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         article-type="Research Paper"
         xml:lang="en">
  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>Journal of Artificial Intelligence and Big Data Disciplines</journal-title>
        <abbrev-journal-title abbrev-type="publisher">jaibdd</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="epub">3049-2122</issn>
      <publisher>
        <publisher-name>Dr. Aaluri Seenu</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.70179/fnpdtw95</article-id>
      <article-id pub-id-type="publisher-id">jaibdd120007</article-id>
      <title-group>
        <article-title>AI and Big Data Integration Strategies for Secure and Efficient ERP System Deployments</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>C</surname>
            <given-names>Venkata Siva Rama Prasad</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
        </contrib>
      </contrib-group>
      <aff id="aff1">Department of Civil Engineering, Malla Reddy Engineering College, Secunderabad</aff>
      <pub-date pub-type="epub" iso-8601-date="2026-02-24">
        <month>02</month>
        <day>24</day>
        <year>2026</year>
      </pub-date>
      <volume>2</volume>
      <issue>1</issue>
      <abstract>
        <p>Enterprise resource planning (ERP) systems are often viewed as a necessary burden that usually delivers less value than originally 
anticipated. Enterprise resource planning systems are supposed to provide organizations with the information they need for central control, 
regulatory compliance, and business processes that are supported by information flowing through the system. Big Data and AI models present an 
opportunity to redefine how ERP is implemented and maintained. It has the potential to move the ERP space from being mundane to being a 
significant driver of future business performance. AI, by automating human performance, can also automate system ownership and improve 
customer satisfaction. In this way, it is a force multiplier that changes the economics of the ERP ecosystem. 
The data being fed into the model and the results of that model present new challenges for ensuring that all of this can be done at the proper level 
of security. This is a central challenge for ensuring the long-term security of both the ERP implementation and the enterprise itself. This paper 
will examine the protocols that must be observed to retain security and maximize this potential transformation associated with the AI-driven future 
of ERP solutions. Both the opportunities and the potential pitfalls will be discussed in the hopes that this will enable a secure and efficient path to 
that future, minimizing the number of disruptions that a company will have to encounter on that path.</p>
      </abstract>
      <kwd-group kwd-group-type="author">
        <kwd>enterprise resource planning</kwd>
        <kwd>ERP systems</kwd>
        <kwd>AI-driven ERP</kwd>
        <kwd>big data analytics</kwd>
        <kwd>enterprise automation</kwd>
        <kwd>regulatory compliance</kwd>
        <kwd>business process optimization</kwd>
        <kwd>AI in business performance</kwd>
        <kwd>system ownership automation</kwd>
        <kwd>customer satisfaction</kwd>
        <kwd>ERP ecosystem transformation</kwd>
        <kwd>ERP security</kwd>
        <kwd>data security</kwd>
        <kwd>AI model integration</kwd>
        <kwd>digital enterprise</kwd>
        <kwd>enterprise system optimization</kwd>
        <kwd>ERP implementation challenges</kwd>
        <kwd>secure ERP protocols</kwd>
        <kwd>AI-enabled business transformation</kwd>
        <kwd>enterprise performance management</kwd>
      </kwd-group>
    </article-meta>
  </front>
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