Quantum computers may deliver an economic advantage to business, even on tasks that classical computers can perform.

Francesco Bova, Avi Goldfarb, and Roger Melko March 06, 2023

Imagine that a pharmaceutical company was able to cut the research time for innovative drugs by an order of magnitude. It could expand its development pipeline, hit fewer dead ends, and bring cures and treatments to market much faster, to the benefit of millions of people around the world.

Or imagine that a logistics company could dynamically manage the routes for its fleet of thousands of trucks. It could not only take a mind-numbing range of variables into account and adjust quickly as opportunities or constraints arose; it could also get fresher products to store shelves faster and prevent tons of carbon emissions every year.

Quantum computing has the potential to transform these and many more visions into reality — which is why technology companies, private investors, and governments are investing billions of dollars in supporting ecosystems of quantum startups.¹ Much of the quantum research community is focused on showing quantum advantage, which means that a quantum computer can perform a calculation, no matter how arbitrary, that is impossible on a classical, or binary, computer. (See “A Quantum Glossary.”) Running a calculation thousands of times faster could create enormous economic value if the calculation itself is useful to some stakeholder in the market.

A Quantum Glossary

Tech-fluent executives should be familiar with these basic quantum computing terms as they monitor the technology and consider potential applications in their own business domains:

Qubit: A qubit is a fundamental unit of quantum information, encoded in delicate physical properties of light or matter and manipulated to produce calculations in a quantum computer. It is analogous to a bit in a classical (binary) computer.

Fault-tolerant quantum computer: These general-purpose digital quantum computers will be able to engage with a broad range of problems with flexibility and reliability. Fault-tolerant computers are proven to have cases of quantum advantage, such as Shor’s algorithm. But they may be many years away from being realized at scale because of the complex error-correction protocols required for qubits.

Noisy: The quantum computers of today and the near term are noisy, similar to the AM/FM radios that existed long before their digital equivalents were possible. Quantum noise is a much more difficult problem to solve in delicate qubits than electronic and magnetic noise in conventional computer bits.

Quantum speedup:Speedup is a way to measure the relative performance of two computers solving the same problem. Quantum speedup is the improvement that the quantum computer has over a classical competitor in solving the problem. There are many ways to define and characterize speedup. One important metric is how it scales with increasing qubit numbers.

Quantum advantage: This occurs when quantum computing solves an “impossible” problem, or rather one that a classical computer cannot solve within a feasible or realistic amount of time. The clearest cases of quantum advantage are defined via an exponentially scaling quantum speedup.

Quantum economic advantage: This occurs when quantum computing solves an economically relevant problem either differently or significantly faster than a classic computer. Quantum economic advantage can occur in cases where quantum speedup is less than exponential — that is, when the scaling is quadratic or polynomial.

However, the expense of building quantum hardware, coupled with the steady improvement of classical computers, means that the commercial relevance of quantum computing won’t be apparent unless researchers and investors shift their focus to the pursuit of what we call quantum economic advantage. A business achieves a quantum economic advantage when a quantum computer provides a commercially relevant solution, even if only moderately faster than a classical computer could, or when a quantum computer provides viable solutions that differ from what a classical computer yields.

Why Quantum Economic Advantage Matters

In a dramatic first, a team at Google led by John Martinis made headlines around the world in 2019 when their quantum machine appeared to complete a calculation (cross-entropy benchmarking) in seconds that would take tens of thousands of years on a classical computer.² Other companies have made similar claims of quantum advantage, including researchers from quantum computing startup Xanadu, which recently completed a well-defined task (Gaussian boson sampling) in under a second that would have taken the best classical supercomputer over 9,000 years.³

Hundreds of articles and many of the top minds in the field have focused on showing these kinds of quantum advantage.⁴ Their demonstrations represent important milestones in the development of quantum computers. But because they typically involve esoteric computations that might not be relevant to the kinds of problems many businesses need to solve, managers might assume that the technology isn’t yet useful and economically viable for businesses. We contend that quantum technology need not provide a quantum advantage to be economically useful, as long as it can still provide different or timelier outputs than its classical counterparts. Any quantum speedup provides an opportunity for quantum economic advantage. (See “Landscape of Quantum Economic Advantage.”)

Landscape of Quantum Economic Advantage

The diagram below shows computer algorithms and applications ranked by their potential for quantum speedup, and their estimated commercial value. Large exponential speedups to date have been demonstrated on applications with no commercial value. Applications with a moderate quantum speedup but high commercial value have a good chance of showing quantum economic advantage.

While quantum advantage will be directly useful in some cases, many of the most important uses of quantum computers will arise from providing cost-effectiveness and speed rather than from performing a calculation that is impossible on a classical computer. In other words, a quantum economic advantage may exist without a quantum advantage.⁵

Quantum speedups create an appetite for solutions to business challenges where the ability to solve complex problems extremely quickly would confer a powerful competitive advantage. Considerable effort in quantum computing is dedicated to searching for potential speedups for business problems, although evidence for a robust quantum speedup for commercially relevant problems has been elusive. Nonetheless, identifying and realizing such commercial potential provides a crucial incentive to build quantum computers and significantly influences their design.

Classical computing remains a valuable tool for solving complex problems; the pioneering work of AlexNet (deep learning) and AlphaFold (protein structure) are two examples that have high commercial value. Quantum computing might be the better tool to solve a business problem if it provides the solution more quickly than a classical competitor. Running the same calculation on both quantum and classical computers may also provide two different answers, either of which might be better. When the commercial stakes are high enough, having both classical and quantum solutions can be useful, meaning the quantum solutions might still be commercially valuable.

The question of when existing or forthcoming improved quantum computers will generate substantial commercial opportunities is therefore of immediate interest, long before clear-cut quantum advantage will become obvious in future fault-tolerant machines.⁶

Opening Valuable Commercial Frontiers

Identifying the commercial potential of a quantum computer does not require an understanding of the quantum physics that undergird the technology. Instead, the focus should be on what quantum computers can do better, faster, or perhaps differently than classical computers and on the stakeholders who will see those outcomes as valuable. The visions from the pharmaceutical and logistics sectors that we shared at the start of this article illustrate the transformative effects that quantum computing could have. The examples we highlight below show the high value that quantum computing can already unlock within existing workflows.

Make better investment decisions faster. The finance industry faces many optimization problems. Finding the optimal trading trajectory, for example, involves determining the best trading strategy for an investment portfolio over a specific period. Portfolio optimization is a valuable problem to solve, given that over $100 trillion in assets are under management globally.⁷ Even small improvements in optimization techniques are valuable because of the absolute level of assets invested.

