Welcome to the Today of Tomorrow

NOTE: These instructions are about submitting your articles to our blog only. We are also launching an academic journal and these instructions DO NOT apply to that. For instructions about submitting articles to our academic journal please visit our journal.

Applied artificial intelligence is maturing fast. With maturity comes adoption, commercialization, and diffusion. What also comes is the responsibility to protect and safeguard the interests of businesses and households. As artificial intelligence products and services become mainstream and the technology becomes consumer facing, we have two responsibilities: to enhance and to protect. Hence, the mission of the AI Post (and The American Institute of Artificial Intelligence) is: To advance artificial intelligence safely and responsibly

  • Advance means to increase the breadth and speed of technology generation and adoption.
  • Safely means to consider the social, economic, spiritual, psychological, ecological, and political elements of the impact of technology on human life.
  • Responsibly means to operate with a state of preemptive awareness and not post-catastrophe reactionary mode.

We achieve our mission by maximizing and accelerating the adoption of the artificial intelligence technology and minimizing the associated social, economic, political and other costs.

The time has come when the artificial intelligence technology should be presented to the world in the most meaningful and responsible manner. Not from the perspective of blatant marketing, not just for the consumption of scientists, not to create hype or fear or panic – but to educate the public and governments in a responsible, objective and sophisticated manner, to help develop the science, to remove the obstacles, to fill in the gaps, and to ensure that artificial intelligence is advanced in a safe and responsible manner where the interests of human and biological lifeforms are protected.

The White House recently organized a conference and distributed an RFI to develop a better understanding of the benefits and perils of the artificial intelligence technology. While the conference was a remarkable success, it clearly implies that the AI technology has matured to a point that the government is getting concerned about its applications and consequences.

We are cognizant of that concern.

Notice that we have deliberately placed an inherent contradiction in our mission. For example, while maximizing the adoption of the technology will drive us to help increase consumption of the AI products and services –minimizing the social, economic, and political costs may lead us to recommend regulation, control, and curtailment strategy. So one one hand, we focus on enhancing demand and removing obstacles to supply (hence increase adoption), on the other hand we assess, analyze, help manage, and examine the risks and impacts.

We operate with this built-in dichotomy. We un-hype the hype and create a balance between fascination and fear. We apply multidimensional and multidisciplinary approaches to our research.  Our experts, therefore, come from different fields and backgrounds. Our objectivity and independence are our most important assets. We do not market, promote, or sell on behalf of any company – and we never will. We do receive advertising revenues from companies wishing to advertise on our site or in our conferences, but that does not, in any manner, influence our judgment or thinking.

To learn more about the AI Post please select the following:

The AI Post Editorial Guidelines
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Our Editorial Framework
How to Become a Contributor?

Winning The Data Governance War

The Information Management field is novel and practitioners are struggling to architect data governance programs that drive operational integrity, give organizational credence, and enable program sustainability.

Ask anyone deeply entrenched in implementing a Data Management program and they will tell you that Data Governance is a warzone. You are constantly under fire from your organizational partners as you tackle the change management issues.You are bombarded by the bouts of fast developing regulatory frameworks. You are pounded by ever-changing systems and technology artifacts. You line up your governance standards but your best soldiers, the data trustees, are busy fighting other wars. When the pressure becomes unbearable, you play defensive, only to find out that your budgets are being questioned by the CFO office. So what is going wrong with the way we are approaching governance, and how can we fix those problems? Very simply, how can we win the data governance war? This article will answer both of these questions.

Since we used the word war to set up this article, it is only fair to begin with two highly relevant quotes from Sun Tzu. The first is “If you know the enemy and know yourself, you need not fear the result of a hundred battles.” The second quote is “Victorious warriors win first and then go to war, while defeated warriors go to war first and then seek to win.” The first quote applies to making an accurate assessment of your surrounding realities and the second focuses on the power of planning right. Let us address these two issues as they apply to data governance.

Know thy Data Governance Challenges

We have analyzed the data governance programs across several companies and have developed a list of twelve critical challenges facing the program offices.

