relevant in one of the two prominent segments. Pet companies that choose to specialize in high-end, premium food products will need to prioritize transparency & education, quality, and expert-approved ingredients to appeal to the health-focused consumer. Meanwhile, budget-friendly offerings will need to emphasize cost-efficiency and strengthen brand loyalty to remain competitive with consumers that value affordability.  

Continue reading by downloading the full report below.

Download the Full 2026 Pet Care Industry Trends Report Here

Read last year’s Pet Care Industry Trends Report here.

Contributions from Natalie Pollock

The post 2026 Pet Care Industry Trends appeared first on Clarkston Consulting.

Download the Content Generation Agent for Pharma Case Study Here

Clarkston recently partnered with a leading biopharmaceutical company to revolutionize how they create marketing content for their products. Their marketing team was struggling with a slow and costly process for developing approved emails, digital banners, and other promotional assets. Every piece of content had to navigate complex medical, legal, and regulatory (MLR) reviews and expensive agency touchpoints. For example, a single banner ad could cost tens of thousands of dollars and take weeks to finalize. The company wanted a faster, more efficient way to generate compliant content on demand, especially for time-sensitive sales follow-ups, and without sacrificing the strict regulatory requirements of pharma marketing. 

To address this challenge, Clarkston developed an AI-powered content generation tool that automates the creation of draft marketing assets within minutes. The solution is built on a fully customized, secure web application that leverages a large language model on the backend. The application was developed to allow a diverse set of the client’s own approved materials and rules – including complex metrics and charts from core visual aids (CVA) and indication-specific compliance guidelines. Marketers can input what type of asset they need (such as a rep-triggered email or banner ad) and any context or messaging points, and the system will produce multiple tailored drafts instantly. Crucially, each draft comes out fully compliant, because the underlying prompt engineering enforces all the necessary medical disclaimer language, safety information, and brand style rules. 

This innovative tool has dramatically accelerated content production for the client. What used to take weeks of agency coordination can now be done in a day. The marketing team can quickly generate and iterate on several versions of an email or ad, then finalize the best one with minimal editing. By cutting out early external agency work, the client can save 50-75% of costs per asset. Ultimately, the content generation agent empowers the client to respond to market needs faster, experiment with messaging more freely, and focus their time on strategy rather than bureaucracy – all while staying confidently within regulatory guardrails. 

Download the Content Generation Agent for Pharma case study, and learn more about our AI Consulting Services by contacting us below. 

Contact Us to Learn More

Contributions by Ankush Garg

The post AI for MLR: Developing a Content Generation Agent for Pharma Marketing appeared first on Clarkston Consulting.

Neither announcement should come as a surprise. People have been using general-purpose AI tools for health questions for years, often without formal guardrails, integrations, or clarity on data handling. What’s new is that leading AI companies are now building health-specific experiences with clearer boundaries, deeper integrations, and more explicit attention to safety and privacy. 

So, what are the implications of ChatGPT Health for life sciences companies? These announcements feel inevitable, and they’re potentially transformative. They also carry tremendous risk. For organizations across the life sciences ecosystem, the question isn’t whether AI will play a role in health, but rather how, where, and under what conditions it should. 

Digging into the New Capabilities 

OpenAI describes ChatGPT Health as a dedicated space for health and wellness conversations that can, at a user’s discretion, connect to sources like medical records, Apple Health, and various wellness apps. The intent is to help people better understand their health data, prepare for appointments, and navigate information that is often fragmented across portals, PDFs, and devices. 

To build ChatGPT Health, OpenAI partnered with over 260 physicians to align the tool’s outputs with best practices surrounding “safety, clarity, appropriate escalation of care, and respect for individual context.” 

OpenAI is explicit that ChatGPT Health is not intended to diagnose or treat disease. It also highlights additional privacy controls, including keeping Health chats and memories separate from other conversations and stating that health data from this experience is not used to train OpenAI’s foundation models. 

Anthropic’s healthcare announcement takes a slightly different angle. Claude for Healthcare is positioned less as a consumer experience and more as a set of HIPAA-ready tools for healthcare and life sciences organizations. Anthropic emphasizes integrations with healthcare standards and data sources, support for clinical and administrative workflows, and expanded capabilities for areas like clinical trials and regulatory operations. 

While the products differ in emphasis, the direction is the same: AI for health is moving from informal experimentation to purpose-built tools with deeper integration into real-world systems. 

Implications for Life Sciences Companies 

For life sciences leaders, these announcements are signals, not just new products to watch. 

First, patient and HCP expectations are changing. As AI-generated summaries and insights become more common, clarity, speed, and personalization will increasingly become patient expectations. Medical affairs, patient support, and commercial teams will all feel this shift . Further, it is possible—even likely—that these tools will start to recommend specific HCPs for where to receive the “best” treatment.