In some cases, determining the optimal investment strategy requires a search through all possible trading trajectories. That effort grows exponentially more challenging as the number of possible securities in the portfolio and the number of opportunities to change the portfolio increase. Recent work has attempted to tackle a portfolio optimization problem that has 10¹,³⁰⁰ possible trading strategies — a quantity far in excess of the number of atoms in the visible universe (10⁸⁰).⁸

Quantum machines may be able to help.⁹ Researchers at the quantum software company Multiverse Computing compared a handful of different methods for solving the optimal trading trajectory problem. Only two of the six optimization methods provided a solution for the most complex version of the problem assessed, and the classical solution took almost 700 times longer to generate than the quantum solution. The quantum tools also yielded a different solution — one with higher profits but lower risk-adjusted returns — than the one suggested by strictly classical techniques.

These outcomes are not an example of quantum advantage, because it remains possible that an exhaustive assessment of all possible approaches to solving this problem might generate solutions that are the same as or better than the quantum approaches the researchers at Multiverse used. But it may nevertheless represent an example of quantum economic advantage, because generating a solution quickly is valuable. (See “Gaining an Edge When Time Is Money.”)

Gaining an Edge When Time Is Money

Imagine that a value-at-risk calculation could be completed in seconds as opposed to hours. How would that change an investment decision in real time? The financial advantage of informing investment decisions in near real time is a straightforward example of potential quantum economic advantage in a high-stakes application.

Quantum speedups would be valuable for a wide range of applications in the financial sector. Banks often use value-at-risk calculations to estimate the likelihood and size of potential losses.ⁱThese calculations can involve a tool called Monte Carlo simulation to run a large number of scenarios with numerous factors to model explicitly. “Quantum” Monte Carlo may help speed up these processes, which can otherwise be time-consuming.ⁱⁱ This may be particularly important in times of extreme market volatility.

Monte Carlo simulation is also used in the pricing of complex financial derivatives. Trillions of dollars in derivative contracts are traded every year for a variety of purposes, including hedging risk and enabling speculation. Most of these trades are for straightforward contracts whose pricing can be determined dynamically with easily calculated formulas. But pricing for more complex derivatives often requires a simulation that can take minutes, hours, or even days on a classical computer. A quantum speedup could enable faster calculations and open new growth opportunities in a market that is already worth billions of dollars.

Determining which of the two solutions is better — quantum or classical — remains a challenge. While financial market stakeholders currently don’t have access to fully fault-tolerant quantum computers, they also typically don’t have easy access to the classical supercomputers that are used to benchmark quantum advantage. Thus, comparing current quantum approaches to classical optimizers that are sold commercially still provides a valuable benchmark for the efficacy of a quantum approach. The best classical supercomputers might still generate a similar or superior solution in a timely manner — that is, in a time span that is less than one human lifetime.

Solve seemingly unsolvable business trade-offs. Almost any business problem that involves complex trade-offs — from day-to-day planning to long-term strategic decisions — could be well suited for quantum computers. Think of retailers that are assessing where to place certain products in their stores to maximize revenue, or educators trying to assess which questions to ask and in which order to maximize learning. These trade-off challenges are known as combinatorial optimization problems. The creation of the best-tasting recipe at a restaurant is also a combinatorial optimization, as are the logistics challenges we described at the beginning of this article. Even modest improvements can have a major impact on a company’s profitability.

Business leaders often rely on human intuition to solve these optimization problems. As businesses grow, they may come to rely on computing power to identify the best solutions. For the most complex of these problems, even today’s most powerful computers can provide only an approximation. Quantum computers could, however, perform a search through all possible combinations of arrangements or sequences to find the best solution, providing a moderate speedup over comparable classical searches.¹⁰

That’s where they can have a wide range of applications in almost any business sector. For instance, identifying the reasons for machine failure when failure rates are low is a challenging combinatorial optimization problem in advanced manufacturing.¹¹ Finding the cause of failure quickly is important, because downtime can be costly. If quantum computing can speed up the process of determining why a manufacturing process failed, it could be valuable even in settings where classical approaches could eventually find the same reason for failure.

Discover better materials. The timeliness and quality of quantum computing solutions should also improve the efficiency of R&D processes that lead to new materials and medicines, because they reduce the cost and quicken the pace of discovery relative to classical techniques.

In materials design, computers already aim to simulate the complex behavior of constituent atoms and molecules and reliably predict the structure-property relationships of molecules. In typical applications, however, classical computers face significant limits on the size of molecules they can simulate. Even simulations involving the smallest molecules are computationally intensive, and the addition of even one atom or electron can make a classical simulation drastically slower. This renders many avenues of computer-aided design unavailable for the larger molecules that are of interest to the pharmaceutical, chemical, and materials industries.

Greater computing speed confers the ability to simulate larger and more complex molecules in a practically useful time frame, and quantum computers are poised to make a significant impact in this area.¹² They are believed to be able to provide speedups for the calculations required to predict the electronic structure of atoms and molecules, although the precise nature of these speedups is currently a matter of intense debate within the scientific community.¹³

The Enduring Value of Quantum Advantage

Recent claims of quantum advantage may have no commercial application — and thus no quantum economic advantage — but they are nonetheless important because they establish the possibility that quantum computers can perform certain tasks that classical computers cannot.

The application of Shor’s algorithm is perhaps the most frequently referenced example of how quantum advantage might affect society. American mathematician Peter Shor, who recently won science’s most lucrative prize, the Breakthrough Prize in Fundamental Physics, demonstrated that a quantum computer could factor a large integer in cases where classical computers could not.¹⁴ A sufficiently large quantum computer could factor these larger integers in days or less, whereas a classical computer might need more time than it would take for the sun to run out of hydrogen.¹⁵

While this might sound abstract, the difficulty that classical computers have in factoring very large numbers is actually what enables modern encryption. The relative ease with which quantum computers could theoretically perform the calculations involved in decryption provides an example of where we expect clear-cut quantum advantage to exist. If a quantum computer could implement Shor’s algorithm, then much of the information encrypted in the past could be decoded — and that includes a great deal of encrypted data that has been stolen from organizations in cyberattacks.

This threat is remote right now, because the quantum community is years away from building fault-tolerant quantum computers large enough to use Shor’s algorithm to break codes. But it warrants the attention of managers in almost all industries, who will eventually make up a large market for new, quantum-robust encryption standards. Here, quantum computers’ ability to generate random numbers can play a role in supporting more advanced cybersecurity defenses.

