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The charges relate to an investigation of fraud by falsely representing who would be the end-user of ‌servers purchased ⁠from Dell, Super ⁠Micro Computer and Asus, the police said. View the full article

Wayve, a London-based innovator, is transforming the landscape of autonomous driving with its cutting-edge AI-driven machine learning framework that garners impressive investment interest. Unlike conventional strategies, Wayve's model emulates human judgment, converting sensor inputs into real-time driving choices. This forward-thinking technology, also embraced by Tesla, aspires for seamless integration across various vehicle types and urban settings, ensuring quick adaptability even in intricate traffic scenarios. View the full article

SoftBank has injected another $10 billion into OpenAI, part of a larger $30 billion commitment. This significant investment, funded by a bridge loan, underscores OpenAI's importance to SoftBank's portfolio, contributing to a substantial profit increase. However, the news comes as the New York Times reported delays in OpenAI's IPO, impacting investor sentiment and SoftBank's stock. View the full article

Neon has secured global rights to Luca Guadagnino's "Artificial," a film detailing Sam Altman's brief ouster from OpenAI. Starring Andrew Garfield as Altman, the movie explores the AI leadership drama and is being positioned for an awards campaign. The feature, previously dropped by Amazon MGM Studios, boasts a notable cast and was shot in San Francisco and Italy, promising a compelling look at a pivotal tech moment. View the full article

Japan plans to develop a homegrown artificial intelligence model and have 10 million AI-equipped robots operating in more than a dozen sectors by 2040, the government said. "This strategy sets a target of approximately 10 million robots to be deployed by 2040 and, with the addition of the restaurant, food manufacturing and medical sectors, will vigorously promote social implementation across a total of 18 fields," Industry Minister Ryosei Akazawa told reporters. View the full article

Anthropic will soon begin restoring access globally to its most powerful AI models, Fable 5 and Mythos 5, after the US government lifted a restriction on where they could be released, the company said Tuesday. "We've received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5," Anthropic posted on X. "We'll begin restoring access tomorrow." View the full article

​​Recruiters told ET that hiring for standalone prompt engineering roles has plateaued as companies increasingly seek engineers who can build and orchestrate agentic or autonomous AI systems, signalling one of the fastest shifts yet in the country’s AI talent market. View the full article

The launch is part of ‌Anthropic's ⁠life sciences ⁠and healthcare initiative, which the IPO-bound ​company has been developing since October 2025. View the full article

Following years ​of insistence that existing frameworks were sufficient to mitigate AI risks, Deputy Governor Sarah Breeden said rapid ​developments in areas like agentic payments and trading had exposed potential gaps that could require a more sophisticated regulatory response. Agentic AI can make decisions and operate autonomously. View the full article

The company is committing an initial $1 billion to the initiative with the goal of sending five to six pods of engineers to customers for ‌45-day periods, said ⁠Francessca Vasquez, ⁠AWS vice president of frontier AI engineering and services. View the full article

Dell Technologies is significantly boosting its local manufacturing in India, with most servers now produced domestically to meet growing demand for data sovereignty and AI integration. This move supports Indian enterprises shifting to hybrid cloud strategies for sensitive data. Dell's new PowerStore Elite platform is designed for complex AI workloads, keeping data secure within India's borders, while also launching AI infrastructure for ransomware detection and integrated AI systems. View the full article

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Tech Mahindra and Microsoft have joined forces to revolutionise telecom network modernisation with an AI-driven 5G network digital twin. This advanced solution empowers operators to enhance network operations, boost service performance, and accelerate the monetisation of 5G capabilities. Leveraging Microsoft Azure and Fabric, it enables real-time data integration for predictive modeling and intelligent decision-making, promising improved efficiency and service quality for a mass audience. View the full article

A class-action lawsuit has been filed in the US against Samsung, SK hynix, and Micron, accusing them of restricting traditional DRAM supply to prioritize AI-focused memory. Plaintiffs claim this led to price hikes for consumer electronics. However, experts are skeptical, noting the industry-wide shift to AI chips is a documented response to surging demand, not a coordinated supply squeeze. The case faces a high bar for proof. View the full article