  1. Manual: Face it, governance is a highly manual process. Writing standards and procedures, conducting customized analysis, and developing organizational structures is a highly manual process. The labor intensive nature of the process not only leads to excruciatingly painful delays but also traps the programs in the trenches dug in quicksand. The harder you try to get out, the more trapped you get. Soon your program loses momentum and you stay stuck and paralyzed right at the first step.
  2. Resource Intensive: If you want to do it right, you would need an army of data scientists, analysts, and managers to help drive and sustain program momentum. This means significant upfront investment. Typically it is a tough sell to justify building an organization which cannot easily demonstrate its bottom line value. Lining up that type of an army often results in other functional organizations questioning the value contribution of data governance.
  3. Value Definition as an After Thought: Defining value typically becomes an afterthought. The value concept dawns only when the news of sluggish movement of data governance programs reaches the operating and financial executives – both trained to seek value in every activity undertaken by business enterprises. When value is questioned, that is when data management executives rush to find ways to demonstrate the value of their programs.
  4. Disjointed: Programs often focus on what data “is” vs. focusing on what data “does”. What data “does” is a totally different perspective and a new paradigm. It alters the entire thinking about governance. But as of now, we are trapped in the old paradigm of fixing data errors and defining data quality in the narrowest possible terms. When we approach governance that way we isolate ourselves from the rest of the organization.We seem far more concerned about fixing errors vs. ascertaining operational integrity of our companies.
  5. Ad Hoc Activities: Governance programs are often implemented as a mishmash of tasks defined by organizational realities and political considerations rather than business value and risk based prioritization. Various departmental programs are launched at a business unit or function levels. These programs are often uncoordinated, have no enterprise vision, and sometimes even result in competing priorities or goal conflicts. Misalignment leads to unnecessary confusion and lack of performance.
  6. Formal Structure: Structurally, the methodology of implementing governance is not defined in a modular structure. Hence, no best practices are available to formalize the implementation and give structure to implement programs in a stepwise and staged manner.
  7. Scalable: When confronted with new regulatory challenges or sudden business centric changes (for example mergers, acquisitions, restructurings, etc.) the governance program executives discover that the programs are inherently un-scalable. When this dawns on them the only strategy left in the toolbox is to throw more heads to do the job. This is not a sound approach.
  8. Repeatable: Do we really need to reinvent the wheel? When data governance programs are not designed with a mindset that keeps future battles in consideration, each battle becomes a learning ground of its own. You have the choice to design the programs in a manner where all the work you do becomes repeatable for future tasks and challenges. Embedding repeatability in your programs is by design, not luck.
  9. Slow and Sluggish:  Still trying to get on the calendars of your trustees? After weeks of agonizing effort to get your trustees in a room, the bombshell is thrown at you. You find out that some people didn’t like the procedure or the standard you spent months to develop. And now you have to go back to the design table. This slothful pace, which is often beyond your control, really destroys the program momentum.
  10. Expensive:  With all of the above going on, it is no wonder that high-impact programs tend to be expensive. Programs that receive funding end up paying too much while those that don’t receive major funding end up taking smaller offices and disappear in the corporate ghettos. Both create waste and missed opportunities.
  11. Dependent on SME: The governance programs tend to be SME dependent and therefore you are working around the vacation schedules and day-job work calendars of your trustees. After a while, they are as annoyed at you as you get on yourself for leading the data management programs. Your emails are ignored and your calls get lost in the depths of the voice mail. You are stuck as you can’t move forward without your SME, while your SME would give anything to get that monkey off his or her back.
  12. Significant Change Management: When they don’t see the value, they are not convinced that your program is either necessary or important for them. Corporate teams are trained to think about the immediate future and rapid results. When you talk about a slow and tedious journey to build a bridge to nowhere, they get both impatient and confused. The incredible tools in your toolkit, for example metadata or master data management, mean nothing for them unless you can show them a path to tangible value. Due to the above realities, like entropy, the chaos around the programs builds up and then the entire thing explodes. New management teams, under the guidance of visionary leaders and concerned board members, are brought in and the entire battle starts again. Regardless of how motivated or experienced the new teams are, unfortunately, the odds of securing a victory remain disappointingly low. And the saga continues.

Now that you have an accurate assessment of the realities surrounding your governance programs, let us focus on the second instruction of Sun Tzu: How can we win the war before the war begins? Winning Before the War Begins We are confident that the above-mentioned challenges can be overcome and the governance battle can be won by implementing the governance program via automation. Our platform, AiPost Governance Management, can assure you of a painless and rapid victory. Let us look at some of the benefits that can be achieved via automated governance platform:

Automate: Yes, you don’t need to implement your program as if you are participating in Attila the Hun battle. You are living in the second decade of the 21st century. Welcome the transformational technology that is enabling efficient and rapid deployment of governance programs. AiPost’s Governance engine automates the entire process of governance and offers tools that can give you a powerful implementation. Having access to the platform means having a modularized and structured approach to implement programs. No more guessing or shooting arrows in the dark. Your program progresses through a highly disciplined process and milestones are achieved with precision-guided accuracy.