Quantum computers are probabilistic, which means they can generate truly random numbers.¹⁶ Their classical counterparts, in contrast, are deterministic and thus can generate only pseudo-random numbers. However, even when a quantum computer can do something with a clear practical application that a classical computer cannot do, business leaders must still weigh the trade-offs. In some cases, the classical approach may be deemed good enough, limiting the organization’s incentives to switch to quantum. This may be the case with some other potential applications for quantum random number generators (RNGs), such as in the lottery and casino gambling sectors.¹⁷

Lotteries select winning numbers either electronically or physically, such as by drawing numbered balls from bins. The resulting numbers are not truly random. Because the process is deterministic, it would be possible for someone to predict the numbers if they knew something about the underlying process that generated them, or to manipulate the process to generate certain numbers.

While such manipulations have indeed occurred, lotteries will probably not abandon their current processes anytime soon, despite what appears to be a clear argument for using quantum computing. First, lottery fraud through manipulation of the number-generating process is rare, perhaps due to the significant costs of getting caught, strict rules against insiders participating in lotteries, and the existence of powerful analytical tools to detect fraud. Second, in the absence of fraud, pseudo-RNGs yield results that are often indistinguishable from their quantum counterparts. While it may be theoretically possible to predict the outcome of a lottery before drawing balls from multiple bins, it is practically infeasible to do so. Lotteries thus have little incentive to switch to quantum machines. Managers confronting similar cases where quantum computing provides a capability that is entirely lacking in classical computing will likewise need to weigh the relative costs and benefits of the optimal versus the good-enough solution.

Preparing for Quantum Computing in Business

To fulfill its promise and create new value and new commercial opportunities, a quantum machine does not have to accomplish a currently impossible task. It only needs to accomplish something useful. That time is coming, as billions of dollars in investments from venture capitalists, major tech companies, and national governments fuel rapid improvements in quantum computers that will make them more efficient than classical ones.

The consensus among most government and industry players seems to be that large-scale fault-tolerant quantum computers almost certainly won’t appear before the end of this decade. Although it may take years for commercially relevant quantum computers to exist at scale, business leaders can already take several steps to prepare their businesses for this era.

Make a list of your “If we could only …” or “What if …?” challenges. Most businesses have such daunting challenges but rarely address them because they are too resource-intensive and those resources have better short-term uses. The speedups and alternative solutions from quantum computing can make the transformative solutions to these problems feasible. What elements of your business are constrained by combinatorial optimization? And how much would a solution be worth to you?

Help your organization become quantum ready. We anticipate that the impact and scale of commercial applications will accelerate rapidly once fully fault-tolerant quantum computers emerge. Organizations have several ways to prepare themselves. Companies with a higher likelihood of lucrative applications — financial services companies, pharmaceutical manufacturers, and makers of specialty materials — can invest in hardware and software and develop a network of experts. Other organizations can familiarize themselves with the basics of quantum computing, connect with academics, and start to train team members.

Start experimenting now.Companies can already allocate a portion of R&D resources to experiment with near-term quantum hardware. They can set up problems in ways the computers can understand, even if existing hardware might not allow them to capitalize on those opportunities yet. These investments are important to the ongoing development of the technology: Quantum computing will not scale solely through academic research.

The startups we have worked with in the Creative Destruction Lab at the University of Toronto’s Rotman School of Management have already achieved short-term benefits from experimenting with quantum — for example, by creating innovations that have led to new materials. The long-term benefit from experimenting with quantum computing today is that a company will be prepared when sufficiently coherent, fault-tolerant quantum computers exist at scale. Those organizations will have a considerable first-mover advantage and be well positioned to capture new opportunities as this emerging technology comes to market.

Francesco Bova is an associate professor at the Rotman School of Management at the University of Toronto. Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School of Management. Roger Melko is a professor in the Department of Physics & Astronomy at the University of Waterloo and an associate faculty member at the Perimeter Institute for Theoretical Physics.

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Copyright © Massachusetts Institute of Technology, 2023. All rights reserved.

1. C. Metinko, “Quantum Technology Gains Momentum as Computing Gets Closer to Reality,” Crunchbase, May 13, 2022, http://news.crunchbase.com; “What America’s Largest Technology Firms Are Investing In,” The Economist, Jan. 22, 2022, http://www.economist.com; and M. Aboy, T. Minssen, and M. Kop, “Mapping the Patent Landscape of Quantum Technologies: Patenting Trends, Innovation, and Policy Implications,” International Review of Intellectual Property and Competition Law 53, no. 10 (November 2022): 853-882.

2. F. Arute, K. Arya, R. Babbush, et al., “Quantum Supremacy Using a Programmable Superconducting Processor,” Nature 574, no. 7779 (Oct. 24, 2019): 505-510.

3. L.S. Madsen, F. Laudenbach, M.F. Askarani, et al., “Quantum Computational Advantage With a Programmable Photonic Processor,” Nature 606, no. 7912 (June 2, 2022): 75-81.

4. A.K. Fedorov, N. Gisin, S.M. Beloussov, et al., “Quantum Computing at the Quantum Advantage Threshold: A Down-to-Business Review” (preprint, submitted in March 2022), https://arxiv.org; L. Mueck, C. Palacios-Berraquero, and D.M. Persaud, “Towards a Quantum Advantage,” Physics World, Feb. 5, 2020, https://physicsworld.com; and S. Chen, “Quantum Advantage Showdowns Have No Clear Winners,” Wired, July 11, 2022, http://www.wired.com.

5. F. Bova, A. Goldfarb, and R.G. Melko, “Quantum Economic Advantage,” Management Science, Articles in Advance, published online Dec. 2, 2022.

6. “Near term” in this context refers to the quantum computers of the next few years, which will be better than today’s but still not fully fault tolerant.

7. “Value of Assets Under Management Worldwide in Selected Years From 2003 to 2021,” Statista, July 8, 2022, http://www.statista.com.

8. “Wall Street’s Latest Shiny New Thing: Quantum Computing,” The Economist, Dec. 19, 2020, http://www.economist.com.

9. S. Mugel, C. Kuchkovsky, E. Sanchez, et al., “Dynamic Portfolio Optimization With Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks,” Physical Review Research 4, no. 1 (January 2022): 1-12.

10. L.K. Grover, “A Fast Quantum Mechanical Algorithm for Database Search,” in “STOC ’96: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing” (Philadelphia: Association for Computing Machinery, 1996).

11. F. Bova, A. Goldfarb, and R.G. Melko, “Commercial Applications of Quantum Computing,” EPJ Quantum Technology 8 (2021): 1-13.

12. S.N. Genin, I.G. Ryabinkin, N.R. Paisley, et al., “Estimating Phosphorescent Emission Energies in Ir(III) Complexes Using Large-Scale Quantum Computing Simulations” (preprint, submitted in November 2021), https://arxiv.org. Since atoms and electrons themselves are quantum objects, they are natural candidates for study by quantum computers. In fact, such “simulation” (or emulation) was the first value proposition made for a quantum computer by California Institute of Technology physicist Richard Feynman in 1982.