Second, clinical development and regulatory operations are likely to see accelerating pressure to modernize. Anthropic’s focus on clinical trials and regulatory workflows underscores how quickly AI is moving into areas traditionally considered too complex or sensitive for anything other than direct human insight. 

Third, data governance and security become a strategic capabilities. When AI tools can connect directly to sensitive datasets, governance decisions influence not just compliance, but speed to value and organizational trust. The risk of sensitive data being leaked increases as it is integrated into new platforms, potentially resulting in legal action and a loss of trust. Ensuring data security is more important than ever before. 

Finally, disruption cuts both ways. Some revenue streams may compress, while others like functional medicine, diagnostics, and medical devices tied to continuous monitoring could expand as consumers pursue more proactive, real-time health optimization. These developments should be monitored closely, representing both threats and opportunities for life sciences companies. 

As organizations evaluate AI-enabled health tools, a few questions tend to separate thoughtful adoption from reactive experimentation: 

• What is the intended use, and what is explicitly out of scope? 
• Where does human oversight occur, especially for higher-risk outputs? 
• What data is connected, and what is the minimum necessary to deliver value? 
• How are security, privacy, retention, and access handled across vendors and partners? 
• How will errors be detected, measured, and addressed in real-world use? 
• How are users trained to avoid over-reliance on confident-sounding outputs? 

These questions are as much about culture and change management as they are about technology. Stakeholders across life sciences organizations, from executives to commercial teams to researchers to clinicians, need to not only answer these questions when considering the use of these tools and the integration of their data, but engrain their answers into their fundamental ways of working. 

High Potential… 

At a high level, the appeal is obvious. Healthcare generates enormous volumes of data, far beyond what any individual—patient or clinician—can realistically synthesize. Clinical studies, guidelines, lab results, imaging, data from wearable devices, physician notes, and patient-reported symptoms all live in different places, often with little context or accessible explanation. Done well, AI has the potential to integrate these disparate sources of data, identify connections, and break down complexity . 

For patients, that could mean clearer explanations of test results, better preparation for physician visits, and support for everyday health behaviors like nutrition, exercise, and sleep. OpenAI notes that health and wellness questions are already among the most common uses of ChatGPT, which suggests strong latent demand even before these features existed. 

For clinicians and healthcare organizations, the opportunity may be even larger. Anthropic highlights use cases around documentation, prior authorizations, coverage checks, and regulatory workflows—areas that consume enormous time but add little clinical value. Reducing friction here doesn’t just save money; it can meaningfully improve access and speed of care. 

One particularly promising area where this technology may be applied is within rare disease diagnostics. Rare diseases are frequently misdiagnosed due to symptom overlap with more common conditions coupled with more common and accessible treatments. The ability to consider information from numerous sources and identify patterns can lead to earlier, more accurate diagnoses that ultimately improve patient outcomes. 

…coupled with high risk 

Despite the potential benefits, it would be irresponsible to not also acknowledge the significant risks. 

One concern is self-diagnosis without appropriate medical oversight. Even when tools clearly state they are not providing medical advice, users may still treat personalized, confident responses as authoritative. Delayed care or false reassurance can have serious consequences. 

Another challenge surrounds data quality. AI systems can only reason over the information available to them from their training and from what is provided by the user. Incomplete medical records, inaccurate wearable data, or biased user inputs can all result in misleading conclusions. Because these models are probabilistic, there is no guarantee they will reference the “right” information every time, even when AI responses sound convincing.   

While it is a good step to consult with numerous physicians across diverse specialties as OpenAI did, this still represents a limited body of knowledge for tool evaluation and refinement. It would be encouraging to see OpenAI, Anthropic and others partner with research institutions and life sciences companies to bolster the systems’ inputs and conduct more extensive evaluation. Combining the vast knowledge of these institutions and companies with the technical capabilities of the companies on the cutting edge of AI will ultimately lead to much more powerful and reliable tools than any individual entity can develop on its own. 

Privacy is another major issue. Connecting medical records and personal health data raises the stakes dramatically. While OpenAI emphasizes additional safeguards in ChatGPT Health, experts have noted that consumer tools do not operate under the same regulatory frameworks as healthcare providers. Users may not fully understand where their data goes, how long it’s retained, or what protections apply. 

There is also the risk of harm at scale. Recent media coverage has highlighted cases where AI tools produced dangerous or inappropriate health guidance. These incidents are rare relative to total usage, but when stakes involve serious illness or mental health crises, even edge cases matter. 

Finally, there is a subtler but important concern around de-skilling. Some clinicians have raised alarms about trainees relying too heavily on AI tools instead of learning through mentorship, questioning, and experience. Using AI to augment expertise is very different from outsourcing judgment, and the line between the two can blur quickly. Numerous industries have already seen an over-reliance on AI tools, diluting labor force judgment and skill development. 