Accelerate: Technology enables you to have access to actionable information, built-in knowledge, and best practices. You don’t need to reinvent the wheel and everything you do becomes repeatable and scalable. No matter what happens with the regulatory environment or the business, your program is robust to withstand any challenges. Most importantly, you can move at an electrifying speed.

Energize: Technology platform is also a powerful tool to recruit, motivate, and energize the teams working on your projects. As you speed up the process, it energizes everyone touched by your programs. You don’t need to sit there and wait for people to approve your requests or for SME’s to cut short their vacations to help you out. The automation and knowledge platforms are industry and regulatory framework compliant and hence minimize the need for domain expertise. Thousands of examples of pre-existing rules, standards, and procedures mean all the groundwork has already been done for you.

Optimize: Having a powerful collaborative platform now gives you the opportunity to increase the efficiency of the program – the improvement potential that can never be achieved through a manual process. With efficient allocation of resources, repeatable and scalable processes, and built in knowledge frameworks, you can drive unparalleled value for your companies.

Monetize: Linking your programs with real value creation is one of the most important missing elements of data management programs. Governance only goes halfway. To go through the full value cycle, governance programs have to focus on what data “does” and not just on what data “is”.

The automation platform can give you the power to create and demonstrate actual, bottomline impacting, power of data. In summary, the data governance war can be won before the war begins by implementing the program through an automated governance platform. Automation increases the likelihood of success and minimizes risks. With all the data coming your way, data governance is a battle you can’t afford to lose.

Artificial Intelligence & Government Accountability

Can artificial intelligence improve government accountability?

Last year Bass provided an excellent overview of the role big data can play in increasing government accountability. His analysis covered the current state of affairs and the problems with achieving the goal of a transparent government (Bass, 2015). In an amazing and eye-opening manner, he showed that while data can increase government accountability, numerous unresolved issues remain that must be overcome before we can have higher transparency. One of the major issues identified by Bass is the fact that many stakeholder (including journalists, agencies, members of congress, and others) don’t want greater transparency. Once that barrier is overcome, the data itself may have several problems (quality, quantity, value, etc.) – he points out. The existing laws and their interpretation can also hinder the progress (e.g. FOIA). He provides several recommendations on how government accountability can be improved.

Bertot, Jaeger and Grimes discussed applications of various technologies (in particular social media) in increasing government transparency (Bertot et al., 2012). While Bass provides a description of what accountability can be and the challenges to achieving that vision, Bertot et al. focus on what technology has been able to accomplish. It is important to point out that while Bass’s focus was on big data, Bertot’s is not on the data itself but on the use of information technology in general (e.g. procurement systems, social media etc.).

The point is that while we have two perspectives, one that argues that information technology has already created (and therefore has the capability to) create greater transparency and accountability. And the second perspective that while a lot had been done, a lot remains to be done and that will require tremendous effort and change. If I was developing a list to develop a change management program to achieve greater transparency, the list would include congress, lobbyists, executive office, agencies, journalists, courts, and the list goes on. This is a grim picture. In addition, it would require significant expertise in data science and technology itself. Frankly, while I realize the monumental challenge to achieve that level of accountability and transparency, I firmly believe that it should be pursued.

In fact, in addition to the technologies pointed out Bertot(Bertot et al., 2012) and Bass (Bass, 2015), we now have artificial intelligence. Artificial Intelligence can be used for:

  • Pointing out accountability issues
  • Bias free assessment of rule of law and its enforcement
  • Evaluating consistency of strategy
  • Recommend policy making
  • Analyzing performance measures
  • Identifying corruption and waste
  • Increasing efficiency
  • Preserving values that we respect (e.g. freedom, liberty justice, equality)
  • Ensuring elections are fair
  • Performing strategic behavioral analysis of leaders
  • Bridging the gap between haves and have-nots
  • And numerous other applications in both tactical and strategic areas

The key point is that artificial intelligence is going beyond the big data analytics and other information technologies. It provides a higher state of consciousness than possible with systems that lack the capacity to learn. Artificial Intelligence learns and it grows, and it understands and develops. The vision of artificial intelligence based government has already been laid out by Bartlett in an extremely interesting article (Bartlett, 2016).

But here are the problems:

  • Would governments undertake the necessary initiatives that will make them more transparent and accountable?
  • The companies that will come up with such technologies (and some are already there) will become so immensely powerful that while they will be able to create higher accountability and transparency for government but in doing so they will become more powerful than the government. And if that happens, would they get favorable legislation and power in other areas of their business.

