I shared a five-part series on LinkedIn on how AI can enhance the entire innovation lifecycle, from identifying opportunities through scaling solutions. This post brings all five installments together in one easy reference.
1. The Shift — AI as a Strategic Innovation Partner
AI is no longer just an efficiency tool; it is becoming a strategic partner that expands creativity, clarity, and speed in innovation work. Used well, AI sharpens human judgment rather than replacing it.
Key theme: AI supports opportunity discovery, problem framing, concept development, prioritization, and scaling.
2. Discovery & Problem Definition
Most innovation failures begin with solving the wrong problem.
AI can deepen problem understanding by synthesizing diverse signals, revealing structural constraints, and reframing challenges from multiple stakeholder perspectives.
Example prompts included stakeholder reframing, root-cause analysis, and trend scanning.
3. Ideation & Concept Development
The goal is not more ideas—it is better ones.
AI helps generate structured, strategically aligned concepts and convert abstract ideas into tangible prototypes or service concepts.
Prompts focused on structured ideation, constraint-guided creativity, concept variants, and cross-industry analogy.
4. Evaluation & Prioritization
If everything looks promising, nothing is truly a priority.
AI enables transparent portfolio evaluation by applying consistent criteria such as strategic fit, ROI potential, feasibility, and adoption risk.
Example prompts included prioritization matrices, scenario testing, and risk assessment.
5. Launch & Scaling
Innovation only creates value when people adopt it.
AI helps design pilot learning loops, customize stakeholder messaging, and track early adoption signals—turning rollout into a disciplined, data-informed process.
Prompts covered pilot design, stakeholder engagement planning, friction analysis, and post-launch learning.
AI-augmented innovation is not about replacing human creativity or leadership—it is about multiplying both.
Across discovery, ideation, evaluation, and launch, AI becomes a powerful accelerator when used deliberately and responsibly.
The 2025 Nobel Prize in Economic Sciences honors Joel Mokyr, Philippe Aghion, and Peter Howitt for illuminating how innovation fuels long-term economic growth — and their insights could not be more relevant to today’s leaders navigating rapid technological change.
Innovation is not only about new products; it’s about building the conditions for adaptability and progress. Mokyr reminds us that progress thrives where knowledge and practice meet — in organizations and cultures that learn continuously. Aghion and Howitt’s theory of creative destruction shows that growth depends on renewal, not protection of the status quo.
Our continued prosperity as a society depends on productivity-driven growth, not just capital accumulation. As new technologies like AI reshape the economy, entrepreneurs and companies must constantly reimagine how they organize, collaborate, and innovate to harness these powerful tools. It always takes some time for societies to realize the full benefits of transformative technologies — but those who adapt faster become the leaders of their industries.
As an innovation practitioner, I am thrilled to see the Nobel Committee once again celebrate the central role of innovation in economic progress — from Robert Solow (1987) to Paul Romer (2018), and now Mokyr, Aghion, and Howitt (2025).
On a personal note: having conducted my doctoral research in Economics on innovation behavior under performance incentives, I’m especially delighted to see the field of innovation economics receive such well-deserved recognition.
In uncertain times, the R&D portfolio is vulnerable to budget cuts. Tying each R&D project to a Business Impact that is justifiable at the Board level protects the R&D portfolio from capricious budgets cuts and provides a rationale for reallocating R&D resources when priorities change.
Why CTOs (Rightly) Hate NPVs
CTOs are often frustrated when asked to justify an R&D project based on its Net Present Value (NPV) or Return on Investment (ROI). Calculating these values for R&D projects are not feasible other than for late-state development projects that are close to market launch. A real danger of using NPV or ROI thresholds to determine which R&D projects make the cut is that early-stage research projects – for which NPV and ROI are impossible to calculate – will be disadvantaged relative to late-stage development projects. This may or may not be a desirable outcome in a particular strategic scenario, but such a bias risks leaving the portfolio empty of potentially important research needed to fuel the next business growth phase.
The fundamental problem with using NPV and ROI measures for R&D evaluation is twofold: First, they attempt to quantify into monetary units outcomes that cannot yet be so quantified; Second, they intentionally collapse the time and money dimensions into a single measure (respectively time-discounted dollars or percentage return) intended to also reflect the time value of money.