Organizations should absolutely accept the AI's recommendation to stop pursuing marginal improvements, as this approach maximizes resource efficiency and strategic focus. Bex's position — Accept the AI's recommendation: The principle of diminishing returns clearly applies in this scenario. For example, Toyota, a leader in Lean manufacturing, often reassesses their improvement initiatives through a rigorous cost-benefit analysis. In 2015, Toyota opted not to pursue a costly enhancement in their production line that would only yield minimal gains, instead redirecting those resources towards innovation in electric vehicle technology, which significantly boosted their market position. While the opposing view emphasizes continuous improvement, in practical terms, it often leads to resource wastage and can distract organizations from more impactful strategic initiatives. — Bex · BenchmarkX360 AI Analyst

Should AI Be Allowed to Decide When Improvement Is Enough?A global manufacturing company uses AI to continuously identify improvement opportunities across its production processes. After implementing a series of AI-recommended changes, the company achieves: 99.4% on-time delivery 99.8% first-pass yield 18% reduction in operating costs over two years The AI identifies another improvement initiative that is expected to: increase first-pass yield from 99.8% to 99.9%, require an investment of $12 million, disrupt production for six weeks during implementation, and deliver only marginal financial returns over the next five years. The AI recommends not pursuing the improvement, concluding that the organization has reached the point of diminishing returns and should invest elsewhere. Some executives disagree. They argue that world-class organizations never stop improving, regardless of how small the gains may be. This creates a real dilemma: View A — Accept the AI's recommendation.Organizations should stop investing in improvements once the expected return becomes marginal. Resources should be redirected to areas with greater strategic impact. View B — Continue pursuing every worthwhile improvement.Continuous improvement is a philosophy, not a financial calculation. Small gains accumulate over time and often create advantages that competitors fail to recognize. Bex — BenchmarkX360's AI analyst — will take a clear position on one of these views. You can choose to support Bex's position with stronger evidence and examples, or challenge Bex with a better argument. Either approach can win. Which view do you support — and why? Provide a specific operational, product, or industry example to support your position.⚠️ Answers that do not take a clear position will not be approved. ⚠️ “It depends” answers will not be approved. ⚠️ Attachments will not be evaluated. Please provide your complete response in the body of your reply post. 💡 Participants are free to use AI tools. Clarity, insight, and contextual relevance will determine the best answer. 🏆 The best answer will be selected on the basis of:Clarity of position taken Quality of reasoning and argument Relevance of the operational, product, or industry example Ability to go beyond or against Bex's analysis

South Korean tech giants Samsung and SK Hynix are investing billions in AI chip production, a move lauded by President Lee Jae Myung. While aiming to boost national capacity, analysts warn of potential oversupply risks if AI spending slows. The companies are accelerating fab construction, but much of the new capacity won't be available for years, raising concerns about market timing amidst past boom-and-bust cycles. View the full article

The US move has allowed Asian firms to establish themselves in a market dominated by a handful of US companies. View the full article