Final Thoughts  

For life sciences organizations, the right posture is neither uncritical enthusiasm nor resistance. It’s informed readiness: embracing the promise of AI while insisting on responsible design, clear boundaries, and human expertise staying firmly in the loop. 

As with AI more broadly, this is both a threat and an opportunity. How it plays out will depend far less on the models themselves, and far more on how thoughtfully we choose to use them. 

The post What ChatGPT Health Means for Life Sciences Companies appeared first on Clarkston Consulting.

Download the full 2026 Luxury Retail Trends Report here.

This free trends report outlines industry perspectives and expert advice from our team of retail consultants. You can view an excerpt of the report below, and if you’d like to discuss any of the trends or other challenges in the luxury retail space, connect with our team today.

Key Luxury Retail Trends

As the luxury retail industry moves into 2026, demographic change and rising expectations are pushing brands to rethink how they connect with shoppers. Winning brands are focusing on building relationships that feel personal and earned, whether through more thoughtful engagement or by elevating what the customer experiences at every touchpoint. Further, sustainability and ethical are becoming core to how luxury brands define themselves and how consumers decide where to place their trust and loyalty over time.

Clarkston’s luxury retail consultants have highlighted the top industry trends that businesses should consider and keep top-of-mind throughout the year:

1. Increasing Numbers of Gen Z & Millennial Shoppers
2. Hyper-Personalization: One-to-One Client Relationships
3. Exclusivity and the Experience Economy
4. Training & Store Associate Enablement as a Competitive Advantage
5. Sustainable Production
6. Brand Image & Reputation

Trend 1:

Increasing Numbers of Gen Z & Millennial Shoppers 

Millennials and Gen Z are projected to account for roughly 45% of the luxury retail market by the end of 2025, marking a meaningful shift in who luxury brands are selling to. As these younger consumers increase their share of spend, retailers are under pressure to adapt their strategies to align with different expectations around engagement, values, and how brands show up across channels. 

Brand loyalty (repeat purchases, advocacy, content engagement, etc.) is one characteristic amongst younger generations when it comes to selecting products. More than half of Gen Zers say they’ll purchase from a brand they love, which places a premium on experiences that build emotional connection over time. Experiential marketing plays a growing role here, particularly through immersive digital tools that feel personalized and interactive.  

Take, for example, augmented reality lenses and virtual try-on experiences – two ways that brands are creating moments that are both memorable and distinctly digital. Nyx Professional Makeup’s collaboration with Arcadia illustrates how this approach can come to life. The “Beauty Bestie” experience combines AR and AI to deliver personalized guidance and brand interaction through a digital lens. As digital natives, Gen Z shoppers are more inclined to engage with these types of experiences and, importantly, to form brand connections through them. Compared to older generations, younger consumers are more likely to translate digital engagement into purchase behavior and longer-term loyalty. 

Social media also continues to be central to how Gen Z and Millennials discover and evaluate luxury brands. Nearly half of luxury shoppers aged 18–34 use social platforms to research high-end purchases, making channel selection a critical component of marketing strategy. Influencer partnerships remain a powerful way to reach these audiences, particularly when the creator’s personal brand aligns authentically with the luxury house. Louis Vuitton’s collaboration with Emma Chamberlain is one example of how brands can tap into established communities and drive awareness and engagement through trusted voices.  

Beyond marketing, social platforms are increasingly functioning as direct commerce channels. Scrolling has evolved into shopping, with a meaningful share of fashion and beauty consumers purchasing luxury items directly through platforms such as TikTok and Instagram. This shift allows influencers and luxury retailers to sell directly to consumers in a way that feels seamless and intuitive. For younger shoppers in particular, social commerce creates a convenient, low-friction path from inspiration to purchase while keeping the brand experience rooted in the channels they use most. 

Trend 2:

Hyper-Personalization: One-to-One Client Relationships 

Personalization continues to be a powerful way for luxury retailers to differentiate their brands and deepen customer loyalty. Building personal relationships with shoppers, whether through remembering individual preferences, acknowledging milestones like birthdays, or tailoring offers based on prior purchases, helps create a sense of recognition that luxury consumers increasingly expect. 

Historically, personalization in luxury retail relied on broad customer segments to approximate preferences. Today, it has evolved into a more precise, data-driven strategy that enables brands to engage shoppers on an individual level.  

Digital tools now allow retailers to anticipate preferences and surface relevant products in a more tailored way. Vuori’s “Personalized Picks for You,” for example, curates product recommendations based on shopper behavior, while Alo uses email personalization to re-engage customers who have abandoned carts. The brand also captures purchase history and stated preferences to inform personalized marketing and targeted advertising. These approaches help create shopping experiences that feel more relevant and intentional, strengthening long-term brand relationships. 

