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четверг, 27 августа 2026 г.

The 2027 CMO planning challenge is bigger than the budget

 


By

Principal Analyst, Forrester


More budget won't fix a marketing model that no longer fits how buyers discover, evaluate, and decide. CMOs need to rethink where they invest.

Many of the assumptions that have guided B2B marketing planning for the past decade are becoming less reliable. 

Buyers were increasingly visible, engagement signals helped indicate interest, channels were manageable, and buying journeys could be observed, influenced, and measured. 

Today, buyers are harder to observe, AI is reshaping discovery and evaluation, traditional measurement signals are weakening, buying networks continue to expand, and volatility is a permanent feature of the operating environment.

The assumptions behind marketing planning no longer match how buyers discover, evaluate, and decide. Forrester described this shift at this year’s B2B Summit as the B2B go-to-market singularity

For CMOs, that makes the planning challenge bigger than deciding where to allocate the budget.

More budget won’t fix an outdated planning model

This creates a different CMO problem. The question isn’t how to allocate next year’s budget across technology, talent, and programs. It’s whether the marketing organization can adapt faster than the market changes.

CMOs will enter 2027 with a favorable investment outlook. In Forrester’s Budget Planning Guide research, nearly nine in 10 B2B marketing decision-makers expected marketing investment to increase over the next 12 months, with increases expected across technology, personnel, and programs. More budget sounds like welcome news. But it won’t automatically create more impact if it flows into a marketing model built for yesterday’s buying environment.

The comfortable response to uncertainty is more: more budget, more AI pilots, more programs, more channels, more content, more campaigns, more activity. Each creates the appearance of progress and gives a CMO another way to show motion. But more of the same won’t fix a model designed for a market that no longer exists.

Worse, more of the same can make the problem harder to see. More activity can create the appearance of momentum while making the organization less adaptive. More AI pilots can signal innovation while scaling unclear decision rights, weak governance, and disconnected workflows. More programs can expand coverage while fragmenting attention. More budget can make legacy assumptions more expensive to maintain.

Optimization can preserve the wrong system

This is where many planning conversations go wrong. Most marketing leaders have spent their careers learning how to optimize: improve conversion rates, campaign performance, channel efficiency, attribution, and productivity. Optimization feels disciplined because it asks every part of the system to get better.

But optimization assumes that the underlying system is still fundamentally sound. When buyers become less visible, AI reshapes discovery, signals weaken, and markets shift faster than annual plans can absorb. Optimization can preserve the complexity that prevents adaptation. It can make an organization better at operating a system that is losing fit with the market.

For CMOs, that is the uncomfortable part. The habits that once signaled strong leadership may not be enough for what comes next. A rigorous planning cycle, a broader program mix, a longer list of AI experiments, and a better-optimized campaign engine can leave the organization exposed when those efforts reinforce assumptions that no longer reflect how buyers behave.

Focus creates the capacity to adapt

The harder leadership move is focus. A focus mindset starts with a different set of questions: 

  • Where can value compound? 
  • Which audiences, segments, capabilities, and market positions deserve disproportionate investment? 
  • Which activities continue because they’re familiar, measurable, or politically difficult to stop? 
  • Which AI experiments are ready to scale, and which are simply automating ambiguity? 
  • Which programs create business impact, and which only create motion?

Focus is the discipline that makes adaptation possible. The logic is straightforward but difficult to execute.

  • Focus creates the basis for divestment.
  • Divestment creates capacity.
  • Capacity enables concentration.
  • Concentration supports adaptation.
  • Adaptation builds resilience.
  • Resilience sustains growth.

Resilience doesn’t come from spreading resources more evenly. It comes from concentrating them where the organization can shift, learn, and respond faster than conditions change.

The harder part of planning is deciding what to stop

It’s easier to optimize the existing portfolio than to decide what no longer deserves the time and money. Those choices can leave marketing organizations less adaptable when adaptability matters most. 

CMOs must invest where new sources of strength are emerging, and those investments only matter if leaders are also willing to stop funding decisions made in the past. Not every segment deserves to remain a priority. Not every familiar program deserves another year of investment.

The planning challenge for 2027 is deciding which assumptions still deserve investment. That requires questioning long-standing priorities, challenging familiar success metrics, and acknowledging that some activities are optimized for conditions that no longer exist.