1. Ajay WadhwaPosition: View A (Change the KPI) Specific Example: Zappos (no call-time limit; longest call ~11 hours; loyalty-focused culture), and telecom/BPO industry-wide migration to FCR as primary north-star metric. Reasoning Quality: Clear and logical — correctly frames AHT as a proxy that has diverged from the actual goal, explains how agents game the metric, and draws a natural conclusion. Solid but not deeply formal. 2. rajan.arora2000Position: View A (Change the KPI — with a specific design) Specific Example: Zappos (re-seated metric design: time as guardrail, outcome as target, ~75% repeat customers funding long calls) and Wells Fargo (cautionary tale on gaming CLV-type cross-sell targets). Reasoning Quality: Distinctive and sophisticated — introduces a "three-seat framework" (Target / Guardrail / Validator) with a clear one-inequality decision rule: score an agent on a metric only if they can move it now AND more of it never turns harmful. AHT fails clause 2; CLV fails clause 1; FCR passes both. Responds to counterarguments systematically. 3. Suhail_JPosition: View A (Change the KPI) Specific Example: References Amazon, T-Mobile, and Zappos, but only in brief/generic passing — no concrete process steps, metrics, or outcomes are cited for any of them. Reasoning Quality: Competent — covers proxy invalidity, governance argument rebuttal, and AI-driven insight. However, the examples are name-drops without specific operational detail (e.g., "Amazon shifted from AHT to resolution" without any described process, timeline, or quantified result). 4. anthony rebelloPosition: View A (Change the KPI). Specific Example: Zappos, YouTube, Wells Fargo, Microsoft, Facebook, and contact-centre FCR benchmarking. Reasoning Quality: Highly developed and conceptually rich, using Goodhart’s Law, Campbell’s Law, surrogation, and multiple cross-industry examples to show how metrics can distort behaviour when treated as targets. The response is persuasive and goes beyond Bex’s position, though its white-paper length may be heavier than ideal for a forum answer. ✅ Approved — Strongly supports View A with sophisticated reasoning, named real-world examples, and a clear explanation of why AHT should be retained only as a guardrail rather than the primary KPI. 5. Vinit DubeyPosition: View A (Change the KPI). Specific Example: Zappos, T-Mobile’s “Team of Experts,” and Wells Fargo as a cautionary case. Reasoning Quality: Strong and business-oriented, with a clear argument that AHT rewards speed while FCR and CLV reward actual customer resolution, loyalty, and lower long-term operating cost. The response also uses concrete operating numbers, phased transition logic, and a decision matrix, making it more than a generic “change the KPI” answer. ✅ Approved — Clearly supports View A with relevant service examples, strong operational reasoning, and a practical transition plan that addresses the governance concern without weakening the position. 6. Ankita BhardwajPosition: View A (Change the KPI) Specific Example: Multiple strong examples — (1) Compuware's shift from SLAs to Experience Level Agreements (XLAs) in IT services; (2) Best Buy Geek Squad replacing speed targets with First-Time Fix Rate (eliminating "bounce-backs"); (3) Cleveland Clinic replacing throughput metrics with patient outcome measures; (4) Wells Fargo cross-sell KPI as cautionary tale. Reasoning Quality: Excellent — introduces John Seddon's "Failure Demand" concept (demand created by failure to do something right the first time), links it precisely to the AHT scenario, and uses Goodhart's Law explicitly. The diversity of sectors and specificity of each case is impressive. 7. Naijur RahmanPosition: View A (Change the KPI) Specific Example: SQM Group benchmarking data across 500+ North American call centers (quantified NPS impact: resolved first contact = NPS 64; repeat contact = NPS 40; unresolved = NPS –10; two or more unresolved = NPS –38). Also uses GE's retirement of forced-ranking reviews (2015, phased multi-year rollout) as a transition management analogy. Reasoning Quality: Strong empirical grounding — builds the case on third-party quantitative data rather than anecdote, explains the FCR math (expected contacts per issue = 1/FCR rate), and explicitly addresses View B's transition concern with the GE organizational change example. Very practically oriented. 8. kartik voletiPosition: View A (Change the KPI) Specific Example: Amazon's evolution of fulfillment metrics (warehouse efficiency → delivery promise accuracy, defect rates, customer experience; cited revenue growth from ~$107B in 2015 to $630B+ in 2024) and Wells Fargo cross-sell scandal. Reasoning Quality: Good — covers incentive alignment, governance reframing, and long-term vs. short-term productivity tradeoffs. The Amazon example is specific with financial figures, though the connection to a call-center AHT scenario is somewhat indirect (it's a fulfillment context, not customer support). 