Product customization is another extension of one-to-one engagement, giving shoppers the ability to shape luxury items around their personal tastes. Burberry Bespoke allows shoppers to customize every aspect of their product from fabric and lining to buttons and embroidery. Meanwhile, Cartier allows shoppers to engrave their jewelry with custom engravings. Experiences like these can foster a deeper emotional connection, as customers receive products that feel uniquely theirs and reflective of their individual style.  

Technology continues to play a growing role in enabling personalized luxury experiences. Estée Lauder’s Virtual Foundation Tool uses a customer’s phone camera to help identify the best shade of Double Wear foundation, removing friction from the discovery process. Gucci’s remote shopping experience, Gucci Live, brings high-touch service into a digital environment by offering personalized video appointments with real advisors, complete with close-up product views and interactive conversation. Integrating technology in this way allows luxury brands to deliver service that feels both seamless and highly attentive. 

Looking ahead, continued advances in technology will unlock even more sophisticated personalization strategies. Retailers that invest in tech-enabled, real-time personalization will be better positioned to anticipate customer needs and respond in the moment, creating differentiated experiences that support long-term loyalty well beyond the initial transaction.

Continue reading by downloading the full report below.

Download the Full 2026 Luxury Retail Trends Report Here

Read last year’s Luxury Retail Trends Report here.

Contributions from Natalie Pollock

The post 2026 Luxury Retail Trends appeared first on Clarkston Consulting.

Economic pressure on consumers, however, is only one half of the picture. In addition to preventing products from being too expensive, brands need to understand the values consumers seek to align themselves with. Entering 2026, wellness and functional ingredients have emerged as key trends in the food industry, and the beverage industry similarly focused on simplified items, protein, and sustainable packaging. These considerations go beyond pricing and require products geared toward consumer lifestyles.  

How Food Brands Are Rethinking Pricing

Clarkston Consulting partner and consumer products industry lead Steve Rosenstock highlighted the need to balance pricing and value as he explored the implications of Pepsi’s price reductions. Below are the key takeaways for food and beverage companies: 

1. Affordability Remains a Priority

Product affordability is a necessary component of meeting consumers where their needs lie. To address concerns about persistent inflation, key players in the industry have been cutting prices as a signal to consumers. Pepsi set the stage for this trend with price reductions for brands like Doritos and Cheetos, aiming to boost competitiveness by appealing to customers. 

General Mills’ decision to reduce prices on nearly two-thirds of its North American grocery portfolio reflects a broader effort to protect volume and remain accessible to cost-conscious customers. Target’s grocery prices demonstrate a similar trend on the retail side, as the brand leveraged discounting to support shoppers during the holiday season. These strategic moves are critical to stabilizing short-term demand while protecting customer loyalty. 

2. Relevance Beyond Price

Ensuring affordability is the first step toward long-term brand strength, which is increasingly driven by alignment with consumer values. Customer preferences are constantly evolving, but brands that track interest in shifts like better-for-you options will be positioned to adapt.  

In recent years, the food and beverage industry has seen a clear migration toward product offerings that focus on wellness outcomes, resulting in substitutions away from traditional snacks in favor of healthier options. Regulatory pressure also contributes to these trades, with the FDA’s red dye ban leading brands to promote natural alternatives. As functional ingredients take center stage, companies will continue to reposition products to appeal to relevant consumer demands. 

3. Strategic Portfolio Adjustments

This combination of price cuts and product adjustments requires additional portfolio recalibration for food and beverage companies. Simplifying assortments and reallocating resources enables organizations to steer their focus toward high-growth or more relevant offerings. By implementing these strategies for long-term success, brands position themselves to invest more in product innovation as well.  

Making deliberate tradeoffs away from lower-velocity SKUs and legacy offerings creates operational flexibility, which is key when companies are looking to reposition products or launch new items. Between trends regarding fiber, protein, and products that improve emotional-wellbeing, food and beverage companies can seize opportunities to differentiate and maintain relevance.   

Getting Started 

Value creation today involves an integrated approach that rebalances pricing and cost with consumer demand. For companies to adapt to market trends, affordability is crucial to appeal to customer concerns about inflation. However, to truly win consumer demand, brands need to create a deeper connection between product offerings and shopper lifestyle goals.  

Once the question of cost is answered, consumers seek to purchase items that improve their health, mood, and daily needs. By integrating these factors strategically into portfolios, food and beverage companies can prepare for long-term growth and innovation. 

To learn more about how your business can strategically meet the needs of today’s consumers, contact Clarkston today. 

Contributions from Hannah Yang 

The post How Food Brands Are Rethinking Pricing and Value to Meet Today’s Consumer appeared first on Clarkston Consulting.

Echo vs. Impact 

Leaders who rely solely on formal reporting and project calendars are often late to recognize and respond to risk during an ERP implementation. Effective organizations are those that pay attention to behavioral and structural indicators before schedules slip, integrations fail, or downstream testing degrades.  