2027 planning needs to build adaptability

Broken assumptions require deliberate choices. CMOs need to focus on where value can compound, divest from work that no longer deserves capacity, concentrate resources where adaptation is possible, and reallocate faster than the market changes. That requires treating adaptability as a planning discipline, rather than an outcome of having more budget or more AI initiatives.

For 2027, the central planning question is whether your organization can adapt as quickly as the market changes. Resilience is the mechanism that allows growth to continue as the assumptions behind growth change.

https://tinyurl.com/34chaj55


2027 Marketing Budgets: Why New Categories Beat Bigger AI Line Items

Greg Jarboe


CMOs are budgeting for 2027 with channel buckets built for a customer journey that's disappearing. I'd rebuild around five functions instead.

Almost a year ago, I recommended that chief marketing officers should “hire an economist or chief economist” to weather a perfect storm of challenges that included changing consumer behavior, rapid technological advancements, and economic uncertainty.

Earlier this month, nearly 200 economists and tech leaders signed a letter to policymakers warning that AI “could bring risks, including large-scale job displacement.” Basically, the letter called for policymakers to do more to understand and respond to potential disruptions from artificial intelligence.

Very few CMOs have hired an economist or chief economist. And I’m skeptical that policymakers are going to move as quickly as artificial intelligence, which is transforming the economy faster than any previous technology. So, as most managers, directors, and executives plan their marketing budgets for 2027 sometime after Labor Day, they may want to recall what the poet June Jordan wrote back in 1978, “We are the ones we’ve been waiting for.” What should they do?

They should start by using an audience research tool to find out who their target customers are, what they are doing, and why they are doing it.

Then, they should craft a prompt like this one:

“Based on original reporting, research, data analysis, or evaluation from authoritative and trustworthy sources with industry experience and expertise, should I consider shifting my budget into some entirely new categories? Yes, I know this could trigger the dreaded reorg or agency review. But now is the time to analyze what’s working and what isn’t without fear or favor. How should I proceed over the next six weeks before I need to submit my budget for 2027?”

Next, they should enter this prompt into Google to compare what the AI Overview and AI Mode recommend. They should also enter this same prompt into ChatGPT, Claude, and Gemini to evaluate what all three recommend. In addition, they should fact-check, ground truth, and look for the receipt of whatever recommendations search and AI tools make.

Finally, they should adopt David Ogilvy’s old-school practice of “going for a long walk, or taking a hot bath, or drinking half a pint of claret,” which he recommended in his classic book, Ogilvy on Advertising.

I did most of this last week, although I updated Ogilvy’s suggestions. Instead, I came up with some critical data, market trends, strategic insights, and tactical advice.

The legacy channel buckets on most 2026 budget templates are measuring a version of the customer journey that is disappearing. Ewan McIntyre, the Gartner analyst who runs the firm’s CMO Spend Survey, put numbers on how fast. CMOs are now allocating 15.3% of marketing budgets to AI initiatives, yet only 30% say their organizations are actually ready to scale that investment. At the same time, awareness and conversion now claim 62.6% of total media spend, a jump of more than 10% since 2024, while spending on loyalty and retention has fallen 29% to less than 15% of the total. McIntyre’s data found one exception to that shift. The most AI-mature organizations hold onto a larger share of loyalty and retention spend rather than chasing acquisition, which suggests less mature organizations are over-indexing on whatever AI can measure and automate most easily. That is not a contradiction. It is a reallocation already underway, and most budget templates have not caught up to it.

Christine Moorman, who directs The CMO Survey out of Duke’s Fuqua School of Business, found something that points in the same direction from an entirely different source. The 35th edition of her survey, fielded in January among 308 marketing leaders, found that generative engine optimization (GEO) is already in use at four in 10 companies, a category that did not exist in her survey until recently. At the same time, she found no marketing technology activity scoring above a 5 on a 7-point performance scale. That gap is exactly where a budget reorganized by function, not by legacy channel, earns its keep.

So instead of asking which channel gets more money, I think CMOs should build their 2027 budgets around five functional categories.

AI visibility and citation management. This replaces a chunk of the SEO line, but not all of it. The job is no longer only ranking a page. It is earning inclusion in the answer itself, tracked through something closer to what I’ve been calling Citation Share of Voice than through keyword rank.