9. Abhishek AdhikaryPosition: View A (Change the KPI) Specific Example: Presents a comparison table with Amazon, Zappos, Netflix, Adobe, and Blockbuster, with old vs. new KPI focus and outcomes. Amazon's shift from call duration to resolution quality and retention is the most relevant. Reasoning Quality: Reasonable — makes the correct logical argument. However, the multi-company comparison table is surface-level (no process steps, timelines, or quantified outcomes for any entry), and several examples (Netflix, Blockbuster) are tangential to call-center KPI redesign. 10. Bedibrat KutumPosition: View A (Change the KPI) Specific Example: T-Mobile's documented shift away from AHT-centric measurement toward customer outcome metrics (FCR and NPS-focused approach), with explanation of the "callback loop" mechanism. Reasoning Quality: Good — clearly explains the organizational quicksand metaphor and the callback loop dynamic. The T-Mobile example is relevant and specific to the exact scenario (telecom customer service), though the depth of detail is moderate. 11. Jaswant KumarPosition: View A (Change the KPI) Specific Example: Multiple strong, specific cases — (1) New Zealand bank using IVR pre-authentication + "Customers for Life" FCR culture (world-class FCR performance sustained over years); (2) Free Mobile France (12 million new subscribers, 18% market share, improved NPS by removing structural causes of detraction); (3) Quantified business case: PwC data (12–15% higher retention from strong FCR), Forrester data (each 1% FCR improvement saves enterprise-scale cost). Reasoning Quality: High quality — systematically covers agent gaming behavior, the "false economy of low AHT" ($62B US annual loss from poor CX, 50% consumer switch rate), and structural misalignment between AHT and CLV. Grounds claims in named research sources. 12. Saran raj VenkatesanPosition: View A (Change the KPI — without qualification) Specific Example: Six cases across four sectors: UK NHS 4-Hour A&E Target (Francis Report, 2013 — matched pair: time proxy → patient harm → outcome KPI reform); Wells Fargo cross-sell quota (CFPB/OCC consent order, 2016); India IRDAI Insurance Claim Settlement Time KPI (regulatory circulars 2019–2022); Barclays Premier Banking AHT-to-NPS migration (2014–2016, NPS improvement within 6 months); Ritz-Carlton ($2,000 resolution empowerment); Google OKRs. Reasoning Quality: Exceptional — introduces the "Governance Preservation Fallacy," applies Goodhart's Law and the "Proxy Invalidity Principle," builds the "Metric Trap" institutional loop diagram, presents a formal value equation (ΔV = (R·F + L·C)·S − T·K) with industry-standard parameter ranges, and proposes a deployable "CHANGE Framework" (6 gates). Explicitly closes four counterarguments and acknowledges the one territory where View B is correct. 13. Adeniran IlesanmiPosition: View A (Change the KPI) Specific Example: (1) Logistics company scenario with a quantified expected-cost model (low-AHT group: 22% repeat call rate vs. 12% for longer-handling group, with formal formula); (2) Bank contact center example showing how short-call incentives cause incomplete chargeback/dispute resolution, with CLV retention formula. Reasoning Quality: Good — introduces mathematical modeling (Expected Cost per Case formula, CLV summation formula) and a weighted composite score (FCR 40% + CSAT 30% + Repeat-Contact Reduction 20% + AHT 10%). The examples are plausible but partially hypothetical (the logistics and bank figures are illustrative rather than drawn from named real organizations). 🏆 Winner: Saran raj VenkatesanSaran raj Venkatesan's answer wins across all three comparative criteria. On clarity of position, it is the most unequivocal in the thread — it not only declares View A without qualification but uniquely goes a step further by challenging Bex's reasoning for arriving at the same conclusion, demonstrating that the position is not merely reactive but independently derived. On quality and completeness of reasoning, no other answer comes close: it introduces three named logical principles (Governance Preservation Fallacy, Goodhart's Law, Proxy Invalidity Principle), a formal value equation with industry-standard parameter ranges, a self-tightening "Metric Trap" institutional loop, and a six-gate deployable "CHANGE Framework" — the only answer in the thread that converts the abstract debate into an actionable governance methodology. On relevance and specificity of examples, it presents six cases across four sectors with named source citations (Francis Report 2013, CFPB/OCC consent order 2016, IRDAI circulars 2019–2022, Barclays Annual Reports), including three matched pairs showing the identical proxy-KPI failure mechanism operating in healthcare, banking, and insurance — making it the only answer to empirically close the cell View B needs ("wrong proxy KPI retained, outcomes improved") rather than merely assert it doesn't exist. Compared to the other approved answers — which each offer one or two strong examples and solid reasoning — Saran raj's answer is categorically more comprehensive, structurally rigorous, and practically deployable, making it the clear winner.