ERP project risk rarely begins with missed milestones or red status reports; it develops earlier and is revealed through changes in how teams communicate, how they make commitments, and how decisions are made across business and technology stakeholders.  

Early signals are the echo of future failure, while missed deadlines, poor data quality, failed testing cycles, and disrupted cutovers are the impact. When early indicators are ignored, risk accumulates quietly until it surfaces in delivery metrics, often when recovery options are constrained and trade-offs become unavoidable. Detecting early ERP project risks requires understanding which signals tend to appear first and how routine ERP project behaviors reveal underlying issues.  

Clarkston recommends a tiered risk framework based on observed warning signs across ERP programs. The tiers reflect patterns commonly seen in ERP implementations that later experience delivery challenges, while the examples and guidance draw from practical ERP project leadership experience. Together, they provide a clear, actionable perspective on how ERP risk develops and how it can be identified early, before it appears in formal status reporting.  

Tier 1: Earliest and Most Dangerous Signals (Act Immediately) 

Tier 1 signals are predictive, behavioral, and quiet. They appear early, often quietly, and always cascade into later failures if left unaddressed. When Tier 1 is present, the project is already at risk, even if all formal indicators remain green. 

Tier 2: Structural Breakdown (Still Recoverable) 

Tier 2 signals are visible but often normalized or explained away. At this stage, recovery is still possible, but only if the signals are acknowledged and addressed. Tier 2 signals indicate that the project is compensating for unresolved issues rather than addressing them directly. 

Tier 3: Lagging Confirmation Signals (Harder to Fix) 

Tier 3 signals confirm issues that have been developing for weeks or months. When these indicators appear, the project has moved from risk prevention to damage control, and recovery requires explicit tradeoffs. Tier 3 signals rarely appear in isolation; they are the visible outcome of earlier Tier 1 and Tier 2 signals that went unaddressed. 

Looking Ahead  

Early risk detection is about recognizing the signals that appear before outcomes are locked in. Across ERP programs, Clarkston has repeatedly observed these patterns and intervened early, often well before traditional metrics indicated risk, allowing teams to reset expectations, resolve constraints, and preserve delivery options.  

The post Detecting Early ERP Project Risk Signals appeared first on Clarkston Consulting.

Download the full 2026 Grocery Trends Report here.

This free trends report outlines industry perspectives and expert advice from our team of retail industry consultants. You can view an excerpt of the report below, and if you’d like to discuss any of the trends or other challenges in the grocery space, connect with our team today.

Key Grocery Trends

These grocery trends point to a common theme: maturity. Across AI, ecommerce, retail media, and enterprise technology, success is no longer driven by experimentation alone but by disciplined execution and integration. In 2026, winning grocery retailers will be those that simplify their strategies, invest in strong foundations, and align innovation with real customer and business needs.

Clarkston’s grocery industry consultants have highlighted the top grocery trends that businesses should consider and keep top-of-mind throughout the year:

1. From Experimentation to Embedded: How AI Will Reshape Grocery Retail in 2026 … AI is becoming embedded in everyday operations, requiring strong data foundations, governance, and clear prioritization of use cases.
2. Online Grocery Scales as Competition and Execution Intensify …  eCommerce continues to scale, and retailers are refining fulfillment models and partnerships to balance growth with profitability.
3. Retail Media Networks: The New Growth Engines  … Retail Media Networks are emerging as meaningful revenue contributors, yet demand operational rigor, consistent measurement, and trust with both shoppers and brands.
4. Improving Technology Across the Organization in 2026 … At the enterprise level, technology investments are increasingly evaluated based on how well they connect across the organization. Siloed tools and fragmented systems will limit impact in an environment where speed, efficiency, and insight are critical. 
5. Macro Forces and Consumer Preferences … Underlying all of this is a consumer who remains value-driven, health-conscious, digitally influenced, and selective.

Trend 1:

From Experimentation to Embedded: How AI Will Reshape Grocery Retail in 2026

In 2025, AI adoption expanded rapidly, accompanied by growing realism about what it takes to move from pilots to consistent, scalable business impact. While confidence in AI’s long-term importance remained high, many organizations struggled to translate pilots into consistent business impact. Executives agree generative AI will eventually transform their businesses but are reconsidering the pace of adoption. This sentiment was echoed at Groceryshop 2025, where industry leaders acknowledged the need to shift from experimentation toward building the foundational systems, processes, and operating models required to unlock value at scale. 

For grocery retailers, this disconnect was clear. Many pursued AI experiments across forecasting, pricing, personalization, and labor planning, while simultaneously highlighting gaps in organizational readiness and deployment maturity. Companies across industries struggled to move AI beyond pilots due to integration complexity, inconsistent data readiness, governance gaps, and talent shortages. The key challenge is bridging experimentation with operational value: retailers are increasingly asking not whether to use AI, but how to prioritize use cases and close systemic gaps to improve business outcomes. 