13. S. Lee, J. Lee, H. Zhai, et al., “Is There Evidence for Exponential Quantum Advantage in Quantum Chemistry?” (preprint, submitted in August 2022), https://arxiv.org.

14. P.W. Shor, “Algorithms for Quantum Computation: Discrete Logarithms and Factoring,” in “Proceedings 35th Annual Symposium on Foundations of Computer Science” (Santa Fe, New Mexico: IEEE, 1994).

15. R. Van Meter and D. Horsman, “A Blueprint for Building a Quantum Computer,” Communications of the ACM 56, no. 10 (October 2013): 84-93.

16. It should be noted that quantum computers are not the only quantum technology that can produce true random numbers.

17. J. Melia, “The Odds Are in Quantum Security’s Favor,” QuintessenceLabs, March 2, 2017, http://www.quintessencelabs.com; and “Rigged Random Number Generators,” QuintessenceLabs, May 27, 2016, http://www.quintessencelabs.com.

i. Bova, Goldfarb, and Melko, “Commercial Applications of Quantum Computing.”

ii. D. Layden, G. Mazzola, R.V. Mishmash, et al., “Quantum-Enhanced Markov Chain Monte Carlo” (preprint, submitted in March 2022), https://arxiv.org; and S. Chakrabarti, J. Krishnakumar, G. Mazzola, et al., “A Threshold for Quantum Advantage in Derivative Pricing” (preprint, submitted in December 2020), https://arxiv.org.

Article link: https://sloanreview.mit.edu/article/the-business-case-for-quantum-computing/

March 16, 2023 | Article

By Fadesola Adetosoye, Danielle Hinton, Gayatri Shenai, and Ethan Thayumanavan

Affordable broadband with wraparound support could expand access to cost-efficient virtual health for underserved communities. Seven actions could help state and local leaders unlock this opportunity.

Listen to Article: https://on.soundcloud.com/QAzAs

Article link: https://www.mckinsey.com/industries/public-and-social-sector/our-insights/virtual-health-for-all-closing-the-digital-divide-to-expand-access?

An electronic health record (EHR) is software that’s used to securely document, store, retrieve, share and analyze information about individual patient care. It enables a digital version of your health record. The Federal Electronic Health Record Modernization (FEHRM) office, along with other federal agencies, is implementing a single, common federal EHR to enhance patient care and provider effectiveness.

A federal EHR means as a provider, you will be able to access patient information entered into the EHR from different doctors, pharmacies, labs and other points of care throughout your patient’s care. There is recognition across the board that the federal EHR saves providers time and enables more standard workflows to support enhanced clinical decision-making and patient safety.

This means you benefit:

• Your patients spend less time repeating their health history, undergoing duplicative tests and managing printed health records.
• You have access to patient data such as service treatment records, Service medals and honors, housing status and other information to ensure patients receive all their earned benefits as they transition to civilian life in a seamless, timely fashion.
• You can start conversations more productively with your patients since you already have a more comprehensive picture of their health before their appointments.
• You will make more informed decisions about your patient’s care as you have access to more relevant data.
• You will have a more seamless care experience. Regardless of whether your patient gets care from the Department of Defense, Department of Veterans Affairs, Department of Homeland Security’s U.S. Coast Guard or another health care system participating in the joint health information exchange, you will be able to easily access and exchange patient health information to enhance quality of care and satisfaction.

Learn more about how the federal EHR is enhancing delivery of care for providers like you.

Article link: https://www.fehrm.gov/enhancing-care-delivery

By Sydney J. Freedberg Jr. on March 14, 2023 at 4:55 PM

WASHINGTON — Despite spending over $800 billion a year, the Defense Department chronically under-invests in cutting-edge technology and struggles to leverage the even greater investments being made by private-sector innovators in fields from AI and networks to space and biotech.

That’s the verdict of a new report by the Reagan Foundation and Institute, released Monday. But there’s good news: Both in the report and at a conference the foundation hosted Tuesday, experts from the Pentagon, Capitol Hill, and industry were full of ideas for improvements.

The central insight from the report and morning panels: For all the reforms of recent years, the US government as a whole — not just the Defense Department — is still an unappealing customer for the most innovative firms in the economy. Even a modest government investment in a nascent, promising technology at a critical, early phase could encourage matching or even greater investment from the private sector, but the government isn’t good at making those targeted investments. So Congress needs to provide more stable, flexible funding, freed from the restrictions and delays of a frequently gridlocked appropriations process — and bureaucrats need the courage to use the authorities Congress has already granted.

“What happens many times is those tools are there, [but] they don’t get used,” said Rep. Rob Wittman, R-Va., chairman of the tactical land and air forces subcommittee. “There are OTAs and MTAs — Other Transaction Authorities and Mid-Tier [Acquisition] — that aren’t used enough.”

“The problem is sometimes, too, Congress says, ‘whoa, whoa, wait a minute’… because we like to have the control,” Wittman continued. That forces Pentagon programs to slow down and wait for a budget cycle that takes a year to grind through Congress, on top of years of planning and infighting at the Pentagon.

RELATED: Oversight or overkill? DoD faces new congressional order to detail Mid-Tier Acquisitions

“It’s the culture, it’s the incentives,” emphasized retired House Armed Services Committee chairman Mac Thornberry, who advised on the scorecard. “It’s not just the Pentagon. It’s Congress too.”

Structured like a report card, the Reagan report grades the government in 10 major areas and dozens of subcategories, all related to what it calls the “National Security Innovation Base” — a construct carefully crafted to include innovative companies, laboratories, and universities outside the traditional defense industry. The scorecard is actually somewhat sanguine about America’s overall technological leadership in the world, giving it an A- despite a surge of patents, PhDs, and tech investment in China. But it all goes downhill from there.

Along with a bunch of Bs and Cs, the Reagan scorecard gave the country as a whole a D+ when it comes to fostering top-flight technical talent — a complex issue tied to education and immigration policies like the H-1B visa. Still worse, it gave the US a blunt D in what it calls “customer clarity”: In essence, how well the government connects to industry, not just through issuing policy statements and publishing strategies, but, most importantly, by providing tangible funding that gives innovators an incentive to work on tech the military wants.

A section of the Reagan Institute’s “scorecard” (Reagan Institute graphic)

That D for being a lousy customer breaks down into three subcategories, the scorecard continues. The government gets a C+ for patchy communication of policy, hampered by many agencies’ slowness in issuing public strategies and Congress’s slowness to confirm key officials, leaving key leadership slots vacant. The government gets a C- for making only limited use of streamlined acquisition processes that bypass the sclerotic traditional system. And it gets an F — the only failing grade in the entire report card — for providing erratic and ill-focused funding, with Congress routinely passing appropriations weeks or months late and frequently plussing-up traditional programs instead of new technology.