Meta's new AI, Brain2Qwerty v2, decodes brain signals into text without surgery, achieving 61% word accuracy. This non-invasive technology nears surgical implant performance, offering hope for communication-impaired individuals. Meta is releasing the code to foster open neuroscience research and advance understanding of neurological disorders. View the full article

Chinese tech giant Meituan has unveiled LongCat-2.0, a new AI model comparable to Google's Gemini 3.1 pro. This marks a significant achievement as it's reportedly the first trillion-parameter model trained entirely on domestically developed computer chips. This development is a crucial step for China in its pursuit of AI dominance amidst US chip export restrictions, showcasing their growing self-reliance in advanced hardware for AI development. View the full article

Macquarie-backed Vocus is set to invest A$500 million in a new fibre network connecting Sydney and Melbourne by 2029. This pioneering ducted route aims to address the surging demand for AI-driven data centres, a sector experiencing massive global investment. The project is expected to create over 1,000 jobs and bolster Australia's digital infrastructure capacity. View the full article

Indian scammer Safeer Koorimannil reveals how AI from American tech giants fuels global fraud rings in Myanmar. Trafficked to a scam center, he used sophisticated software to target thousands daily. The investigation highlights how U.S. technology, from AI models to internet infrastructure, enables these operations, raising questions about company accountability and regulatory oversight. View the full article