By late 2025, AI adoption in grocery retail was increasingly moving beyond isolated experiments toward deeper integration within core business processes – AI transitioned from a promising tool to embedded infrastructure in how businesses operate. Innovations like Instacart’s instant checkout powered by ChatGPT demonstrate how AI is becoming embedded in both customer-facing experiences and operational workflows, signaling a shift to practical applications at scale.  

AI is also enhancing end-to-end supply chain unification at retailers through more accurate forecasting, greater visibility into in-store inventory, increased productivity in the warehouse, and informed optimization of logistics management. This transition is supported by technical progress. Advances in reasoning, benchmarking, and multi-step decision support make AI more suitable for complex decision-making, not just automation. Looking ahead to 2026, industry outlooks underscore a pragmatic approach, prioritizing which processes generate measurable ROI from AI and what levels of human oversight are appropriate for each use case.  

Ultimately, competitive advantage will come from execution maturity, not experimentation volume. While 2025 was defined by AI optimism, 2026 will favor leaders who start with strong infrastructure foundations and embed intelligence into core workflows, supported by strong data foundations, change management, and clear governance. 

Trend 2:

Online Grocery Scales as Competition and Execution Intensify 

Grocery eCommerce and omnichannel strategies are entering a more competitive and consequential phase heading into 2026. Online grocery continues to be a primary growth engine for the retail sector, with delivery and pickup gaining share globally as consumers prioritize convenience and speed. In the U.S., online grocery sales reached recurring monthly highs throughout 2025 attributed by increased penetration and higher order frequency. Looking ahead, online grocery is expected to represent a 20% share of total U.S. ecommerce, driven by improved technology integration and evolving consumer expectations. 

Platform strategies are also reshaping the competitive landscape. DoorDash has significantly expanded its grocery footprint by adding national, regional, and local grocers, while deepening partnerships that extend full grocery assortments to millions of households nationwide. The launch of a grocery shopping experience within ChatGPT highlights the next frontier of eCommerce ordering, reshaping how shoppers discover and purchase groceries. At the same time, Amazon’s continued push into same-day grocery delivery is raising service expectations and intensifying competitive pressure across the sector. 

Retailers are also reassessing fulfillment strategies in pursuit of profitability. Kroger’s decision to close certain automated fulfillment centers while reinforcing partnerships with third-party delivery providers reflects a broader industry reset toward capital efficiency and margin improvement. Similarly, Ahold Delhaize has reported progress toward eCommerce profitability, demonstrating that disciplined omnichannel execution can balance growth with financial performance. 

Despite continued growth, retailers enter 2026 facing ongoing pressure to drive eCommerce profitability. Emerging technologies, including conversational commerce, voice ordering, and personalization, present opportunities to improve conversion and engagement, but long-term success will depend on how effectively retailers prioritize implementation aligned with business size, budget, and customer needs. 

Continue reading by downloading the full report below.

Download the Full 2026 Grocery Trends Report Here

Read last year’s Grocery Trends Report here.

The post 2026 Grocery Trends appeared first on Clarkston Consulting.

Download the Selecting an Enterprise PLM Platform Case Study Here

As a global leader in water management and treatment solutions, this client delivers innovative products across pool and spa equipment, residential water solutions, and commercial and industrial water systems. While the organization maintained a strong market position, its product lifecycle management (PLM) capabilities were constrained by an internally developed, SharePoint-based system and a collection of offline tools. Over time, these platforms created inconsistent lifecycle governance, limited end-to-end visibility, and fragmented product data with poor traceability—introducing scalability challenges and long-term risks to business continuity. 

Recognizing the need for an enterprise PLM platform to support future growth, data transparency, and customer-centric operations, the client partnered with Clarkston to lead a structured request for proposal (RFP) and vendor selection initiative. The engagement was designed to ensure an objective, requirements-driven evaluation while establishing a clear foundation for implementation readiness. 

Over a 12-week period, Clarkston developed and executed a comprehensive PLM RFP strategy, including the creation of vendor briefing materials, evaluation scorecards, response analysis tools, and scenario-based demonstration scripts tailored to the client’s highest-priority business needs. Clarkston managed vendor communications, facilitated a competitive evaluation process involving five vendors, and guided the client through a shortlisting to three finalists for in-depth demonstrations. Three full-day demonstration sessions were conducted using standardized scenarios to enable meaningful, side-by-side comparisons. 

Following the demonstrations, Clarkston aggregated evaluator feedback and delivered a data-driven recommendation supported by a comprehensive analysis and final report. In parallel, Clarkston defined strategic proposals for PLM data governance, go-live scope, and organizational change management, along with a high-level implementation roadmap. 