“The big companies have this problem with this inconsistent demand signal from government,” making it hard to plan long-term investments, said Eric Fanning, former Army Secretary (and Acting Air Force Secretary) who’s now CEO of the Aerospace Industries Association. “The smaller companies have all sorts of other barriers,” especially with the administrative overhead necessary to meet government requirements. And, he said, innovators whose technology has wide commercial applications — like drones or artificial intelligence — can afford to give up on government sales if the bureaucracy proves too difficult a customer.

Being a good customer is crucial, agreed Sreenivas Ramaswamy, a senior advisor at the Commerce Department. “I’ve been in this role for two years,” he said, “and one of things I’ve seen … is how much people underestimate the role of the Defense Department in issuing the demand signal.”

A Government Solution

So how to fix this? The government needs to make “bigger, bolder, more flexible bets” on emerging, high-potential technologies, the report recommends, and it needs to “foster stronger relationships” with the private-sector investors that drive most innovation in the modern economy, using government funding as seed money to draw larger private investments.

This does not require massive government purchases to build up entire industries, Ramaswamy emphasized. Instead, he argued, the government can help kickstart new sectors by buying a relatively small amount of new technology early on, when the price is still too high for most commercial applications.

But encouraging and exploiting private-sector investment is not something the government usually thinks about. “It wasn’t until I became chairman that I became focused on outside investment and talked to investors,” said Thornberry, “[because] we don’t vote on that.”

“Government… as a whole does not understand the private capital that is leveraged by the Department of Defense,” said Fanning. A modest Pentagon purchase can incentivize the private sector to make its own investments in the hope of landing future sales — if those investors feel they can rely on that future government funding to materialize.

So what mechanisms would work for this kind of funding? Congress and the Pentagon have enacted significant reforms, like Mid-Tier Acquisition — although there’s been recent pushback about MTA on Capitol Hill — and the Software Pathway. But for both fiscal and cultural reasons, the experts said at today’s conference, it’s been a struggle to scale these up to have a major impact.

“The government is not an easy customer to move from prototype … to production,” said Fanning, “especially if you’ve developed it yourself” outside the government’s R&D pipelines.

“Starting prototype programs has never been easier. This is a good thing,” said assistant Army secretary Douglas Bush, the service’s chief acquisition officer. “[But] it’s still difficult to just carve out the money to take those prototypes and put them into production because again, when you get to that stage, you’re now competing with everything else, fighting for those limited production dollars… It’s a shark tank.”

“Congress often does some of the hard work of pushing innovations and innovative solutions on the Department,” Bush added. “That’s good, [but] we need to do a better job on that on our own.”

“There’s no question, there is a cultural challenge [about] being able to take a risk,” Thornberry said. But often, he said, Congress reinforces bureaucratic over-caution by hammering officials who tried something new that didn’t work out, or by imposing strict controls up front.

“We need to do our funding a little differently,” he said, “so that there is a pool of money for a particular purpose” — for example, artificial intelligence — that isn’t tied to a particular program, so Pentagon officials can rapidly respond to emerging opportunities in fast-paced tech sectors.

That way, “you can move fast [without] asking ‘Mother may I?’ two years in advance,” Thornberry said. “There’s got to be parameters around it, but I’d really like to give Doug [Bush] some pots of money for a purpose and then let him make the adjustments as the technology is developed.”

“That would be amazing,” Bush said. The audience burst into laughter.

Article link: https://breakingdefense-com.cdn.ampproject.org/c/s/breakingdefense.com/2023/03/us-gets-an-f-for-erratic-unfocused-funding-of-defense-innovation-says-reagan-foundation/?

As our industry continues to move toward more people-focused care, we’re driven to improve patient engagement and enhance outcomes to create a better healthcare experience for everyone. By making it more convenient for people to access their health data wherever and whenever they need it, we can help empower individuals and clinicians to make better health and well-being decisions.

“We imagine a world where individuals are empowered and have their healthcare data at their fingertips,” says Rob Helton, vice president of platform development, Oracle Health. “They have ownership of their data and are able to review and add to it. They can choose to share it, and who it gets shared to. We believe by putting patients in control of their data, they’ll become active participants in managing their health.”

The US federal government is continuing its push to place patients at the center of their own care, including ensuring patients have access to all of their electronic health information(EHI). The 21st Century Cures Act Information Blocking Rule, among other things, ensures patients have electronic access to their EHI in the manner the patient chooses, a right originating in HIPAA’s individual right of access.

Additionally, the federal government is setting minimum standards for health IT (HIT) that chooses to participate in the HIT certification program under the Office of the National Coordinator for HIT. While most healthcare organizations are striving to comply, some struggle to stay on top of the extra work needed to reach the full potential of interoperability. Luckily, technology can help streamline the process, reducing the regulatory burden on providers and making it easier for people to securely access their data.

Automating patient health data access

Historically, for a patient to use a health app of their choosing, a health system would have to review and approve the health app beforehand. However, with Oracle Health’s newly released functionality, called consumer automatic provision, this process is streamlined enabling patients easier access to their health data.

Consumer automatic provision allows consumer-facing health apps to be immediately provisioned for FHIR API endpoints upon registration. From there, health data stored within the electronic health record (EHR) system can be retrieved by the app upon explicit authorization from the patient or their authorized representative. Patients maintain full control over authorizing an app to access any of their EHI or only certain data types.

Advancing meaningful health insights and maintaining regulatory compliance

When people can use apps on their smartphones to measure and record critical information like physical activity, weight, sleep, heartbeat, and blood sugar level or track their medicine to ensure they don’t miss a dose, they can make better decisions about their health.

In addition to the clinical benefits, the consumer automatic provision aligns with several regulatory requirements that impact healthcare providers, including rules regarding the Medicare Promoting Interoperability program, health IT certification, HIPAA privacy, and information blocking and sharing.

The bottom line of these regulations is patients have the right to access their information electronically for any purpose they may choose, and that right should be facilitated by health IT developers and healthcare providers. Automatic provisioning of successfully registered consumer apps for your FHIR API endpoint helps patients access health information through any app of their choice in a timely fashion without any additional action by your organization.

Protecting patient health data

Protections are built into the FHIR APIs to help keep health data private and secure, but there are potential risks involved with authorizing the disclosure of EHI to a third-party app. While these risks are largely mitigated by protections built into the API connections and access, we all have a responsibility to help educate patients on how to keep their data safe. Although the constraints and protections that Oracle Health can put in place under the 21st Century Cures Act regulations are limited, here are the actions we’re taking to further protect consumer privacy and make the health app ecosystem more reliable and secure for consumers.