While “What is the NPV or ROI?” is the wrong question to ask for an R&D project, the intent behind the question is entirely legitimate, which is to gauge whether the R&D will add real value to the business. A better question is therefore how the R&D will impact key areas of the business. Reframing the conversation to be about the expected Business Impact, which is the contribution of R&D to important business outcomes, will facilitate more productive discussions between the CTO, the CEO and other executives, and the Board.
The goal of R&D portfolio management is to ensure that limited financial and human resources are deployed in service of the corporate strategy. The composition of the R&D portfolio is the ultimate responsibility of the CTO while the corporate strategy is the responsibility of the CEO, acting in concert with the Board. Considering the potential Business Impact of any R&D project or project proposal creates a built-in mechanism to align the R&D portfolio with the corporate strategy, and by implication to adjust the R&D portfolio as needed when the corporate strategy changes.
Constructing a 3D-Portfolio of R&D Projects
There are at least three major dimensions according to which each project in the R&D portfolio should be classified. I call this creating a 3D-Portfolio of R&D Projects. The three essential dimensions are
the type or area of Business Impact;
the relativemagnitude of the expected Business Impact;
and the approximate time horizon over which this impact is expected to happen.
Other dimensions or considerations may be added, such as the type of technology (technology family or generation), the level of maturity of the technology (though this will be highly correlated with the time horizon in most cases), or the extent to which an R&D project departs from current technology (incremental or radical change).
A sound R&D portfolio-management process will require making design decisions about each of these three dimensions, and then designing the process flows by means of which projects will be added to or removed from the portfolio. Typically, there will need to be a cyclical process (such as the annual budget cycle) and an exception-based process for when portfolio adjustments need to be made inflight.
The sections that follow provide guidance on designing the three dimensions, followed by a very brief discussion of designing the supporting portfolio management processes, as such processes will always be highly specific to any company or organization.
Step 1: Defining Your Set of Business Impact Areas
What is a suitable Business Impact to associate with an R&D project? A Business Impact must be more specific than “increasing revenue” or “lowering costs” or “making operations more sustainable.” However, it is the highest-level answer as to how specifically R&D can contribute to such top-level corporate goals. As such, each Business Impact is a vital link connecting desired R&D outcomes to corporate strategic goals.
Business Impact areas can be divided into three categories: those that are revenue-related, those that are cost-related, and a third category covering other areas such as sustainability-, safety-, or regulatory-related Business Impacts. It is advisable to employ a set of Business Impacts that includes at least one selected from each of these three categories.
While business objectives are best phrased at a high level, they also require sufficient specificity. This suggests looking to typical industry metrics or key performance indicators (KPIs) for inspiration. For example, a retail store chain may have the revenue goal “to increase sales per square foot” rather than simply to increase total revenue. An automotive OEM may have the cost-related goal of reducing “warranty cost per vehicle.” A chemicals company may have a KPI related to regulatory or environmental compliance, specified in terms of minimizing the “number of regulatory violations”.
Each industry has such KPIs with which everyone will be familiar. Table 1 contains examples for typical industries but is far from exhaustive. It is best to select about four or five such Business Impact areas in total for classifying your company’s R&D projects. If these have already been spelled out in a Board directive to the executive you are lucky, but absent that it is usually not hard to pick an uncontroversial set of business outcomes that R&D can impact and which few would disagree with.
The beauty of being able to classify each R&D project by the Business Impact dimension is that it makes it easy to answer questions from the Board such as “How much of our $50 million R&D spending is going to greenhouse gas (GHG) reduction?” In this example, if GHG reduction were one of your Business Impact areas (as it should be if it’s important for your company and in your industry), you would have the subtotal of R&D spending on that Business Impact readily available.
When the corporate strategy then needs to be adjusted due to say economic headwinds, the conversation can be about how to shift the balance of the R&D portfolio between Business Impacts. For example, the current R&D portfolio may be 30 percent allocated to a cost-related KPI such as “overhead ratio” in financial services. If a banking crisis causes the Board to demand a greater emphasis on such projects, it would mean shifting the balance to say 50 percent of the total R&D portfolio. Such a change may be achieved by increasing the number of projects related to that impact area, or reducing other projects, or by a combination of both actions.