References:

Bartlett, S. J. (2016) The Case For Government By Artifical Intelligence. [online]. Available from:

Bass, G. D. (2015) Big Data and Government Accountability: An Agenda for the Future. ISJLP. [Online] 11 (1), 13–48.

Bertot, J. C. et al. (2012) Promoting transparency and accountability through ICTs, social media, and collaborative e-government. Transforming Government: People, Process and Policy. [Online] 6 (1), 78–91. [online]. Available from:

Submission Guidelines

NOTE: These instructions are about submitting your articles to our blog only. We are also launching an academic journal and these instructions DO NOT apply to that. For instructions about submitting articles to our academic journal please visit our journal.

Please submit all queries, articles, infographics, or other content here.

We do not impose any length guidelines. Typical business articles tend to be between 750-2500 words. Since policy articles may require significant explanation, they can be longer.

Our Editorial Framework

NOTE: These instructions are about submitting your articles to our blog only. We are also launching an academic journal and these instructions DO NOT apply to that. For instructions about submitting articles to our academic journal please visit our journal.

Our editorial framework closely follows our mission, the AI Post Diffusion Framework (patent pending), and the AI Post Risk Assessment (patent pending). At the highest level we have split our content between the adoption enhancement centric content(Business) and the risk and impact management centric content(Policy).

In line with our mission, we will publish articles on the broad topics of:

  • BUSINESS: This segment will focus on successful diffusion of the artificial intelligence technologies. The subcategories include:
    1. SCIENCE: Articles in this area focus on state of the science and technology; updates on research; what are the main hurdles and obstacles and how to remove them; includes topics such as machine learning, natural language processing, data science etc.
    2. INVESTMENT: Articles on the analysis of investment going into AI research and development, companies, universities, and research institutions. Problems and hurdles with investment. New venture investment. valuations, mergers and acquisitions, financial performance potential, and return analysis.
    3. BUSINESS: This segment focuses on AI commercial applications and uses, and their business aspects; AI Business models; AI business ideas; AI business special issues; Operating dynamics and issues, AI business opportunities.
    4. LEADERSHIP: Leadership issues in AI; focus area includes need for diverse and multidisciplinary leadership; explores need for organizational dynamics in the new business environment; includes organizational analysis.
    5. PRODUCT DEVELOPMENT: Topics covered include algorithm development; technology preference; how do AI technologies interact with other technologies; the importance of datasets; how to improve quality and governance of training and post training data; user group involvement; bias analysis.
    6. COMMERCIALIZATION: This area focuses upon:What are the problems of commercialization? How to involve users and focus groups in product and service development?
    7. RISK: Analysis of adoption risk specific to certain AI based business models, products or services, and commercialization. This segment is designed to help businesses navigate through strategic, operational, and financial risks.
    8. LEGAL: This segment focuses on property rights and other legal aspects of artificial intelligence and its growth.
    9. INSTITUTIONS: This segment focuses on guiding businesses to specific institutions and guiding institutions to the businesses. This section also focuses on introducing areas on which institutions can focus on.
  • POLICY:This area focuses on the risks and impact of artificial intelligence. The broad areas considered are Social, Economic, Spiritual, Legal, Political, Psychological, Physical, Ecological, and Ethical. We invite and encourage open debate on key issues. We encourage articles on relevant and controversial topics. Our focus areas include:
    1. SOCIAL: Includes topics such as impact on society and values, impact on norms and human relationships, law and justice (social aspects), discrimination, privacy, social psychology, and society in general.
    2. ECONOMIC: Covers topic such as unemployment, impact on transaction costs,impact on institutions,impact on markets, impact of fundamental economic theory, displacement costs, trade costs, interest rates, cost of capital, etc.
    3. POLITICAL: Impact on government, elections, democracy, political institutions, state, civil rights, power, dictatorships and other such issues.
    4. LEGAL: Impact on law and justice (process aspects), legislative and judicial branches, legal proceedings, and legal process.
    5. SPIRITUAL: Impact on religion, spirituality, faith, and the concept of God.
    6. PSYCHOLOGICAL:This segment analyzes the impact of artificial intelligence on human psychology, emotions, behavior, and emotional well-being.
    7. ETHICS:This area examines the topics related to moral philosophy and ethics and the impact of artificial intelligence.
    8. PHYSICAL:Covers studies on the impact of artificial intelligence on human body and its physical environments.
    9. ECOLOGICAL: The impact on climate change and other ecological impacts of artificial intelligence are explored in this segment.