As a result, the client confidently selected an enterprise PLM platform aligned to its long-term transformation objectives to enter the implementation phase with clarity, alignment, and momentum—positioning the organization to strengthen product governance, improve visibility across the lifecycle, and scale PLM capabilities enterprise-wide. 

Download the Selecting an Enterprise PLM Platform case study, and learn more about our PLM Consulting Services by contacting us below. 

Contact Us to Learn More

The post Selecting an Enterprise PLM Platform to Enable Product Lifecycle Transformation appeared first on Clarkston Consulting.

Download the full 2026 Laboratory Informatics Trends Report here.

This free trends report outlines industry perspectives and expert advice from our team of lab informatics consultants. You can view an excerpt of the report below, and if you’d like to discuss any of the trends or other challenges in the laboratory space, connect with our team today.

Key Laboratory Informatics Trends

The laboratory informatics trends of 2026 highlight the increased use of AI and ML in both positive and negative ways, Smart LIMS concepts, the opportunities of a Lights-Out laboratory, enhanced interoperability, and increased security measures. The benefits from these trends are vast as they allow for increases in compliance and data integrity while operating more efficiently, but they should be implemented judiciously to ensure potential risks are mitigated.  

Clarkston’s laboratory informatics consultants have highlighted the top lab informatics trends that businesses should consider and keep top-of-mind throughout the year:

1. The evolution from LIMS to Smart LIMS 
2. Robots and Cobots pave the way for Lights Out Labs 
3. Interoperability Standards 
4. Cybersecurity: An Operational Strategy

Trend 1: 

The evolution from LIMS to Smart LIMS 

Laboratory Information Management Systems (LIMS) has been a cornerstone of laboratory operations for decades. Laboratories have relied on these systems for sample tracking, data output, product quality, and regulatory compliance. A Smart LIMS incorporates a wide use of Artificial Intelligence (AI) and Machine Learning (ML), which has become critical to laboratories for predictive analytics and real-time data interpretation, enhancing operational performance. Agentic AI can proactively respond to deviations, failures, and inconsistencies, saving valuable time and money for an organization. 

A Smart LIMS that involves Agentic AI can allow a LIMS to modify its workflow based on patterns of results across large datasets, optimizing laboratory functions. Automating instrument communication bidirectionally with a Smart LIMS can predict instrument maintenance, preventing costly downtime and potential delays to market. The real-time data analysis that can predict failures and assess impacts before they happen ensures the product is of the highest quality without constant human monitoring.   

Trend 2:

Robots and Cobots pave the way for Lights Out Labs 

In laboratory informatics, the use of automation for laboratory functions are growing rapidly.  There are many benefits to automation – reduction of human error, increased efficiency of operations, and integrations for seamless communication across instruments and systems.   

Automation has been used in science since 1875, slowly progressing until the 1950s when the first standalone automated analyzer and automatic titrators in a laboratory were used.  Soon after, in 1961, Robotics were introduced to manufacturing, and are used in laboratories today to transfer materials and samples to another location, or to provide liquid handling with precision.   

Cobots are being introduced as human assistants, making the laboratory safer and more efficient.  These cobots can safely handle hazardous chemicals, automate workflows that are often lengthy to set up, and aid in routine tasks such as sample preparation. When integrated with a laboratory informatics platform, there is full traceability of each action.  

In 2026, laboratories are trending towards an End-to-End (E2E) or Lights-Out Lab to operate unattended by humans, using robots and cobots to continue processing.  The advantages of implementing advanced automation technologies are handling higher throughput with around the clock operations, increased consistent repeatability, and reduced risk of injury to laboratory personnel – all while maintaining regulatory compliance. 

Continue reading by downloading the full report below.

Download the Full 2026 Lab Informatics Trends Report Here

Read last year’s Lab Informatics Trends Report here.

The post 2026 Lab Informatics Trends appeared first on Clarkston Consulting.

In the data-rich landscape of retail, KPI best practices establish a foundation to help cut through the noise and provide focus. KPIs create consistency across reporting tools and analytics teams, and they bridge the gap between long-term strategy and near-term execution. Without KPIs, retailers lack a clear definition of success.  

In our experience, we’ve seen retailers rely on instinct instead of insight, leading to misaligned decisions, reactive firefighting, and inefficiencies across the value chain. This leads to an overall lack of objectivity in tracking performance. With retailers moving deeper into AI-enabled analytics ecosystems, selecting and defining the right KPIs is more important than ever. 

Best Practices for Selecting & Defining KPIs 

The following methods and tools can help drive efficiency and effectiveness when picking KPIs. 