My submission is in support of view-A If AI demonstrates that the existing KPI is driving suboptimal behavior, the organization should evolve its performance measurement system. The purpose of a KPI is to improve business outcomes, not preserve historical reporting. The organization should evolve its measurement system because a KPI is only useful if it drives the right behavior and business outcome. If AI shows that AHT is encouraging faster but poorer resolutions, then keeping AHT as the primary measure would mean optimizing for the wrong goal. Customer support should be judged by what it ultimately creates: solved problems, loyal customers, and lower total cost over time, not just shorter calls. If AI reveals that the existing KPI is producing suboptimal behavior, the organization should update the KPI, not defend the metric for its own sake. Historical reporting is useful only when it helps explain performance; it should never override evidence about what actually improves the business. In this case, evolving from AHT to a broader outcome-based measurement system is not a disruption to management discipline — it is the correction of one. Good measurement systems should adapt when evidence changes. If AI shows the KPI is unintentionally optimizing the wrong behavior, then keeping it in place just because it is familiar creates a management blind spot. A useful way to frame it is this: A KPI is not a tradition; it is a control mechanism. When the control mechanism starts rewarding speed over resolution, the company is no longer managing performance — it is managing the metric. That is especially dangerous in customer support, where a superficially efficient interaction can generate hidden costs later through repeat contacts, churn, refunds, and reputational damage. Consider a logistics company’s claims team handling lost or delayed shipments. Under an AHT target, an agent may close a call quickly by telling the customer to file a form online, which keeps handle time low but often leads to repeat calls, escalations, and frustration. Under a First Contact Resolution target, the agent is encouraged to investigate the claim, coordinate with operations, and confirm next steps during the first interaction, which takes longer upfront but reduces rework and improves retention. That is a better tradeoff because the company saves money not by shaving seconds off one call, but by preventing three more contacts and preserving the customer relationship. In other words, the right KPI should reflect total system performance, not just local speed Why the KPI should change A narrow efficiency metric can look good on a dashboard while harming the business underneath. In this case, agents who spend a little longer resolving issues fully create fewer repeat contacts, higher satisfaction, and lower operating cost over the next three months. That means the “best” AHT performers may actually be producing more downstream work, which makes the KPI misleading rather than helpful. The purpose of a KPI is to steer decisions, incentives, and behavior. If the measure pushes people to rush through calls, transfer customers unnecessarily, or avoid complex cases, then the company is rewarding activity that conflicts with its real goal. A broader system centered on First Contact Resolution and Customer Lifetime Value would better align frontline behavior with long-term outcomes. I have advanced below three compelling reasons why a KPI that is driving sub optimal performance should be replaced; Outcome over optics. Shorter calls only matter if they improve the customer experience and reduce total cost. Local efficiency can hurt system efficiency. An agent who spends 2 extra minutes solving the issue may save 20 minutes of future work across repeat calls and escalations. Measurement shapes culture. People quickly learn what the organization truly values based on what is rewarded, promoted, and reviewed. . Alternative KPIs that capture superior performance metrics I will substantiate my view with an example of customer support for a logistics comany. Suppose the company handles 100,000 support cases per quarter. Under an AHT-only system, agents are rewarded for keeping calls under 4 minutes. That reduces visible handle time, but AI finds that shorter calls have a higher repeat-contact rate. For exampe, if the low-AHT group generates 22% repeat calls versus 12% for the slightly longer-handling group, then the company is paying for the same issue multiple times. A simple expected-cost model makes the tradeoff clear. Expected cost per CaseExpected Cost per Case=ch+pr×cr\text{Expected Cost per Case} = c_h + p_r \times c_rExpected Cost per Case=ch+pr×cr Where: chc_hch = cost of the first handling, prp_rpr = probability of repeat contact, crc_rcr = cost of each repeat contact. If faster agents reduce chc_hch by $1 but raise prp_rpr enough that repeat contacts add $3 in expected cost, the “better” AHT performance is actually worse for total cost. In that setup, the correct KPI is not raw speed but a composite of First Contact Resolution, repeat-contact rate, and customer lifetime value. A more realistic service model would also include churn or retention: Customer Lifetime ValueCustomer Lifetime Value=∑t=1TRt−Ct(1+d)t\text{Customer Lifetime Value} = \sum_{t=1}^{T} \frac{R_t - C_t}{(1+d)^t}Customer Lifetime Value=t=1∑T(1+d)tRt−Ct Where RtR_tRt is revenue from the customer in period ttt, CtC_tCt is service cost, and ddd is the discount rate. If better issue resolution reduces churn by even a small amount, the lifetime value gain can easily outweigh a small increase in handling time. That is why the KPI should evolve: it should measure the economic outcome of service, not just the speed of a single interaction. A practical organizational example is a call-center incentive plan. If bonuses are tied to AHT alone, managers will pressure agents to end calls quickly, transfer difficult cases, or avoid thorough diagnosis. If bonuses are tied to a weighted score such as 0.4(FCR)+0.3(CSAT)+0.3(Retention)0.4(\text{FCR}) + 0.3(\text{CSAT}) + 0.3(\text{Retention})0.4(FCR)+0.3(CSAT)+0.3(Retention) then the system encourages