Trust verification. I wrote mid-July that only 28% of Americans trust AI search results. That gap is a budget line now, not a footnote. Brands that fund the work of getting their facts, credentials, and reviews structured so an AI model can verify them are the ones who close that trust gap before a competitor does.

Distribution engineering. This is where the DIRHAM 2.0 framework I taught in Dubai this spring actually earns its keep. Content built once and pushed through owned, earned, and AI-crawled surfaces at the same time, instead of funded channel by channel, is a budgeting decision as much as a production one.

Human judgment and editorial oversight. Gartner’s own data argues for this line item indirectly. Labor rose from a mean 21.9% of marketing budgets to 24.5% this year, even as 43% of CMOs told Gartner they expected to cut labor spending. The CMOs winning that argument internally are the ones who can show what a trained editor or strategist catches that a model does not.

Measurement rebuild. Last click attribution cannot see a customer who asked ChatGPT for a recommendation and never clicked anything. On May 20, 2026, AMEC (the International Association for the Measurement and Evaluation of Communication) launched its GEO Principles. These and a genuine Citation Share of Voice metric are a more honest place to put next year’s measurement dollars.

None of this means SEO, paid media, content marketing, social media marketing, or digital marketing go away. It means the org chart for your budget stops mirroring the outdated PESO media model (paid, earned, shared, and owned media), which did real work to sort budgets and assign campaigns to channels. But that framework was answering a distribution question, not the one marketers actually face now. Knowing where to place content doesn’t tell you whether it gets seen, and visibility today is decided by algorithms, not people scrolling a feed.

Your 2027 Budget Reorg: 3 Steps Before You Submit

 Step 1. Re-tag last year’s spend against the five functions, not the old channels. Pull 12 months of budget data and sort every dollar into AI visibility, trust verification, distribution engineering, human oversight, or measurement rebuild instead of SEO, paid social, email, and display. This alone usually surfaces work you’re already funding that has no name on your budget template yet.

 Step 2. Run your audience data against each function, not each channel. Use SparkToro or GWI to check where your customers are actually spending attention right now. Fund the function where the gap between spend and attention is widest first, not the channel that’s loudest in the planning meeting.

 Step 3. Walk into the CFO conversation with one number that isn’t last click. Bring your Citation Share of Voice, or an equivalent GEO metric, as the evidence for the reorg. A CFO who hears “AI is changing things” will push back. A CFO who sees a citation trend line next to last year’s flat organic traffic will ask what’s next.

I’ll conclude this column by saying the part Gartner’s press releases won’t. A CMO who submits a 2027 budget organized around outdated channels, in an environment where AI is already reallocating attention faster than any technology I’ve covered in 20 years, is not being prudent. They’re being unprepared.

https://tinyurl.com/2sup8w7t

Adaptive Strategic Approaches and Their Impact on Economic Growth in Dynamic Markets

 













https://tinyurl.com/4w9am8zy

среда, 26 августа 2026 г.

Adaptive Strategy in modern business

 


Adaptive Strategy is a framework designed an approach in business and organizational management that emphasizes flexibility, responsiveness, and continuous adjustment to changing environmental conditions and unforeseen challenges.

Traditional strategic planning relies on a stable matrix: a company sets a 5-year goal, creates a rigid roadmap, and executes it step-by-step.

In contrast, an adaptive strategy treats a business plan not as a fixed blueprint, but as a living hypothesis. Instead of predicting the future, the organization builds the capacity to respond to real-time changes, continuously experimenting, learning, and pivoting based on market feedback.

⚙️ Core Principles of Adaptive Strategy

To successfully implement an adaptive strategy, businesses shift from a "command-and-control" mindset to an evolutionary approach based on four pillars:

  • Continuous Environmenta Sensing: Constantly scanning the horizon for early warning signs, shifts in customer behavior, and emerging technologies rather than relying on annual market reports.
  • Rapid Experimentation: Testing multiple small-scale initiatives simultaneously to see what works, rather than betting the entire company's budget on one massive project.
  • Decentralized Decision-Making: Empowering frontline employees and autonomous teams to make quick decisions, eliminating the bureaucratic bottlenecks of corporate hierarchy.
  • Dynamic Resource Allocation: Dynamically moving capital, talent, and technology away from failing initiatives and instantly funneling them into high-performing experiments.