1. Map KPIs directly to strategic objectives: KPIs should not be created just because the data is available. Each KPI needs to connect directly back to a business goal. Before building a KPI, make sure the strategic objectives are clearly understood and agreed on. Identify the relationship between the initiative and the metric being tracked. For example, if the strategic objective is to reduce operational costs, a relevant KPI might be Cost per Order. If the goal is to improve customer satisfaction, an organization may track the Net Promoter Score (NPS). 
2. Keep KPIs simple and unambiguous: Everyone should understand what a KPI means and how it is calculated. Use standard definitions where you can and clearly call out any nuances in the calculation. Because KPIs are used across multiple teams, clarity helps reduce confusion and keeps everyone aligned, enabling more efficient training and adoption. For example, if you are tracking return rate, be specific. Does that include damaged items? Is it % of units returned or % of sales dollars returned? Ensure there is little to no room for ambiguity. 
3. Balance leading and lagging indicators: Lagging indicators measure what has already happened and are useful for understanding outcomes. Leading indicators help predict what will happen and allow teams to be proactive. A good KPI set uses both. Lagging indicators tell the story of past performance, and leading indicators help teams intervene before issues escalate. Examples of leading indicators include Demand Forecast Accuracy, Add to Cart Rate, and Allocation accuracy. Examples of lagging indicators include Sales/Sales Growth, Revenue, and Market Share.  
4. Establish consistent data definitions and calculation logic: When defining KPIs, make sure the rules and calculation logic are clearly documented. This includes identifying source systems, aggregation rules, population filters, and time windows. Keep these definitions consistent across all reporting so teams work from the same version of the truth. In retail, a common KPI is the Sell-Through Rate. One team may calculate sell-through using units sold divided by units received, while another calculates it using units sold divided by beginning inventory. Some reports may include returns, clearance sales, or online orders, while others exclude them. Without a consistent definition, leadership may see conflicting performance signals across channels or regions.
5. Validate KPIs with stakeholders and iterate: Review KPIs with cross-functional partners before rolling them out. Confirm that each KPI actually drives the behavior you want and does not create unintended impact or incentives. This helps close the loop between your KPIs and your strategic goals. A retail company could implement Forecast Accuracy as a primary KPI for demand planning. They would then need to validate this KPI with commercial, supply chain, and finance stakeholders so the KPI can be refined to include SKU-level accuracy or bias measures, aligning forecast performance with both service-level and revenue goals. 

Common Pitfalls in Selecting and Defining KPIs 

Now that we’ve discussed some of the best practices, it’s also important to consider the common pitfalls that retailers face when implementing KPIs.  

• Measuring everything instead of prioritizing what matters: A common mistake is trying to measure everything, which creates cluttered dashboards and adds unnecessary noise. When teams track too many KPIs, it becomes harder to identify what truly impacts performance. For example, a retail team may monitor 40 customer metrics when only a handful influence conversion or loyalty. Prioritizing those that align best with your strategic goals and tie to real results can help simplify your KPI landscape and keep the organization most focused on high-value work.
• Selecting KPIs based on available data rather than business value: Teams often fall into the trap of defining KPIs based solely on the data that is easiest to pull rather than the metrics that best reflect true business priorities. While this simplifies reporting in the short term, it produces KPIs that are convenient to measure but misaligned from strategic objectives. Instead, organizations should begin by clearly defining the business objectives they are trying to answer and then map KPIs directly to those strategic goals, regardless of initial data constraints. When the ideal data is not immediately available, teams can temporarily adopt a metric with a clearly defined roadmap to evolve toward the optimal KPI.
• Ignoring data quality limitations: KPIs built on inaccurate or ungoverned data will mislead stakeholders and damage trust in reporting. To avoid this, teams need strong data quality checks, ongoing monitoring, and resolution of upstream issues rather than patching problems downstream. 
• Poorly defined or inconsistent calculation logic: Inconsistent definitions and logic across teams create confusion even when the KPI name is the same. The solution is to clearly document KPI logic, including source systems, filters, aggregation rules, and time windows, and maintain these definitions in a shared data dictionary. 
• Over-reliance on lagging indicators: Lagging indicators such as sales, claims, or adherence reflect past performance and limit an organization’s ability to respond proactively. Balance lagging indicators with strong leading indicators that predict future outcomes.  

Final Thoughts 

Effective KPIs are the backbone of strong decision-making in retail. When chosen and defined correctly, KPIs bring clarity, reduce uncertainty, and create alignment across merchandising, supply chain, store operations, and digital teams. They enable retailers to move quickly from insight to action, ensuring decisions are grounded in a shared, trusted view of performance. 

As AI becomes more deeply embedded in analytics workflows through chatbots, quality monitoring, and forecasting, thoughtful KPI design becomes even more critical. While AI can surface patterns, generate recommendations, and deliver advanced insights faster than ever, its effectiveness depends entirely on the quality of the metrics guiding it.  

Following best practices for KPI selection and avoiding common pitfalls is essential to successfully integrating AI into analytics workflows and advancing digital transformation. When poorly defined or misaligned KPIs are combined with AI, organizations risk accelerating the wrong insights and reinforcing flawed recommendations. 

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