Table 1. Examples of Industry KPIs Associated with Business Impact (Illustrative, Not Exhaustive)
Industry
Revenue-related (topline)
Cost-related(bottom line)
Other metrics
Automotive
Market share
Warranty cost
Fuel efficiency; platform commonality
Chemicals
Plant utilization
Energy usage per production unit
Number of regulatory violations; environmental compliance
Consumer packaged goods
Average consumer spending (on company products)
Supply chain efficiency
Brand loyalty; percent of ethically sourced products
Financial services
Assets under management
Overhead ratio
Risk-weighted assets; Basel III ratios
Manufacturing
Capacity utilization rate
Scrap rate
Backorder rate; changeover time
Metals & mining
Production volume
Cost per ton
Water quality index; safety
Oil & gas
Break-even oil price
Lifting costs
Exploration success rate; carbon intensity
Pharmaceuticals
Percentage of revenue from blockbuster drugs
Cost per new drug development
Pipeline strength; number of clinical trial failures
Retail
Sales per square foot
Cost per square foot
Average discount depth; customer satisfaction score (CSAT)
Technology
License and subscription renewal rates
Cost per line of code
Platform uptime; data breach incidents
Step 2: Defining the Range Scale for Each Business Impact
How do you prioritize R&D proposals that are tied to different Business Impact areas? Moving away from a single metric such as NPV or ROI to compare a R&D project’s value-add to multiple Business Impacts means that you have to find a way of comparing apples with oranges. However, this is not as hard to do as it may seem but it does require a shift in mindset:
The common scale of comparison for all Business Impacts becomes an ordinal scale with three to four intuitive impact descriptions on it, such as Low, Moderate, High, and Very High. The design work is in defining what each impact description means for each Business Impact area. For example, if the Business Impact is reflected by a KPI that can typically only be moved in single digit percentages, it would look like the “Business Impact 1 KPI” in Table 2, where a “High” impact is classified as a 5 to 10 percent increase. Another Business Impact may be easier to inflect, such as “Business Impact 2 KPI”, and therefore may need to be improved by 20 to 30 percent for it to be considered a “High” impact.
Completing such a table for each Business Impact area versus the ordinal scale for the impact (e.g., low to very high) will result in an impact “rubric” which can be used to compare the potential relative benefit or value-add for R&D projects that are entirely different in kind, for example, an increase in market share versus engine fuel efficiency for an automobile OEM.
Table 2. Illustrative Rubric for Comparing Business Impact
Business Impact 1 KPI
Business Impact 2 KPI
Low
< 1%
< 10%
Moderate
1 to 5%
10 to 20%
High
5 to 10%
20 to 30%
Very high
> 10%
> 30%
Before launching the new rubric to grade the first R&D project proposals, the CTO’s team must take great care in calibrating the scales with the involvement of the entire R&D leadership and key stakeholders in other departments. That will create trust and prevent anyone from gaming the system to advance their pet projects.
It is also important to be fair and maintain the integrity of the grading for each R&D project by using peer review to check the claimed magnitude of the Business Impact KPI movement for each case under consideration. This is best done in an R&D budget decision meeting with all the major parties present.
Step 3: Defining the Time Horizons to Use
A multi-year classification framework is needed to indicate the approximate timeframe in which an R&D project’s Business Impact is expected to be achieved. The simplest and most intuitive approach is to use the Three Horizons, where Horizon 1 (H1) is the short term, Horizon 2 (H2) is the medium term, and Horizon 3 (H3) is the long term. For illustrative purposes, H1 is typically 12 to 18 months, H2 is 18 months to 3 or 4 years, and H3 is farther out. However, the durations will differ by industry as industries have shorter or longer cycle times. You’ll need to define the Time Horizons that make sense for your industry and apply them consistently.
The Time Horizons will facilitate the cross-referencing of the 3D R&D portfolio with your company’s Technology Strategy. For example, where your strategy is to only implement fairly mature technologies in a particular technology domain, that may result in only selecting R&D projects in that domain that fall within H1. On the other hand, if you see yourself as a technology leader in your industry for a particular domain, that would argue for having R&D projects all the way out into H3.
Creating the 3D-Portfolio of R&D Projects
By far the best way to have a portfolio of R&D projects that can each be tied to Business Impact is to create it that way from the start. This can be done by employing the portfolio framework described above.
Rather than simply inviting and collecting R&D proposals or ideas – as is often the case during the annual proposal cycle – you must give guidance on what types of proposals are desired and how they will be evaluated. You should map out the portfolio composition you want to end up with by constructing a table indicating desired allocations (percentages or dollars) by Business Impact versus Time Horizon. This guidance should be compiled ahead of the proposal invitation period by the CTO’s team in consultation with key business partners and with reference to the corporate strategic objectives for the year.
