The report attributed the halt of the deal, which was in early discussions, to investors’ concerns about software companies that may be vulnerable to artificial intelligence disruption.
Qualtrics’ existing loan, a $1.5 billion tranche due in 2030, has slipped from about 100 cents on the dollar in February to 86 centers currently, according to the report.
The company aimed to raise a debt package that would include a $3.3 billion leveraged loan and another $2 billion that would be sold to investors for its $6.75 billion acquisition of data analytics firm Press Ganey Forsta, per the report.
Reached by PYMNTS, Qualtrics declined to comment on the report.
JPMorgan did not immediately reply to PYMNTS’ request for comment.
Qualtrics announced in October that it signed a definitive agreement to acquire Press Ganey Forsta to add that company’s data, benchmarking and healthcare expertise to its own experience management technology.
It was reported Jan. 31 that software companies were seeing their loan prices fall amid investor concerns that AI advances such as the coding capabilities of Anthropic’s Claude model will make many software offering redundant. At that time, software debt in collateralized loan obligations had notched the worst total returns to that point in 2026 versus all sectors.
PYMNTS reported in February that the enterprise software space is being threatened by the growth of AI. As of Feb. 4, more than $800 billion in market value had been wiped out of the enterprise technology sector after Wall Street analysts pointed to the disruptive potential of new enterprise AI tools from providers such as Anthropic designed to automate processes like contract reviews and legal briefings.
S&P Global Ratings said March 12 that AI is likely to impact software companies on a case-by-case basis rather than causing a sector-wide decline in credit ratings. The firm said that the software companies most at risk of credit rating downgrades are those whose products are most vulnerable to replacement by AI because they are less differentiated and rule-based. It added that those least likely to be disrupted by AI are the companies with sector expertise and proprietary data.