To learn more, please select:

Why The AI Post?
The AI Post Editorial Guidelines
Welcome Message
Submission Guidelines
How to become a contributor?

Why The AI Post?

When it comes to artificial intelligence, business readers are at a disadvantage. Caught between the hype-ridden, company-backed, advertising-oriented, commercials-cloaked-in-whitepapers content on one hand and the conspiracy-ridden, fantasy-frosted, doomsday fortuneteller content on the other hand, business readers don’t get the objective and intelligent information on artificial intelligence. So interested readers find content on sites where they have to constantly fight never-ending barrage of advertisements (that appear from every possible direction in virtual space and time) or they visit outlandish sites whose content would shame UFO and bigfoot theorists.

Desperate, when they turn to the artificial intelligence research community, they find a tight network of extremely bright people who have neither the time nor the inclination to explain the complexities of the technology. In the fast moving AI world, where research is now thumping to enter the bold world of translational and applied AI, the publication styles and preferences of the researchers are extremely academic, rigorous, and scientific – and for good reasons. For most business readers, the research oriented scholarly articles in scientific journals would be either too complex or would require too much time to read.

So while the investment in artificial intelligence is surging, business applications are emerging in multiple sectors, and artificial intelligence is impacting our lives in a huge way, we have no reliable, business-centric, objective, and single-focused publication to inform and educate the general public and business readers or to examine and analyze the issues confronting the AI. In other words, where is the Harvard Business Review or MIT Sloan or Strategy and Business equivalent publication for business readers? Welcome to the AI Post, the first publication to exclusively focus on the business, policy, economic, social, political and other similar issues resulting from the rise of the artificial intelligence technology.

To read more, please select:

The AI Post Editorial Guidelines
Our Editorial Framework
Welcome Message
Submission Guidelines
How to become a contributor?

The AI Post Editorial Guidelines

NOTE: These instructions are about submitting your articles to our blog only. We are also launching an academic journal and these instructions DO NOT apply to that. For instructions about submitting articles to our academic journal please visit our journal.

Our Mission

The AI Post is the premier business and professional publication that chronicles and analyzes the advancement of artificial intelligence technology and its impact on economy, society, and state(s). With an eye on advancing the technology while helping government shape responsible policies, protecting economic interests of people and businesses, consumer welfare, and safety of biological lifeforms, the publication takes the dual position of: maximizing and accelerating the adoption of the artificial intelligence technology while minimizing the associated social, economic, and political costs.

Our mission is: To advance artificial intelligence safely and responsibly.

Advance means to increase the breadth and speed of technology generation and adoption.

Safely means to consider the social, economic, spiritual, and economic elements of the impact of technology on human life.

Responsibly means to operate with a state of preemptive awareness and not post-catastrophe reactionary mode.

The AI Post is written for concerned and serious scholars, business people, government officials, attorneys, executives, and others who are interested in having a serious debate about artificial intelligence and its diffusion. Far removed from the hype-ridden Silicon Valley culture or the overly abysmal technology-is-bad crowd, we want to provide a space where intellectual debate and examination of new technologies is possible in a more pragmatic and applied sense.

Primary Reader Targets

The primary audiences for the AI Post are business leaders, concerned citizens, regulators, academic scholars interested in understanding the broad footprint of the field, public policy influencers, decision makers at all levels of government, investment professionals, and human rights organizations. The publication caters to a diverse group of readers and therefore our authors come from different fields and backgrounds.

Reader Group Relevance

As mentioned in the previous section, the publication caters to wide spectrum of interests and hence serves several different audiences: business leaders, investment professionals, not-for-profit leaders, academic researchers, regulators, policymakers, trade association representatives, attorneys, and consumer advocates. Hence, we find it extremely important that articles should focus on one or more these audiences.

Style

We prefer clear and moderate complexity articles. Since we do focus on primary and secondary research, and prefer original thought leadership, in terms of style our typical articles will be somewhere between a typical newspaper article and a journal article. A good example will be to see the style of Harvard Business Review articles. We expect our authors to make clear and valid points and provide support for their arguments.

Authors and Review

Our authors come from a wide variety of fields and backgrounds and includes academics, practitioners, and policy makers. We welcome the diversity in thinking. Unlike other publications, you will always get a call or email back from us. We will review your submission within a week and will provide feedback. Criteria used by reviewers will be in accordance with our mission. The editor has final review authority. The editor may reach out to a committee of experts to obtain feedback on certain articles.

To read about the Content please click here.

To learn more, please select:

Why The AI Post?

Welcome Message

Submission Guidelines

How to become a contributor?