Following this process will effectively cascade the corporate strategic goals down to high-level R&D departmental goals and shape the resulting new R&D portfolio. [I previously described the complete hierarchy of innovation value-creation levels versus the parts of the organization responsible for each step in Chapter 7 of my book, Innovation for Value and Mission – An Introduction to Innovation Management and Policy.]
Once you have defined your 3D framework, you can use it to retroactively classify and organize all existing R&D projects. This may be necessary in case of a mid-cycle event such as receiving a new strategic directive from the Board or CEO that necessitates a rebalancing of the R&D portfolio. Having an R&D portfolio classified by the three dimensions will facilitate the most rational and unbiased way of making portfolio changes, even if there has to be cuts. And it will minimize the likelihood of making cuts that you’ll deeply regret later!
This article was originally posted by the author on LinkedIn.
The CHIPS and Science Act of 2022 injects $280 billion into U.S. research, innovation, and manufacturing over the next five years. The “CHIPS” name reflects the priority given to the semiconductor industry with $52.7 billion of dedicated semiconductor spending, including $39 billion in grants and a 25% tax credit for on-shore US manufacturing. The policy goal is to increase the U.S. manufacturing share of this crucial technology after years of decline – from 37 percent in 1990 to 12 percent (relative to the US semiconductor consumption of 34%) – mostly due to more aggressive industry investments by other governments. The Act also reflects the urgency of addressing semiconductor shortages and cyclic dynamics which trouble multiple industries (for example, automotive manufacturing), and impede U.S. economic growth.
The Importance of Semiconductors
Our modern economy runs on semiconductors: both discrete devices such as power transistors and diodes that handle electric power or govern electric motors, and integrated circuits (ICs or “chips”) which are typically manufactured as wafers and contain thousands to billions of devices, mainly transistors. ICs may be microprocessors or memory chips used in computing, or commodity electronic building blocks used in a myriad of circuit designs, as well as custom circuits on a single chip, such as 5G wireless or GPS chips for your smartphone. An exponential growth in chip complexity over the last few decades has enabled our digital age and so much of the functionality we too easily take for granted. The smartphone in your pocket has far more computing power than NASA used for 1969 Apollo moon mission.
Though the CHIPS Act passed with bipartisan majorities in both the Senate and House, most Republicans opposed it. This is in line with a long tradition of skepticism about the U.S. government’s role in shaping and subsidizing industries. Unlike many other developed countries, the United States has generally eschewed an industrial policy of deliberately building up strategic industries. Yet, it also has a long tradition of making exceptions considered to be in the national interest.
The exigencies of World War II forced the U.S. government to be directly involved in weapons development, from basic research to production. The Manhattan Project (nuclear bomb) is the most famous example, but radar and computers were wartime government projects too. In each case, the government partnered with select universities and private firms. After the war, the government initially resolved to limit itself to only funding basic research. But the 1957 Sputnik launch and the military threat posed by the Soviet Union soon changed that, leading to the creation of NASA and DARPA in 1958. Technologies created with defense dollars have subsequently enabled great private-sector innovation, for example the internet and GPS. DARPA maintains partnerships with semiconductor firms for the development of new technologies, often with both military and civilian applications.
Policy Objectives
What is the public-policy rationale for a $52.7 billion government investment in one industry?
First, there is a substantial U.S. national-security interest, which includes self-sufficiency in advanced devices for defense and aerospace systems. Specifically, China’s territorial claims on Taiwan, which dominates global semiconductor manufacturing, is seen as a national-security risk.
Second, there is a desire to increase industry resilience to global supply-chain disruptions such as happened due to COVID lockdowns. A reliable supply of semiconductors is needed to make anything from home appliances and automobiles to the computers and data centers essential for continued national productivity growth.
Third, there is a global manufacturing capacity shortfall in the industry with factories running at full capacity, but unable to meet demand; backlogs are running at six months or longer.
Fourth, semiconductors are a top 5 U.S. export amounting to $60 billion, and a category in which the United States maintains a trade surplus.
Understanding more about the development and state of the semiconductor industry will provide further context on why the semiconductor industry was considered worthy of an exception to the general avoidance of industrial policy.