• Employing industry-standard privacy and security protections as a built-in feature of the APIs
• Developing enhancements to provide consumers with details of an app’s posture in relation to certain security-related best practices as part of the authorization user interface
• Instructing developers connected with Oracle Health APIs to align with industry best practices for the safe and ethical handling of EHI communicated by the CARIN Alliance
• Implementing patient-directed scope selection and redaction in the consumer app authorization workflow

The CARIN Alliance provides useful tips for consumers including:

• Only download from trusted app stores
• Always review app privacy terms and conditions
• Know the app developer
• Find out if the app is sharing or selling your data
• Use strong and unique credentials

View the full list of recommendations.

Streamlining patients’ ability to access personal health data through an app of their choice is an important part of prioritizing the human experience in healthcare. Consumer automatic provision is one step of several that we’re taking to help patients control their own health and care, enhance clinical decision-making, and improve regulatory compliance for greater interoperability.

For more information, check out our resources on information blocking.

Related content:

• How an open EHR advances usability for healthcare providers
• The prior authorization process: What makes it painful and how to improve it
• HIPAA violations and consequences: How the recent updates impact the healthcare industry

Article link: https://blogs.oracle.com/healthcare/post/helping-patients-protect-their-electronic-health-information?

By EDWARD GRAHAMMARCH 8, 2023

The official cautioned that “modernization would require VistA be rewritten almost from scratch.”

The Department of Veterans Affairs must continue to maintain its legacy health information system as it moves forward with its multi-billion dollar electronic health record modernization project, even though the decades-old software is too outdated to serve as a viable long-term solution, a VA official said during a House Veterans’ Affairs subcommittee hearing on Tuesday. 

Daniel McCune, VA’s executive director of software product management, told members of the committee’s Technology Modernization Subcommittee that the Veterans Health Information Systems and Technology Architecture—or VistA—“has served VA and veterans for over 40 years, and we are aware of its limitations.” But he added that, as the agency continues to slowly deploy the new Oracle Cerner EHR system, “VistA remains our authoritative source of veteran data.”

Modernizing the EHR system has been a priority for VA for some time, albeit one with few tangible results until now. Prior to awarding Cerner the contract for the new EHR system—known as Millennium—in 2018, VA spent years attempting to modernize VistA. During a House Veterans’ Affairs Committee hearing on Feb. 28, U.S. Comptroller General Gene Dodaro told lawmakers that VA has spent “over $1.7 billion dollars for failed predecessor electronic healthcare record systems that either failed, or did not come to fruition.”

Rep. Matt Rosendale, R-Mont.—the chairman of the subcommittee—said VistA “must be maintained,” and called for the VA to identify “key areas of VistA that need to be modernized and are feasible to undertake.” Rosendale previously introduced legislation in January that would terminate the VA’s transition to the Oracle Cerner system if the agency “cannot demonstrate significant improvement” in its deployment. 

“The reality is, regardless of whether the Oracle Cerner implementation can be accomplished, and regardless of how we feel about that, the VA will probably continue to rely on VistA for at least another decade, and some of the elements of VistA will probably never go away because no replacement even exists,” Rosendale added.

Rep. Sheila Cherfilus-McCormick, D-Fla.—the panel’s ranking member—also agreed that maintaining VistA “is imperative” for the VA, noting that “there are functions of VistA that are not related to the EHR and would likely exist long after the EHR is replaced.”

But Cherfilus-McCormick expressed concerns about the current state of VistA, including cybersecurity risks, data management issues and a code-base “considered to be obsolete by many.”

“I’m not here to say that the Oracle Cerner approach in EHRM is going well, but I’m not sure returning to VistA is correct either,” Cherfilus-McCormick added. 

McCune agreed that maintaining VistA during the deployment of the new Oracle Cerner EHR system was essential, noting that VA has already been working to enhance the legacy system by “standardizing VistA code,” implementing an API gateway and transitioning portions of the system to a cloud environment. He noted that “20 instances of Vista have been moved to cloud, with an additional 54 planned this year.”

But McCune told lawmakers that VistA is “an old technology ill-suited for the modern digital age,” and added that “modernization would require VistA be rewritten almost from scratch, at a great cost and great risk.”

McCune highlighted the fact that VistA is written in an old programming language called Mumps, which he noted “is not taught in computer science classes.” While he said VA had been able to retain Mumps programmers “much longer than a typical workforce,” he added that “approximately 70% of our Mump programmers today are retirement eligible, and we have few options to hire or contract additional ones.”

The rollout of the new Oracle Cerner EHR system, however, has been fraught with technical issues, patient safety concerns, delays and cost overruns since it first went live at the Mann-Grandstaff VA Medical Center in Spokane, Washington in 2020. A report released in July 2022 by the VA’s Office of Inspector General found that the new software deployed at the Spokane facility inadvertently routed more than 11,000 orders for clinical services to an “unknown queue” without alerting clinicians, resulting in “multiple events of patient harm.” 

VA announced in October that it was pausing further rollouts of the Oracle Cerner software until June 2023 to give the agency time to “fully assess performance and address every concern” with the system’s deployment. The agency announced last month that it was also pushing back the planned deployment of the Oracle Cerner EHR system at the VA Ann Arbor Healthcare System until later this year or in 2024. The go-live for Ann Arbor was originally scheduled to happen in June of this year.

Ongoing issues with the new software’s deployment have only increased lawmakers’ worries about the viability and usability of the new system. Rosendale, in particular, expressed concern during the hearing about reported disparities regarding patient safety when it came to medical facilities continuing to use VistA, and those that have transitioned to the Oracle Cerner EHR system. 

Rosendale cited VA’s patient safety reports related to its EHR systems and noted that—for the 166 medical facilities still using VistA—the agency received 12,644 reports in 2020; 14,637 in 2021; and 9,211 in 2022. For the two years after Cerner went live in Spokane, Rosendale said VA received a combined 1,033 patient safety reports at that facility alone.

“That’s over 500 reports per year from one hospital using Cerner,” he said. “One hospital, compared to an average of 55 reports annually from the VistA hospitals.”

Rosendale said the data showed that, as a result of the Oracle Cerner EHR system’s beleaguered rollout, Mann-Grandstaff has “become the most dangerous VA hospital in the country.”

Article link: https://www.nextgov.com/it-modernization/2023/03/official-vas-legacy-ehr-must-be-maintained-not-long-term-solution/383753/

By CHRIS BROWN MARCH 10, 2023 03:25 PM ET

COMMENTARY | Many agency chief data officers would like to see the creation and appointment of a federal CDO.