 Key components

·        Flexibility in Planning: Developing strategic plans that can be easily adjusted based on new information or changing circumstances.

·        Feedback Mechanisms: Implementing systems to gather and analyze feedback from various sources to inform strategic adjustments.

·        Iterative Processes: Using iterative cycles of planning, execution, and review to refine strategies continuously.

·        Continuous Learning.

·        Flexibility and Agility.

·        Scenario Planning.

·        Cross-Functional Collaboration.


source: IntelligentHQ


📊 Traditional vs. Adaptive Strategy

Feature

Traditional Strategy

Adaptive Strategy

Market View

Predictable and stable

Volatile, uncertain, and complex

Planning Cycle

Fixed (3 to 5 years)

Continuous, real-time adjustments

Core Goal

Sustainable competitive advantage

Successive, temporary advantages

Risk Management

Risk avoidance through planning

Risk mitigation through fast failure

Execution Style

Top-down compliance

Bottom-up experimentation


Situations for which the concept is particularly well suited

·        Highly volatile and dynamic markets

·        Industries experiencing rapid technological change

·        Startups and innovative companies needing to pivot frequently

·        Organizations facing significant regulatory or competitive pressures

 

Practical application

1.     Conduct Environmental Scanning: Regularly analyze the external environment using tools like PESTEL analysis to identify key trends and potential disruptions.

2.    Develop Flexible Plans: Create strategic plans with built-in flexibility, including contingency plans for different scenarios.

3.    Establish Feedback Mechanisms: Set up processes to gather feedback from customers, employees, and other stakeholders to detect early signs of change.

4.    Empower Teams: Decentralize decision-making to allow teams to respond quickly to new information and challenges.

5.    Implement Iterative Processes: Use short planning and execution cycles (e.g., quarterly reviews) to regularly assess and adjust strategies.

Examples of Adaptive Strategy in Practice:

 

6.    Tech Industry: Tech companies often use adaptive strategies to stay ahead of rapid technological advancements and changing consumer preferences.

7.     Healthcare: Hospitals and healthcare providers may use adaptive strategies to respond to public health crises, such as pandemics, by reallocating resources and adjusting protocols quickly.

8.   Retail: Retailers may adapt their strategies in response to shifting consumer behavior, economic changes, or supply chain disruptions by altering product offerings, pricing, and distribution channels.

🔍 Real-World Business Examples

1. Netflix: The Ultimate Pivot

  • The Context: Netflix started as a DVD-by-mail rental service competing with Blockbuster.
  • The Adaptation: Instead of doubling down on physical logistics, leadership constantly monitored internet bandwidth improvements. They experimented early with streaming technology, even when the video quality was poor. Later, sensing that content creators would eventually pull their licenses, they adapted again by producing original content (House of Cards). Recently, they shifted resources into mobile gaming and ad-supported tiers to combat subscriber stagnation.

 

2. Zara (Inditex): Agile Supply Chains

  • The Context: Traditional fashion retailers design clothing lines up to nine months in advance, leaving them highly vulnerable to changing consumer tastes.
  • The Adaptation: Zara uses an adaptive supply chain driven by real-time data. Store managers report daily on what customers are buying, what they are asking for, and what they are leaving on the racks. This data is routed directly to designers in Spain. Zara produces clothes in small batches and can design, manufacture, and deliver a new clothing line to stores in less than three weeks, matching unpredictable fashion trends on the fly.

 

3. Haier: RenDanHeYi Model

  • The Context: Haier, a massive Chinese home appliance manufacturer, realized that rigid corporate structures destroy speed and innovation.
  • The Adaptation: The company eliminated its entire middle management layer and transformed into an ecosystem of thousands of autonomous micro-enterprises. Each micro-enterprise acts as an independent startup. They can instantly modify products, change pricing, or collaborate with outside partners to solve specific consumer pain points without waiting for approval from the corporate CEO.

Benefits to using this concept in strategic planning

·        Enhanced ability to respond to unexpected changes

·        Increased organizational agility and resilience

·        Better alignment with real-time market conditions

·        Improved innovation and responsiveness

·        Greater competitive advantage in dynamic environments

 


Adaptive Strategy - Why it Matters


Here’s why adaptive strategy design matters and how to get it started:

·        About that whole “auto” thing: An adaptive strategy ensures automatic adaptation, but that’s not going to happen, well, automatically. It is your job to create a system that’s intuitive, easy to change and upgrade, and designed to automatically respond correctly after upgrades or maintenance. “Auto” is the ultimate goal, but it’ll always take some leg work and elbow grease to get you there.