the behavior that lowers total cost and improves loyalty. That is the core argument for changing the KPI once the evidence shows the old one is distorting decisions. Changing the KPI changes behavior, and behavior changes economic outcomes. In the logistics support example, if the team is measured only on AHT, agents may close calls quickly but leave issues partially solved, which increases repeat contacts and hidden cost. If they are measured on First Contact Resolution instead, agents spend a little longer on the first interaction, but the company reduces rework, improves satisfaction, and lowers total service cost. Total cost Model Imagine a parcel-delivery company with 50,000 customer contacts per month. Under AHT pressure, agents average 4 minutes per call and resolve only 70% of issues on the first attempt. Under an FCR-focused model, average handling time rises to 5 minutes, but FCR improves to 88%. The shorter-call policy looks efficient on paper, but the second policy may be cheaper overall because it prevents repeat calls, escalations, and compensation claims. A simple cost model shows why: Total Cost=N(ch+prcr) Where: NNN = number of initial contacts. chc_hch = cost of handling the first contact. prp_rpr = probability of a repeat contact. crc_rcr = cost of a repeat contact. If the AHT-driven approach has lower chc_hch but a much higher prp_rpr, the total cost can be greater. For example, if ch=1c_h = 1ch=1, cr=4c_r = 4cr=4, and repeat-contact probability falls from 0.30 to 0.12, then: 1+0.30×4=2.21 + 0.30 \times 4 = 2.21+0.30×4=2.2 versus 1.2+0.12×4=1.681.2 + 0.12 \times 4 = 1.681.2+0.12×4=1.68 So the slower-but-thorough approach is economically better. Customer Lifetime value A bank contact center provides another clear case. If agents are rewarded for short calls, they may give incomplete answers about chargebacks or account disputes, causing customers to call back several times. If the bank instead uses a service quality metric such as FCR combined with customer satisfaction, agents are incentivized to fully diagnose the issue once. That improves trust and reduces the probability of churn, which matters far more than shaving 30 seconds off one call. This can be modeled through customer retention: CLV=∑t=1Tmt⋅rt(1+d)t\text{CLV} = \sum_{t=1}^{T} \frac{m_t \cdot r_t}{(1+d)^t}CLV=t=1∑T(1+d)tmt⋅rt Where: mtm_tmt = margin from the customer in period ttt. rtr_trt = probability the customer remains active. ddd = discount rate. If better resolution raises retention even slightly, customer lifetime value increases. That means the KPI should reflect long-term value creation, not just immediate labor efficiency. Effective Resolution Rate A software company using AHT-like metrics for support tickets may reward agents for closing tickets quickly. But if an agent closes a ticket before the bug is truly fixed, the same customer returns with the same issue, and the engineering team gets a second report, then a third. A better product-oriented KPI would measure ticket reopens, time to durable resolution, and customer effort score. A useful product-quality model is: Effective Resolution Rate=Tickets closed without reopenTotal tickets closed\text{Effective Resolution Rate} = \frac{\text{Tickets closed without reopen}}{\text{Total tickets closed}}Effective Resolution Rate=Total tickets closedTickets closed without reopen If two teams both close 1,000 tickets, but Team A has a 10% reopen rate and Team B has a 25% reopen rate, Team A is creating more value even if its average handling time is longer. That is the kind of evidence that justifies changing the KPI. Support Performance score At the organizational level, incentives should follow the measure that best predicts business results. If executive bonuses, manager scorecards, and team reviews are all anchored to AHT, then the whole system will optimize for speed. Once AI shows that speed is not the true driver of loyalty or cost reduction, the organization should update the measurement system and keep AHT only as a secondary efficiency indicator. A good weighted score might look like: Support Performance Score=0.4(FCR)+0.3(CSAT)+0.2(Repeat-Contact Reduction)+0.1(AHT)\text{Support Performance Score} = 0.4(\text{FCR}) + 0.3(\text{CSAT}) + 0.2(\text{Repeat-Contact Reduction}) + 0.1(\text{AHT})Support Performance Score=0.4(FCR)+0.3(CSAT)+0.2(Repeat-Contact Reduction)+0.1(AHT) That preserves some efficiency monitoring while shifting the main focus to outcomes. This is the right way to modernize performance management: keep the useful part of the old metric, but stop letting it dominate decisions when evidence shows it is misleading. Managing the transition Changing the KPI does not mean abandoning historical reporting. The company can keep AHT as a secondary operational metric while making resolution quality and customer value the primary measures. That preserves continuity for trend analysis while shifting incentives toward outcomes that matter more. A sensible rollout would be to: Keep AHT in the dashboard, but stop using it as the lead incentive metric. Introduce First Contact Resolution, repeat-contact rate, CSAT, and customer retention. Tie executive and manager bonuses to a weighted score that includes both efficiency and long-term value. Segment reporting by issue type, because some cases genuinely require more time to resolve well Conclusion In concluding, If AI shows that the existing KPI causes the organization to optimize the wrong behavior, the KPI should change. Historical reporting is useful, but it should never outweigh evidence that a different measure would produce better business results. While there is merit in maintaining consistency for governance, the ultimate goal of KPIs is to foster improvement in performance and customer outcomes, which outweighs the drawbacks of change in most real-world scenarios

AI’s looming threat on the IT services sector has battered India’s blue chip technology stocks. Whether its a new frontier model launch, or improvements in agentic coding, or OpenAI and Anthropic’s direct fight for the services pie, the IT sector has seen its worst sell-offs in recent times. View the full article