Here’s the essential irony behind data in the federal government: Agencies have captured and cataloged plenty of it, but they still aren’t doing nearly enough with it to maximize its value.

The current administration is attempting to fix this: In its Federal Data Strategy 2021 Action Plan, the White House calls for agencies to pursue a “robust [and] integrated approach to managing and using data” while still protecting security, privacy and confidentiality. Its ten-year vision includes goals such as the initiation of data governance, planning and infrastructure; the optimization of self-service analytics capabilities; and the execution of proactive, evidence-based decisions.

Then, late last year, the Data Foundation released its annual survey of federal chief data officers to get a sense of agencies’ responses to the plan. Findings revealed that less than one-quarter of CDOs are making notable progress in implementing it.

Another intriguing finding which stood out was that three of five survey participants would like to see the creation and appointment of a federal CDO. Such leadership could lend needed vision and support to help them achieve key components of success, which include the development of data strategy and governance (as cited by two-thirds of the CDOs); the facilitation of data-driven decision-making at all levels (nearly one-half); and the enabling of data-sharing (also nearly one-half).

This leads to a central question: If the White House established a federal CDO position, what should be the top priorities on his or her “to do” list? I’d recommend the following:

Encourage agency CDOs to “think different”

To borrow from the iconic Apple ad slogan, a federal CDO could start by encouraging agency-level CDOs to shift their focus. Too many spend most of their time attempting to catalog their data, and then set policies for it. True, those CDOs need to play a leadership role in governance. But they should also drive teams to leverage data for innovation, by helping them discover how to securely share it and maximize its value. Unfortunately, this isn’t happening nearly enough.

Much of the problem is rooted in fixed mindsets and organizational culture. Data owners build a “hoarding mentality” about data—they want to gather lots of it to create impressive inventories and then lord over this digital fiefdom. But that’s as far as it goes. Agency CDOs do not take the critical next step of making data accessible and shareable. A federal CDO must champion the idea that cataloging data is really just a starting point, and hardly the endgame.

Direct agencies to liberate users with free-flowing—but secure—access to data

Agency CDOs often resist opening up data because they fear the consequences. They worry about all of the “what ifs,” e.g., “what if ‘top secret’ information trickles down to ‘secret?’” This freezes any forward progress. They don’t want to overshare and invite potential risks.

Much of the problem is rooted in the continued dependence on siloed legacy systems that require endless paperwork and labor-intensive oversight. Then, there are the highly antiquated security approaches that are still in play. To successfully protect massive amounts of data while still making it readily accessible and shareable, CDOs need to adopt an attribute-based access control model.

Simply defined, ABAC provides dynamic, real-time data access decisions and—coupled with tools such as data-use agreements and data-activity logging—ensures authorized users can proceed without security-based bottlenecks or friction while avoiding new risks for their agencies. ABAC is based on user and data attributes for data security, determining who should have access to sensitive data and whether they are seeking it for the right reasons. With this, it dynamically empowers authorization to sensitive data, applications and databases in real-time.

Issue report cards that grade real progress

Whenever the government establishes a new “fed czar,” we see lots of lofty proclamations and visionary statements. But we don’t see enough follow-through that results in actual, meaningful progress. A federal CDO needs to be empowered enough to inspire change.

Fully funded programs aimed at the improved securing, sharing and leveraging of data would help, obviously. And the federal CDO could make agency counterparts more accountable by developing metrics which measure how effectively they are deploying data sets, enabling end users and so on, and then by issuing regular report cards that publicly grade agencies on these key capabilities.

Certainly, the appointment of a federal CDO may result in much value-generating impact. But it might end up as yet another lost opportunity. A federal CDO who is genuinely empowered should lead a charge that leaps far beyond simply governance and proclamations, and instead goes toward optimal innovation. Doing so requires the enforcement of security controls, so people can fully utilize and share data sets. Without this, data is worthless. But with it, the federal CDO would truly drive agencies to not only “think different,” but do different.

Chris Brown is the chief technology officer for Immuta.

Article link: https://www.nextgov.com/ideas/2023/03/top-3-priority-recommendations-first-federal-chief-data-officer/383858/

By CHRIS BROWN MARCH 10, 2023 03:25 PM ET

COMMENTARY | Many agency chief data officers would like to see the creation and appointment of a federal CDO.

Here’s the essential irony behind data in the federal government: Agencies have captured and cataloged plenty of it, but they still aren’t doing nearly enough with it to maximize its value.

The current administration is attempting to fix this: In its Federal Data Strategy 2021 Action Plan, the White House calls for agencies to pursue a “robust [and] integrated approach to managing and using data” while still protecting security, privacy and confidentiality. Its ten-year vision includes goals such as the initiation of data governance, planning and infrastructure; the optimization of self-service analytics capabilities; and the execution of proactive, evidence-based decisions.

Then, late last year, the Data Foundation released its annual survey of federal chief data officers to get a sense of agencies’ responses to the plan. Findings revealed that less than one-quarter of CDOs are making notable progress in implementing it.

Another intriguing finding which stood out was that three of five survey participants would like to see the creation and appointment of a federal CDO. Such leadership could lend needed vision and support to help them achieve key components of success, which include the development of data strategy and governance (as cited by two-thirds of the CDOs); the facilitation of data-driven decision-making at all levels (nearly one-half); and the enabling of data-sharing (also nearly one-half).

This leads to a central question: If the White House established a federal CDO position, what should be the top priorities on his or her “to do” list? I’d recommend the following:

Encourage agency CDOs to “think different”

To borrow from the iconic Apple ad slogan, a federal CDO could start by encouraging agency-level CDOs to shift their focus. Too many spend most of their time attempting to catalog their data, and then set policies for it. True, those CDOs need to play a leadership role in governance. But they should also drive teams to leverage data for innovation, by helping them discover how to securely share it and maximize its value. Unfortunately, this isn’t happening nearly enough.

Much of the problem is rooted in fixed mindsets and organizational culture. Data owners build a “hoarding mentality” about data—they want to gather lots of it to create impressive inventories and then lord over this digital fiefdom. But that’s as far as it goes. Agency CDOs do not take the critical next step of making data accessible and shareable. A federal CDO must champion the idea that cataloging data is really just a starting point, and hardly the endgame.

Direct agencies to liberate users with free-flowing—but secure—access to data

Agency CDOs often resist opening up data because they fear the consequences. They worry about all of the “what ifs,” e.g., “what if ‘top secret’ information trickles down to ‘secret?’” This freezes any forward progress. They don’t want to overshare and invite potential risks.

Much of the problem is rooted in the continued dependence on siloed legacy systems that require endless paperwork and labor-intensive oversight. Then, there are the highly antiquated security approaches that are still in play. To successfully protect massive amounts of data while still making it readily accessible and shareable, CDOs need to adopt an attribute-based access control model.