Semiconductor Developments
Transistors are tiny multilayered devices made from silicon or germanium of which some parts are precisely infused with impurities, enabling them to amplify or switch electrical current. Transistors and other semiconductors are the active components in almost all modern electronics. The transistor was invented by Bell Labs scientists in 1947 and the first integrated circuits (ICs) containing multiple transistors on a single chip appeared in the 1950s. In 1965, Gordon Moore (a future cofounder of Intel) wrote a prescient paper predicting that ICs containing more integrated electronics would revolutionize telecommunications and computing. Moore’s observation that the number of transistors on a single chip were doubling every two years as techniques improve became known as Moore’s Law, and was soon interpreted to mean that computing power would double every two years. In 1965 only 60 transistors fit on one IC, but Moore’s Law meant that by 1975 a state-of-the-art microchip would contain 65,000 transistors, which came to pass in 1975 exactly as predicted. Moore’s Law subsequently set industry expectations and became a self-fulfilling prophesy. By 1989, Intel launched the first 1-million-transistor microprocessor, the 80486. Today, over 2 trillion transistors can be crammed onto a chip. The end of Moore’s Law has been declared many times as miniaturization techniques ran into physical limits, yet ingenuous chip designers keep inventing new techniques to extend it.
The semiconductor node size in nanometers (nm) historically represented the smallest features that could be created by a particular manufacturing process. The node size is an indicator of how many devices one chip can contain (the smaller the node, the more devices) and hence related to Moore’s Law. Node size is also used to indicate the technology generation, with successive generations having smaller node sizes. The current cutting-edge node, 5 nm, is used for chips with the highest transistor densities such as powerful processors for mobile phones and computers. But as nodes advance, manufacturing and design costs escalate. Fabs, as individual factories are called, need new equipment to build different nodes. Building a 7nm or 5nm fab is so expensive that only Intel, TSMC, and Samsung have done so. These firms are now launching 3 nm processes.
However, most chip applications do not require the smallest nodes. Production of chips continue in nodes as large as 130 nm, while 20, 14 and 12 nm nodes can still meet support high-performance applications as process advances continue to be made by companies such as GlobalFoundries. There is a misperception that sub-7nm logic chip technology – still less than 30 percent of the market – is all-important. The United States needs to onshore a much broader set of semiconductor technologies. Such technologies include radio and optical communication chips used in a vast number of products essential for national security and industry resilience. Leading-edge innovation in these technologies depends more on specific device and circuit architectures, and less on node size.
Little is gained if American-made wafers have to shipped offshore to be packaged into devices. Chip packaging – historically a low-margin business – is now a critical technology as 2.5D and 3D architectures are needed for advanced designs. The CHIPS Act accordingly invests in microelectronics packaging technology with the new Advanced Packaging National Manufacturing Institute created by the Act.
The Current State of The Industry
The industry is global and interconnected with three main types of semiconductor companies: those who design but do not manufacture are called “fabless” firms; those who only manufacture, usually for multiple design clients, are called foundries; and those who package and test the semiconductors coming out of the foundries. Increasing foundry capacity is costly with a new fab’s construction and operating costs easily being $2 -3 billion; more for the latest technologies. Chipmaking is a highly capital-intensive industry with each node generation demanding a larger expenditure on equipment than the previous. Due to high fixed costs, most chip companies outsource manufacturing to foundries, who achieve high utilization by making chips for multiple customers. AMD, Nvidia, and Qualcomm are all fabless: their chips are manufactured by contract foundries. Intel is one of the few remaining Integrated Device Manufacturers (DMs) – designers with their own foundries – as is Samsung. But even IDMs use contract foundries to make some of their chips.
The majority of semiconductors worldwide, including the most advanced chips with the highest component densities, are made in Taiwan by various foundries including the world’s largest, TSMC, which counts Apple, Qualcomm, Nvidia, and other technology companies among its clients. The next largest manufacturer is Samsung in South Korea.
MAPI, BMNP Strategies, and Decodexis teamed up to study the impact of advanced analytics on the manufacturing sector.
Ask innovation and product development executives at U.S. manufacturers, and they will agree — advanced analytics will change the face of innovation. Two-thirds of executives surveyed expect that analytics will improve their innovation performance in the near future.
46% of manufacturing executives believe that advanced analytics will drive major changes in the industry, and 49% believe that it will drive some changes in how their industry innovates.
Where in the analytics journey are you and what tools and techniques are you using? A variety of tools and techniques are used in the manufacturing industry and companies typically start with implementing analytics tools that support decision-making.