·        Adaptation is the grease of business savvy: Mobile readiness falls under the umbrella of “responsive design”. The ability to adapt and respond appropriately in any context is crucial. Responsive design is centered on websites. Just because a website looks great on your laptop or tablet doesn’t mean it looks the same on every other browser or platform. What about customers using the latest iPhone or those on a really old device? Responsive design is the ability to “adapt” to every possibility (and it requires constant testing).

·        The customer - and context - is always right: Why do you need to adapt and not your clients or customers? Because they can go many other places, or even just one other place, and get the kind of service they deserve. As a business owner, it’s your job to adapt to the needs of those you serve. If you don’t, you’re going to be seen as outdated, unprofessional, and uncaring about your users. Ideally, you’re also adapting to the needs of your employees and giving them the tools they need to do their best work.

·        Adaptation in your strategy plan: Adaptation should be an integral part of any marketing campaign or business plan. Change is inevitable, and some industries evolve at lightning speed. To stay competitive and offer the best solutions, your business needs to do what it takes to stay on the cutting edge. Consider each of your projects and your business as a fluid, ever-changing being. That will get you on the right track.


Styles of Adaptive Strategy


There are many styles of adaptive strategy that can help companies achieve business sustainability during a turbulent environment. A company’s optimal choices are mainly a function of the environment ― especially the rate at which it is changing, the predictability of change, and the degree of change required. There are four broad styles of adaptive strategy.

·        The Sprinter: In environments with only a moderate degree of both turbulence and required change, companies can focus on rapidly optimizing and exploiting existing business models to track an increasingly volatile environment. The fashion retailer Zara, for example, focuses on building a fast feedback cycle between sales data from its stores and the design and manufacture of new products. This model allows the company to stay at the forefront of fashion trends without having to make big bets on where the trends are headed.

 

·        The Experimenter: In environments where turbulence is high but the degree of change required is low, companies whose business models are fundamentally sound must nevertheless modify their product mix or other low-level aspects of their business through a process of iterative experimentation. McDonald’s, for example, uses a structured process to design, test, and introduce menu items while keeping its overarching business model unchanged. This enables it to evolve along with customer preferences and still preserve the well-honed efficiency of the kitchen model at the core of its operations.

 

·        The Migrator: In environments with moderate turbulence and a high degree of required change, companies must deliberately migrate their obsolescent business models or domains toward more attractive ones using a targeted and deliberate process. Virgin, for instance, systematically manages a diverse portfolio of challenger businesses by rapidly scaling up potential winners and cleanly divesting or shutting down losers.

 

·        The Voyager: In environments with a high degree of both turbulence and required change, companies need to deploy an exploratory approach to the business model or system. This can involve “live” tests with a mixed portfolio of competing business models and strategies, some of which may even be mutually contradictory. Netflix, which has reinvented fundamental aspects of its business strategy and model several times in the extremely turbulent movie-rental business, is a good example of a voyager. It removed late fees (at one time a mainstay of industry profits) and is exploring video streaming on a variety of platforms, potentially cannibalizing its DVD-by-mail business in order to stay ahead of the competition. Netflix has succeeded in dominating and reshaping a chaotic industry in which less adaptive competitors have fared poorly.

An adaptive strategy is no longer a luxury; it is a survival requirement. In an era dominated by rapid artificial intelligence integration, macroeconomic shifts, and fluctuating consumer loyalty, organizations that anchor themselves to rigid long-term plans risk becoming obsolete before their planning cycle even ends.


Adaptive Strategy Roadmap for the pharmaceutical industry 

Designing an Adaptive Roadmap for the pharmaceutical industry amidst explosive AI integration requires a complete departure from traditional linear planning. The pharmaceutical sector is historically conservative: drug development cycles span 10–12 years, and regulatory requirements (FDA, EMA) are incredibly rigid.

However, generative AI, advanced molecular modeling (like newer iterations of AlphaFold), and quantum computing are compressing the early stages of R&D from years into months. Below is a detailed guide on how to design a flexible strategy that ensures a pharma company stays ahead of the technology curve.