Simply defined, ABAC provides dynamic, real-time data access decisions and—coupled with tools such as data-use agreements and data-activity logging—ensures authorized users can proceed without security-based bottlenecks or friction while avoiding new risks for their agencies. ABAC is based on user and data attributes for data security, determining who should have access to sensitive data and whether they are seeking it for the right reasons. With this, it dynamically empowers authorization to sensitive data, applications and databases in real-time.

Issue report cards that grade real progress

Whenever the government establishes a new “fed czar,” we see lots of lofty proclamations and visionary statements. But we don’t see enough follow-through that results in actual, meaningful progress. A federal CDO needs to be empowered enough to inspire change.

Fully funded programs aimed at the improved securing, sharing and leveraging of data would help, obviously. And the federal CDO could make agency counterparts more accountable by developing metrics which measure how effectively they are deploying data sets, enabling end users and so on, and then by issuing regular report cards that publicly grade agencies on these key capabilities.

Certainly, the appointment of a federal CDO may result in much value-generating impact. But it might end up as yet another lost opportunity. A federal CDO who is genuinely empowered should lead a charge that leaps far beyond simply governance and proclamations, and instead goes toward optimal innovation. Doing so requires the enforcement of security controls, so people can fully utilize and share data sets. Without this, data is worthless. But with it, the federal CDO would truly drive agencies to not only “think different,” but do different.

Chris Brown is the chief technology officer for Immuta.

Article link: https://www.nextgov.com/ideas/2023/03/top-3-priority-recommendations-first-federal-chief-data-officer/383858/

By Molly Weisner Thursday, Mar 2

As private sector companies and academia shoot for the moon when it comes to exploring the potential of artificial intelligence, in the federal government, discussions are more down to earth.

The heights to which AI technology has ventured is both awesome and scary, panelists discussed at a conference last month hosted by the Advanced Technology Academic Research Center, which focuses on technology and government. Take recent headlines about ChatGPT,Microsoft’s uncanny chatbot, and Stanford’s “Woebot” robo-therapists, for example. In the government, AI is a nascent novelty, though agencies and the military say they already see use cases within reach.

RELATED

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OpenAI’s ChatGPT surpassed 1 million registered users within a week of its launch in late 2022.

By Colin Demarest

“What I’m most excited about is not necessarily the sexiest AI technologies … things that are showing up in science magazines,” said Greg Singleton, chief artificial intelligence officer at the U.S. Department of Health and Human Services. “What I’m really excited about for the department is seeing where we can apply these technologies to the general business that we do.”

That might mean holding off on the self-driving cars and robot secretaries for now and using AI instead to pick the low-hanging fruit of automating burdensome tasks that have grown for agencies while their workforces have not.

HHS employs 80,000 people and saw a nearly 30% increase in Medicaid/CHIP enrollment right before the pandemic.

“Our workforce is dedicated, they want to do a good job, but you have challenges when people are overloaded by processes, overloaded by volume, overloaded by batch transactional communications,” Singleton said at the ATARC conference.

The department has identified dozens of scenarios that could benefit from AI, including an automatic tool for finding chemical names in biomedical literature, a virtual assistant for finding grants, and a bot to ensure disability accommodations follow an employee who is being reassigned or promoted.

There are other agencies facing similar problems of growing workloads and deficits in manpower. The Social Security Administration’s workforce is at a 25-year low while the number of Americans receiving benefits has grown 20% since 2010. At the Federal Deposit Insurance Corporation, 38% of its workforce will be able to retire by 2027. 

These examples present opportunities for AI to render simple, clerical tasks more efficient through existing technology that is relatively quick and easy to scale, panelists said.

Failure is not an option

In October, President Joe Biden signed a bill that would require a government-led AI training program for its acquisition workforce that would be regularly updated. The government is also buying AI technology experimentally with the focus on research-based deals as opposed to hardware or software contracts, which is more mindful of risk, according to analysis by the Brookings Institute.

“The federal government is not a Silicon Valley start-up; it does not look kindly on failure, no matter the potential future payoff,” wrote Corin Stone, a former Office of the Director of National Intelligence senior official, in an article for Just Security. “The government is understandably hesitant to take risk, especially when it comes to national security activities and taxpayer dollars.

Panelists noted that appetites for AI can differ across agencies, as do budgets and resources. Congress has said that regardless, the government overall needs to play catch up on AI, with Sen. Joe Manchin saying it would be “disastrous” if the U.S. failed to be ready for China and Russia’s scaling up of cyber warfare in a hearing last year.

“It’s not just about rudimentary tasks,” said Bonnie Evangelista, who works in the Office of the Chief Digital and Artificial Intelligence Officer at the U.S. Department of Defense. “Even from a government perspective, I think there are some of us who are trying to go big and go bold, but it can be hard from a workforce perspective. It’s hard to scale because of culture.”

AI may also draw concerns from those who have misgivings about its ethical use and from union representatives who may want to fully understand repercussions for the workforce, including any possible reduction of work hours.

RELATED

ChatGPT hints at potential for artificial intelligence in government

Agency leaders must clear up the common misconception that AI/ML infrastructure, data governance and efficiency must be perfectly aligned to get started.

By Jay Meil

Research suggests that AI can “outperform workers in an increasing set of complex tasks mainly done by educated workers,” though technology has also been recognized as a source of new kinds of jobs, a report for the White House’s Council of Economic Advisers found. Panelists said AI has the potential to free up employee’s workloads so they can devote time to creative or innovative projects and “upskill,” or get up to speed on new tech.

Evangelista also said agencies are building out new teams and positions devoted to exploring AI. Last April, the Department of Defense hired Craig Martell to lead the Chief Digital and Artificial Intelligence Office, which was created just two months earlier.

Meikle Paschal, a program manager for robotic process automation at the U.S. Department of Homeland Security, said the easiest way to standardize a process is to automate it, and solving problems that way is an alternative to hiring more people to transcribe or paper push.

“This is like a generational gift that we’re going to give to the people who come after us in the workforce,” he said. “And if we hold back at this point because we’re scared to make a mistake, then the next three or four generations are going to be behind the ball.”

About Molly Weisner

Molly Weisner is a staff reporter for Federal Times where she covers labor, policy and contracting pertaining to the government workforce. She made previous stops at USA Today and McClatchy as a digital producer, and worked at The New York Times as a copy editor. Molly majored in journalism at the University of North Carolina at Chapel Hill.

Article link: https://www.c4isrnet.com/govcon/contracting/2023/03/02/the-opposite-of-silicon-valley-how-feds-expect-to-use-aiml/