🗺️ Structuring the Adaptive Roadmap: Moving from Fixed Dates to "Opportunity Horizons"

Instead of anchoring plans to rigid dates (e.g., "Implement AI in marketing by Q3 2027"), an adaptive roadmap is built around three rolling horizons that constantly self-correct based on technological shifts.

🌅 Horizon 1: Short-term (0–12 months) — "Low-Hanging Fruit" & Automation

The goal is to deploy commercially available AI tools to instantly drive efficiency and free up capital.

  • AI in Regulatory Writing: Using Large Language Models (LLMs) to automatically draft thousands of pages of documentation required for IND (Investigational New Drug) and NDA (New Drug Application) submissions.
  • Smart Scientific Search: Building internal AI assistants that instantly scan and cross-reference the company's historical patent archives and past preclinical trial data.
  • Commercial Operations: Automating medical content generation for healthcare professionals (HCPs) and hyper-personalizing marketing campaigns.

🌅 Horizon 2: Medium-term (12–36 months) — Core Process Transformation

The goal is to integrate specialized AI into parts of the value chain where the technology is mature but requires process redesign.

  • AI-Driven Clinical Trial Design: Utilizing predictive models to identify ideal patient cohorts, forecast drop-out rates, and create "digital twins" for control groups, reducing the need for actual placebo patients.
  • Supply Chain Optimization: Predicting raw material shortages and rare drug demand fluctuations through advanced predictive analytics.

🌅 Horizon 3: Long-term (36+ months) — Business Model Reinvention

The goal is to build entirely new sources of competitive advantage where AI serves as the core engine.

  • Autonomous De Novo Drug Design: Moving fully toward AI-generated molecular structures tailored to specific biomarkers (generative chemistry), paired with automated synthesis in robotic laboratories.
  • Hyper-Personalized Medicine: Creating mRNA vaccines or cell therapies customized to an individual patient’s genetic profile within days.

🛠️ 4 Steps to Design the Adaptive Process

To ensure the roadmap remains a "living" document, the organizational framework must include the following structural elements:

1. Establishing a Cross-Functional "AI Radar" (Continuous Sensing)

A pharma company must continuously scan the tech landscape. This requires an internal steering committee comprising biologists, data scientists, and regulatory experts.

  • What to watch: The release of new open-source AI models, regulatory policy updates (e.g., FDA approval of a drug fully designed by AI without human intervention at the design stage), and quantum computing breakthroughs capable of simulating complex protein folding.

2. A Portfolio Approach to Experimentation (Rapid Experimentation)

Instead of betting a $100M budget on a single enterprise AI platform, the company allocates capital across 10–15 small-scale pilots (PoCs — Proof of Concepts):

  • Testing AI for finding targets against a specific oncological biomarker.
  • Testing computer vision AI to analyze MRI scans in clinical settings.
  • The Adaptive Rule: If a pilot fails to show intermediate milestones within 3 months, it is shut down, and resources are instantly reallocated to successful projects.

3. Data Architecture as the Foundation (Dynamic Infrastructure)

No AI model can succeed without clean data. The adaptive roadmap must include a continuous track dedicated to data modernization:

  • Breaking down data silos across isolated global labs into a unified Data Lake.
  • Enforcing the FAIR data principles (Findable, Accessible, Interoperable, Reusable) so commercial and proprietary AI models can effectively train on your internal chemical formulations.

4. Proactive Regulatory Compliance (Responsible AI)

Regulatory bodies change rules much slower than technology evolves. An adaptive strategy builds a proactive relationship with regulators. The roadmap must prioritize the development of Explainable AI (XAI), ensuring the company can always demonstrate why an algorithm selected a specific molecular structure, eliminating the "black box" dilemma.


📈 Example: How the Roadmap Adapts to Market Disruptions

  • Plan A: The company sets a 2-year timeline to build a proprietary AI model to scan its chemical libraries.
  • The Trigger: A partner startup or the open-source community releases a pre-trained model that outperforms the company's internal build.
  • The Adaptive Pivot: The company instantly stops internal development (saving millions), licenses or hooks into the superior model via API, and immediately redirects its internal data scientists to focus on the validation and laboratory testing phases of that model's outputs. Time-to-market for the clinical phase is cut by 18 months.